From 0f84b9fdaff7dfe00109c4b4ae5c02f5bb318cbc Mon Sep 17 00:00:00 2001 From: crvernon Date: Mon, 8 Apr 2024 14:02:20 -0400 Subject: [PATCH 01/12] notebook mods --- ...emand_WaterRights_File_Modification.ipynb} | 397 ++++++++++-------- ...=> N2_Evaporation_File_Modification.ipynb} | 275 +++++++----- ...b => N3_Reservoir_File_Modification.ipynb} | 218 ++++++---- ... => N4_Streamflow_File_Modification.ipynb} | 235 ++++++++--- ...inal.ipynb => N5_Batch_Modification.ipynb} | 206 ++++++--- 5 files changed, 864 insertions(+), 467 deletions(-) rename notebooks/{Quickstarter_Notebook_1_Final.ipynb => N1_Demand_WaterRights_File_Modification.ipynb} (63%) rename notebooks/{Quickstarter_Notebook_2_Final.ipynb => N2_Evaporation_File_Modification.ipynb} (64%) rename notebooks/{Quickstarter_Notebook_3_Final.ipynb => N3_Reservoir_File_Modification.ipynb} (68%) rename notebooks/{Quickstarter_Notebook_4_Final.ipynb => N4_Streamflow_File_Modification.ipynb} (68%) rename notebooks/{Quickstarter_Notebook_5_Final.ipynb => N5_Batch_Modification.ipynb} (96%) diff --git a/notebooks/Quickstarter_Notebook_1_Final.ipynb b/notebooks/N1_Demand_WaterRights_File_Modification.ipynb similarity index 63% rename from notebooks/Quickstarter_Notebook_1_Final.ipynb rename to notebooks/N1_Demand_WaterRights_File_Modification.ipynb index aa9fd2d..9169a91 100644 --- a/notebooks/Quickstarter_Notebook_1_Final.ipynb +++ b/notebooks/N1_Demand_WaterRights_File_Modification.ipynb @@ -15,13 +15,13 @@ "source": [ "In this series of five notebooks, we demonstrate the functionality of `statemodify` using three of the five subbasins on the West Slope basin in the state of Colorado: Gunnison, San Juan/Dolores, and the Upper Colorado Basin. There are two classes of adjustments offered in `statemodify` that can be used to create alternative future states of the world for the region: \n", "\n", - "1. Application of multipliers or additives to the original dataset \n", + "1. Application of multipliers or additives to the original dataset which are sampled from specified bounds using a Latin hypercube sample \n", "\n", "2. Complete swap of input data with data generated from an external method\n", "\n", - "Option 1 is applicable to .ddm (monthly demand), .ddr (water rights), .eva (reservoir evaporation), .res (reservoir storage).\n", + "Option 1 is applicable to `.ddm` (monthly demand), `.ddr` (water rights), `.eva` (reservoir evaporation), `.res` (reservoir storage).\n", "\n", - "Option 2 is applicable to .xbm (monthly streamflow) and .iwr (irrigation demand). In `statemodify` we provide a Hidden Markov Model (HMM)-based approach to generate synthetic flows across the basins and tie in irrigation demand to be negatively correlated to increased streamflow. " + "Option 2 is applicable to `.xbm` (monthly streamflow) and `.iwr` (irrigation demand). In `statemodify` we provide a Hidden Markov Model (HMM)-based approach to generate synthetic flows across the basins and tie in irrigation demand to be negatively correlated to increased streamflow. " ] }, { @@ -32,6 +32,18 @@ "In this first notebook, we will demonstrate now to use the demand (`modify_ddm()`)and water rights (`modify_ddr()`) modification functions in the San Juan River Basin. Demands are projected to increase with the growth of cities and agriculture and water rights will likely change as discussions on changes to the Colorado Compact and re-allocation of water across the Colorado River Basin to promote sustainable development continue. " ] }, + { + "cell_type": "markdown", + "id": "59a51930", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Tip: When each StateMod file is mentioned, clicking on the name will link the user to the StateMod documentation with more information on that file. \n", + "\n", + "
" + ] + }, { "cell_type": "markdown", "id": "c4724769-1cd7-4582-8089-72eb9370e19b", @@ -54,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "e4b22249-5a13-47a5-81ab-88718b856cad", "metadata": { "tags": [] @@ -65,19 +77,31 @@ "import logging\n", "import os\n", "import pickle\n", - "import subprocess\n", "from string import Template\n", + "import subprocess\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd\n", - "\n", + "import pandas as pd \n", "import statemodify as stm" ] }, + { + "cell_type": "markdown", + "id": "6ab8229a", + "metadata": {}, + "source": [ + "
\n", + " \n", + "NOTE: Each simulation in this notebook is run for the length of the historical period (from 1909-2013). If you want to reduce the length of the simulation, navigate to the `.ctl` file and adjust the `iystr` and `iyend` variables. For this notebook, these files are located in: `data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod\n", + "Notebook/sj2015.ctl` and `data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015.ctl` \n", + "\n", + "
" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "77d95e75-6737-44f9-98b7-e8936fe854b3", "metadata": { "tags": [] @@ -98,7 +122,11 @@ "\n", "# data directory and root name for the target basin\n", "data_dir = os.path.join(\n", - " home_dir, \"data\", \"sj2015_StateMod_modified\", \"sj2015_StateMod_modified\", \"StateMod\"\n", + " home_dir,\n", + " \"data\",\n", + " \"sj2015_StateMod_modified\",\n", + " \"sj2015_StateMod_modified\",\n", + " \"StateMod\"\n", ")\n", "\n", "# directory to the target basin input files with root name for the basin\n", @@ -113,9 +141,17 @@ "parquet_dir_ddr = os.path.join(data_dir, \"parquet_ddr\")\n", "\n", "# path to ddm and ddr template file\n", - "ddm_template_file = os.path.join(home_dir, \"data\", \"sj2015B_template_ddm.rsp\")\n", + "ddm_template_file = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"sj2015B_template_ddm.rsp\"\n", + ")\n", "\n", - "ddr_template_file = os.path.join(home_dir, \"data\", \"sj2015B_template_ddr.rsp\")" + "ddr_template_file = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"sj2015B_template_ddr.rsp\"\n", + ")" ] }, { @@ -123,12 +159,13 @@ "execution_count": null, "id": "4568a7bb-7e35-4c54-9e95-f24f44d7384c", "metadata": { + "scrolled": true, "tags": [] }, "outputs": [], "source": [ "# run statemod\n", - "subprocess.call([statemod_exe, basin_path, \"-simulate\"])" + "subprocess.call([statemod_exe, basin_path, \"-simulate\"])\n" ] }, { @@ -136,7 +173,7 @@ "id": "8e2d4be2-4249-439b-943e-95dd3f061d25", "metadata": {}, "source": [ - "Once StateMod has run successfully, we can now extract user shortages from the .xdd output file using the `statemodify` output modification function `convert_xdd()`. We denote a list of user IDs ('2900501','2900519','2900555') who we want to extract shortages for and then these shortages are saved in a compressed Parquet file format that can then be read in as a Pandas dataframe in Python. We can also remove the larger output files once the requested shortages have been extracted and saved. " + "Once StateMod has run successfully, we can now extract user shortages from the [`.xdd`](https://opencdss.state.co.us/statemod/latest/doc-user/OutputDescription/521/) output file using the `statemodify` output modification function `convert_xdd()`. We denote a list of user IDs ('2900501','2900519','2900555') who we want to extract shortages for and then these shortages are saved in a compressed Parquet file format that can then be read in as a Pandas dataframe in Python. We can also remove the larger output files once the requested shortages have been extracted and saved. " ] }, { @@ -148,9 +185,9 @@ }, "outputs": [], "source": [ - "# Extract shortages using statemodify convert_xdd() function\n", + "#Extract shortages using statemodify convert_xdd() function \n", "\n", - "# create a directory to store the historical shortages\n", + "# create a directory to store the historical shortages \n", "output_dir = os.path.join(data_dir, \"historic_shortages\")\n", "\n", "# create a directory to store the new files in if it does not exist\n", @@ -166,10 +203,10 @@ " # path, glob, or a list of paths/globs to the .xdd files you want to convert\n", " xdd_files=os.path.join(data_dir, \"*.xdd\"),\n", " # if the output .parquet files should only contain a subset of structure ids, list them here; None for all\n", - " id_subset=[\"2900501\", \"2900519\", \"2900555\"],\n", + " id_subset=['2900501','2900519','2900555'],\n", " # how many .xdd files to convert in parallel; optimally you will want 2-4 CPUs per parallel process\n", " parallel_jobs=4,\n", - ")" + ")\n" ] }, { @@ -181,7 +218,7 @@ }, "outputs": [], "source": [ - "data = pd.read_parquet(os.path.join(output_dir, \"sj2015B.parquet\"), engine=\"pyarrow\")\n", + "data=pd.read_parquet(os.path.join(output_dir,'sj2015B.parquet'),engine='pyarrow')\n", "data" ] }, @@ -190,7 +227,7 @@ "id": "a76e9af6-307e-4240-b81a-5e0cf2747cfa", "metadata": {}, "source": [ - "Upon inspecting the Parquet file above, we see the contents of the .xdd file, including the shortages experienced by the structures that we specified for the length of the historical period." + "Upon inspecting the Parquet file above, we see the contents of the `.xdd` file, including the shortages experienced by the structures that we specified for the length of the historical period." ] }, { @@ -212,13 +249,14 @@ }, "outputs": [], "source": [ - "data[\"shortage_total\"] = data[\"shortage_total\"].astype(float)\n", - "data[\"year\"] = data[\"year\"].astype(int)\n", + "data['shortage_total']=data['shortage_total'].astype(float)\n", + "data['year']=data['year'].astype(int)\n", "\n", "fig, ax = plt.subplots()\n", "\n", - "for name, group in data.groupby(\"structure_id\"):\n", - " ax.scatter(group[\"year\"], group[\"shortage_total\"], label=name)\n", + "for name, group in data.groupby('structure_id'):\n", + " ax.scatter(\n", + " group['year'], group['shortage_total'], label=name)\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", @@ -250,7 +288,7 @@ "tags": [] }, "source": [ - "Now that we've run StateMod in baseline mode, the next step shows how we can run it in an exploratory analysis mode. To do this, we need to create some plausible futures and adjust the input files of StateMod to reflect these changes. In this step, we'll demonstrate Option 1 for statemodify adjustments using the `.ddm` file as an example, which involves multiplying the current demand time series for these users by a value in between 0.5 to 1.5. Here we specify the IDs of the users and the bounds from which we want to sample multipliers for the demand. We create 2 alternative states of the world (SOW) using a Latin hypercube sampling procedure and store them in the `input_files` directory. " + "Now that we've run StateMod in baseline mode, the next step shows how we can run it in an exploratory analysis mode. To do this, we need to create some plausible futures and adjust the input files of StateMod to reflect these changes. In this step, we'll demonstrate Option 1 for statemodify adjustments using the [`.ddm`](https://opencdss.state.co.us/statemod/latest/doc-user/InputDescription/417/) file as an example, which involves multiplying the current demand time series for these users by a value in between 0.5 to 1.5. Here we specify the IDs of the users and the bounds from which we want to sample multipliers for the demand. We create 2 alternative states of the world (SOW) using a Latin hypercube sampling (LHS) procedure and store them in the `input_files` directory. " ] }, { @@ -263,7 +301,10 @@ "outputs": [], "source": [ "# a dictionary to describe what users you want to modify and the bounds for the LHS\n", - "setup_dict = {\"ids\": [\"2900501\", \"2900519\", \"2900555\"], \"bounds\": [0.5, 1.5]}\n", + "setup_dict = {\n", + " \"ids\": [\"2900501\", \"2900519\",\"2900555\"],\n", + " \"bounds\": [0.5, 1.5]\n", + "}\n", "\n", "output_directory = output_dir = os.path.join(data_dir, \"input_files\")\n", "\n", @@ -288,24 +329,22 @@ "basin_name = \"San_Juan\"\n", "\n", "# generate a batch of files using generated LHS\n", - "stm.modify_ddm(\n", - " modify_dict=setup_dict,\n", - " query_field=query_field,\n", - " output_dir=output_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " sampling_method=\"LHS\",\n", - " n_samples=n_samples,\n", - " skip_rows=skip_rows,\n", - " n_jobs=n_jobs,\n", - " seed_value=seed_value,\n", - " template_file=None,\n", - " factor_method=\"multiply\",\n", - " data_specification_file=None,\n", - " min_bound_value=-0.5,\n", - " max_bound_value=1.5,\n", - " save_sample=True,\n", - ")" + "stm.modify_ddm(modify_dict=setup_dict,\n", + " query_field=query_field,\n", + " output_dir=output_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " sampling_method=\"LHS\",\n", + " n_samples=n_samples,\n", + " skip_rows=skip_rows,\n", + " n_jobs=n_jobs,\n", + " seed_value=seed_value,\n", + " template_file=None,\n", + " factor_method=\"multiply\",\n", + " data_specification_file=None,\n", + " min_bound_value=-0.5,\n", + " max_bound_value=1.5,\n", + " save_sample=True)" ] }, { @@ -326,8 +365,7 @@ "outputs": [], "source": [ "import numpy as np\n", - "\n", - "sample_array = np.load(output_directory + \"/ddm_2-samples_scenario-1.npy\")\n", + "sample_array=np.load(output_directory+'/ddm_2-samples_scenario-1.npy')\n", "sample_array" ] }, @@ -348,7 +386,7 @@ "tags": [] }, "source": [ - "Now that we have created the input files, the next step is to run StateMod with the new input files. The file that StateMod uses to configure a simulation is called a .rsp file. For this dataset, the configuration file is `sj2015B.rsp`. This file contains the paths of all of the supporting files that StateMod needs to run. We create a template .rsp file (`sj2015B_template_ddm.rsp`) and swap in the path to the two new alternative .ddm files that are created. Then we run StateMod for the two scenarios and store the shortages in Parquet file format. Each scenario will take approximately 4 minutes." + "Now that we have created the input files, the next step is to run StateMod with the new input files. The file that StateMod uses to configure a simulation is called a [`.rsp`](https://opencdss.state.co.us/statemod/latest/doc-user/InputDescription/41/) file. For this dataset, the configuration file is `sj2015B.rsp`. This file contains the paths of all of the supporting files that StateMod needs to run. We create a template .rsp file (`sj2015B_template_ddm.rsp`) and swap in the path to the two new alternative `.ddm` files that are created. Then we run StateMod for the two scenarios and store the shortages in Parquet file format. Each scenario will take approximately 4 minutes." ] }, { @@ -356,6 +394,7 @@ "execution_count": null, "id": "4e303b1d-ac7f-4123-b76d-e1c11d407478", "metadata": { + "scrolled": true, "tags": [] }, "outputs": [], @@ -366,62 +405,51 @@ "\n", "# read RSP template\n", "with open(ddm_template_file) as template_obj:\n", + " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", + " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - "\n", + " \n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"DDM\": f\"../../input_files/sj2015B_{scenario}.ddm\"}\n", - "\n", + " \n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - "\n", + " \n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir_ddm, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - "\n", + " \n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}.rsp\")\n", - "\n", + " \n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - "\n", + " \n", " # construct simulated basin path\n", - " simulated_basin_path = os.path.join(\n", - " simulated_scenario_dir, f\"sj2015B_{scenario}\"\n", - " )\n", + " simulated_basin_path = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}\")\n", "\n", " # run StateMod\n", " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - "\n", - " # Save output to parquet files\n", - " print(\"creating parquet for \" + scenario)\n", - "\n", - " output_directory = os.path.join(parquet_dir_ddm + \"/scenario/\" + scenario)\n", - "\n", + " \n", + " #Save output to parquet files \n", + " print('creating parquet for ' + scenario)\n", + " \n", + " output_directory = os.path.join(parquet_dir_ddm+\"/scenario/\"+ scenario)\n", + " \n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - "\n", - " stm.xdd.convert_xdd(\n", - " output_path=output_directory,\n", - " allow_overwrite=False,\n", - " xdd_files=scenarios_dir_ddm\n", - " + \"/\"\n", - " + scenario\n", - " + \"/sj2015B_\"\n", - " + scenario\n", - " + \".xdd\",\n", - " id_subset=[\"2900501\", \"2900519\", \"2900555\"],\n", - " parallel_jobs=4,\n", - " )" + " \n", + " stm.xdd.convert_xdd(output_path=output_directory,allow_overwrite=False,xdd_files=scenarios_dir_ddm + \"/\"+ scenario + \"/sj2015B_\"+scenario+\".xdd\",id_subset=['2900501','2900519','2900555'],parallel_jobs=4)" ] }, { @@ -454,7 +482,7 @@ "outputs": [], "source": [ "output_directory = os.path.join(data_dir, \"input_files\")\n", - "sample_array = np.load(output_directory + \"/ddm_2-samples_scenario-1.npy\")\n", + "sample_array = np.load(output_directory+'/ddm_2-samples_scenario-1.npy')\n", "sample_array" ] }, @@ -475,41 +503,27 @@ }, "outputs": [], "source": [ - "# Read in raw parquet files\n", - "baseline = pd.read_parquet(\n", - " data_dir + \"/historic_shortages/sj2015B.parquet\", engine=\"pyarrow\"\n", - ")\n", - "SOW_1 = pd.read_parquet(\n", - " parquet_dir_ddm + \"/scenario/S0_1/sj2015B_S0_1.parquet\", engine=\"pyarrow\"\n", - ")\n", - "SOW_2 = pd.read_parquet(\n", - " parquet_dir_ddm + \"/scenario/S1_1/sj2015B_S1_1.parquet\", engine=\"pyarrow\"\n", - ")\n", + "#Read in raw parquet files\n", + "baseline=pd.read_parquet(data_dir+'/historic_shortages/sj2015B.parquet',engine='pyarrow')\n", + "SOW_1=pd.read_parquet(parquet_dir_ddm+'/scenario/S0_1/sj2015B_S0_1.parquet',engine='pyarrow')\n", + "SOW_2=pd.read_parquet(parquet_dir_ddm+'/scenario/S1_1/sj2015B_S1_1.parquet',engine='pyarrow')\n", "\n", - "# Subtract shortages with respect to the baseline\n", - "subset_df = pd.concat(\n", - " [\n", - " baseline[\"year\"],\n", - " baseline[\"shortage_total\"],\n", - " SOW_1[\"shortage_total\"],\n", - " SOW_2[\"shortage_total\"],\n", - " ],\n", - " axis=1,\n", - ")\n", - "subset_df = subset_df.set_axis([\"Year\", \"Baseline\", \"SOW_1\", \"SOW_2\"], axis=1)\n", - "subset_df[\"Baseline\"] = subset_df[\"Baseline\"].astype(float)\n", - "subset_df[\"SOW_1\"] = subset_df[\"SOW_1\"].astype(float)\n", - "subset_df[\"SOW_2\"] = subset_df[\"SOW_2\"].astype(float)\n", - "subset_df[\"Year\"] = subset_df[\"Year\"].astype(int)\n", - "subset_df[\"SOW_1_diff\"] = subset_df[\"SOW_1\"] - subset_df[\"Baseline\"]\n", - "subset_df[\"SOW_2_diff\"] = subset_df[\"SOW_2\"] - subset_df[\"Baseline\"]\n", + "#Subtract shortages with respect to the baseline\n", + "subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total'],SOW_2['shortage_total']],axis=1)\n", + "subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1','SOW_2'], axis=1)\n", + "subset_df['Baseline']=subset_df['Baseline'].astype(float)\n", + "subset_df['SOW_1']=subset_df['SOW_1'].astype(float)\n", + "subset_df['SOW_2']=subset_df['SOW_2'].astype(float)\n", + "subset_df['Year']=subset_df['Year'].astype(int)\n", + "subset_df['SOW_1_diff']=subset_df['SOW_1']-subset_df['Baseline']\n", + "subset_df['SOW_2_diff']=subset_df['SOW_2']-subset_df['Baseline']\n", "\n", "\n", - "# Plot shortages\n", + "#Plot shortages\n", "fig, ax = plt.subplots()\n", "\n", - "ax.scatter(subset_df[\"Year\"], subset_df[\"SOW_1_diff\"], label=\"Decreased Demand\")\n", - "ax.scatter(subset_df[\"Year\"], subset_df[\"SOW_2_diff\"], label=\"Increased Demand\")\n", + "ax.scatter(subset_df['Year'], subset_df['SOW_1_diff'],label='Decreased Demand')\n", + "ax.scatter(subset_df['Year'], subset_df['SOW_2_diff'],label='Increased Demand')\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", @@ -538,7 +552,7 @@ "id": "561a26d5-280d-4fb2-b121-e204f27a7d75", "metadata": {}, "source": [ - "Following from Step 2, we can run the same analysis for the function that manipulates the `sj2015.ddr` file, which corresponds to users water rights. In this function, we can specify the IDs of the users and can can utilize a variety of options for how we want to change the .ddr file. We can either sample from some bounds that apply multipliers to the decree, hard code in values for the decree, or adjust the rank of the user. In this simple example, we take a very junior user: 2900501 and make them have the highest water right by changing their rank to 1. " + "Following from Step 2, we can run the same analysis for the function that manipulates the `sj2015.ddr` file, which corresponds to users water rights. In this function, we can specify the IDs of the users and can can utilize a variety of options for how we want to change the [`.ddr`](https://opencdss.state.co.us/statemod/latest/doc-user/InputDescription/46/) file. We can either sample from some bounds that apply multipliers to the decree, hard code in values for the decree, or adjust the rank of the user. In this simple example, we take a very junior user, ID: 2900501, and make them have the highest water right by changing their rank to 1. " ] }, { @@ -554,9 +568,11 @@ "setup_dict = {\n", " # ids can either be 'struct' or 'id' values\n", " \"ids\": [\"2900501\"],\n", + "\n", " # turn id on or off completely or for a given period\n", " # if 0 = off, 1 = on, YYYY = on for years >= YYYY, -YYYY = off for years > YYYY; see file header\n", " \"on_off\": [1],\n", + "\n", " # apply rank of administrative order where 0 is lowest (senior) and n is highest (junior); None is no change\n", " \"admin\": [1],\n", "}\n", @@ -583,24 +599,22 @@ "basin_name = \"San_Juan\"\n", "\n", "# generate a batch of files using generated LHS\n", - "stm.modify_ddr(\n", - " modify_dict=setup_dict,\n", - " query_field=query_field,\n", - " output_dir=output_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " sampling_method=\"LHS\",\n", - " n_samples=n_samples,\n", - " skip_rows=skip_rows,\n", - " n_jobs=n_jobs,\n", - " seed_value=seed_value,\n", - " template_file=None,\n", - " factor_method=\"multiply\",\n", - " data_specification_file=None,\n", - " min_bound_value=-0.5,\n", - " max_bound_value=1.5,\n", - " save_sample=True,\n", - ")" + "stm.modify_ddr(modify_dict=setup_dict,\n", + " query_field=query_field,\n", + " output_dir=output_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " sampling_method=\"LHS\",\n", + " n_samples=n_samples,\n", + " skip_rows=skip_rows,\n", + " n_jobs=n_jobs,\n", + " seed_value=seed_value,\n", + " template_file=None,\n", + " factor_method=\"multiply\",\n", + " data_specification_file=None,\n", + " min_bound_value=-0.5,\n", + " max_bound_value=1.5,\n", + " save_sample=True)" ] }, { @@ -608,7 +622,7 @@ "id": "f3b10d2a-30e0-424e-b330-4f7cbb01b38b", "metadata": {}, "source": [ - "In the `input_files` directory, you can open the `sj2015B_S0_1.ddr` file and see that the Admin # of our selected user has now become 1.0000. Now we rerun our code to do the StateMod simulation, this time using the ddr template file. " + "In the `input_files` directory, you can open the `sj2015B_S0_1.ddr` file and see that the Admin # of our selected user has now become 1.0000. Now we rerun our code to do the StateMod simulation, this time using the .`ddr` template file. " ] }, { @@ -616,6 +630,7 @@ "execution_count": null, "id": "0cb6d099-8aa4-4d52-b5ee-94ab8a254d4a", "metadata": { + "scrolled": true, "tags": [] }, "outputs": [], @@ -626,62 +641,51 @@ "\n", "# read RSP template\n", "with open(ddr_template_file) as template_obj:\n", + " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", + " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - "\n", + " \n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"DDR\": f\"../../input_files/sj2015B_{scenario}.ddr\"}\n", - "\n", + " \n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - "\n", + " \n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir_ddr, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - "\n", + " \n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}.rsp\")\n", - "\n", + " \n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - "\n", + " \n", " # construct simulated basin path\n", - " simulated_basin_path = os.path.join(\n", - " simulated_scenario_dir, f\"sj2015B_{scenario}\"\n", - " )\n", + " simulated_basin_path = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}\")\n", "\n", " # run StateMod\n", " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - "\n", - " # Save output to parquet files\n", - " print(\"creating parquet for \" + scenario)\n", - "\n", - " output_directory = os.path.join(parquet_dir_ddr + \"/scenario/\" + scenario)\n", - "\n", + " \n", + " #Save output to parquet files \n", + " print('creating parquet for ' + scenario)\n", + " \n", + " output_directory = os.path.join(parquet_dir_ddr+\"/scenario/\"+ scenario)\n", + " \n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - "\n", - " stm.xdd.convert_xdd(\n", - " output_path=output_directory,\n", - " allow_overwrite=False,\n", - " xdd_files=scenarios_dir_ddr\n", - " + \"/\"\n", - " + scenario\n", - " + \"/sj2015B_\"\n", - " + scenario\n", - " + \".xdd\",\n", - " id_subset=[\"2900501\"],\n", - " parallel_jobs=2,\n", - " )" + " \n", + " stm.xdd.convert_xdd(output_path=output_directory,allow_overwrite=False,xdd_files=scenarios_dir_ddr + \"/\"+ scenario + \"/sj2015B_\"+scenario+\".xdd\",id_subset=['2900501'],parallel_jobs=2)" ] }, { @@ -689,7 +693,7 @@ "id": "26aeb572-a614-4778-b692-bd788e3f8423", "metadata": {}, "source": [ - "As before, let's go ahead and plot the shortages for our user:2900501 with respect to the baseline shortages." + "As before, let's go ahead and plot the shortages for our User 2900501 with respect to the baseline shortages." ] }, { @@ -701,28 +705,22 @@ }, "outputs": [], "source": [ - "# Read in raw parquet files\n", - "baseline = pd.read_parquet(\n", - " data_dir + \"/historic_shortages/sj2015B.parquet\", engine=\"pyarrow\"\n", - ")\n", - "SOW_1 = pd.read_parquet(\n", - " parquet_dir_ddr + \"/scenario/S0_1/sj2015B_S0_1.parquet\", engine=\"pyarrow\"\n", - ")\n", + "#Read in raw parquet files\n", + "baseline=pd.read_parquet(data_dir+'/historic_shortages/sj2015B.parquet',engine='pyarrow')\n", + "SOW_1=pd.read_parquet(parquet_dir_ddr+ '/scenario/S0_1/sj2015B_S0_1.parquet',engine='pyarrow')\n", "\n", - "# Subtract shortages with respect to the baseline\n", - "subset_df = pd.concat(\n", - " [baseline[\"year\"], baseline[\"shortage_total\"], SOW_1[\"shortage_total\"]], axis=1\n", - ")\n", - "subset_df = subset_df.set_axis([\"Year\", \"Baseline\", \"SOW_1\"], axis=1)\n", - "subset_df[\"Baseline\"] = subset_df[\"Baseline\"].astype(float)\n", - "subset_df[\"SOW_1\"] = subset_df[\"SOW_1\"].astype(float)\n", - "subset_df[\"Year\"] = subset_df[\"Year\"].astype(int)\n", - "subset_df[\"diff\"] = subset_df[\"SOW_1\"] - subset_df[\"Baseline\"]\n", + "#Subtract shortages with respect to the baseline\n", + "subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total']],axis=1)\n", + "subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1'], axis=1)\n", + "subset_df['Baseline']=subset_df['Baseline'].astype(float)\n", + "subset_df['SOW_1']=subset_df['SOW_1'].astype(float)\n", + "subset_df['Year']=subset_df['Year'].astype(int)\n", + "subset_df['diff']=subset_df['SOW_1']-subset_df['Baseline']\n", "\n", - "# Plot shortages\n", + "#Plot shortages\n", "fig, ax = plt.subplots()\n", "\n", - "ax.scatter(subset_df[\"Year\"], subset_df[\"diff\"])\n", + "ax.scatter(subset_df['Year'], subset_df['diff'])\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", @@ -745,10 +743,34 @@ "Now, continue on to Quickstarter Notebook #2 to learn how to use the reservoir evaporation modification fuction." ] }, + { + "cell_type": "markdown", + "id": "a90af3f9", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Tip: If you are interested in understanding how to apply `statemodify` functions to your own model, take a look at the source code found in the repository here: \n", + " \n", + " \n", + "
\n", + " 1. modify_ddm()\n", + "\n", + "
\n", + "\n", + "
\n", + " 2. modify_ddr()\n", + "\n", + "
\n", + " \n", + "\n", + "
\n" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "aa05248b-925b-4912-ba91-a2463d771a2d", + "id": "aa8a0c57", "metadata": {}, "outputs": [], "source": [] @@ -771,6 +793,35 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/notebooks/Quickstarter_Notebook_2_Final.ipynb b/notebooks/N2_Evaporation_File_Modification.ipynb similarity index 64% rename from notebooks/Quickstarter_Notebook_2_Final.ipynb rename to notebooks/N2_Evaporation_File_Modification.ipynb index 303f813..4b39ff8 100644 --- a/notebooks/Quickstarter_Notebook_2_Final.ipynb +++ b/notebooks/N2_Evaporation_File_Modification.ipynb @@ -47,20 +47,36 @@ "import logging\n", "import os\n", "import pickle\n", - "import subprocess\n", "from string import Template\n", + "import subprocess\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd\n", - "\n", + "import pandas as pd \n", "import statemodify as stm" ] }, + { + "cell_type": "markdown", + "id": "b3faed86", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-21T16:35:02.103151Z", + "start_time": "2024-03-21T16:35:02.089141Z" + } + }, + "source": [ + "
\n", + " \n", + "NOTE: Each simulation in this notebook is run for the length of the historical period (from 1909-2013). If you want to reduce the length of the simulation, navigate to the `.ctl` file and adjust the `iystr` and `iyend` variables. For this notebook, this file is located in: `data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015.ctl` \n", + "\n", + "
" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "3e259af6-2bd0-4820-b736-d71a23bffda6", + "id": "4f42b539", "metadata": { "tags": [] }, @@ -80,7 +96,11 @@ "\n", "# data directory and root name for the target basin\n", "data_dir = os.path.join(\n", - " home_dir, \"data\", \"gm2015_StateMod_modified\", \"gm2015_StateMod_modified\", \"StateMod\"\n", + " home_dir,\n", + " \"data\",\n", + " \"gm2015_StateMod_modified\",\n", + " \"gm2015_StateMod_modified\",\n", + " \"StateMod\"\n", ")\n", "\n", "# directory to the target basin input files with root name for the basin\n", @@ -90,7 +110,11 @@ "scenarios_dir = os.path.join(data_dir, \"scenarios\")\n", "\n", "# path to eva template file\n", - "eva_template_file = os.path.join(home_dir, \"data\", \"gm2015B_template_eva.rsp\")" + "eva_template_file = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"gm2015B_template_eva.rsp\"\n", + ")" ] }, { @@ -98,12 +122,13 @@ "execution_count": null, "id": "e4d8fefd-298f-4474-a17f-c66e4e7f3ebd", "metadata": { + "scrolled": true, "tags": [] }, "outputs": [], "source": [ "# run statemod\n", - "subprocess.call([statemod_exe, basin_path, \"-simulate\"])" + "subprocess.call([statemod_exe, basin_path, \"-simulate\"])\n" ] }, { @@ -111,7 +136,7 @@ "id": "6618531f-332f-4ba8-90e8-59b990e9b179", "metadata": {}, "source": [ - "In this notebook, rather than acquiring user shortages which are found in the .xdd output file, we can track reservoir storage which is found in the `gm2015B.xre` output file. Thus, in `statemodify`, we create a method that will allow us to extract output from the `gm2015B.xre` file and save it as a .csv file. Here we extract the shortages for Blue Mesa, one of the most important upstream reservoirs in the Gunnison that is responsible for supplying emergency water to Lake Powell." + "In this notebook, rather than acquiring user shortages which are found in the `.xdd` output file, we can track reservoir storage which is found in the [`.xre`](https://opencdss.state.co.us/statemod/latest/doc-user/OutputDescription/522/) output file. Thus, in `statemodify`, we create a method that will allow us to extract output from the `gm2015B.xre` file and save it as a `.csv` file. Here we extract the shortages for Blue Mesa, one of the most important upstream reservoirs in the Gunnison that is responsible for supplying emergency water to Lake Powell." ] }, { @@ -123,7 +148,7 @@ }, "outputs": [], "source": [ - "# create a directory to store the historical reservoir levels at Blue Mesa\n", + "# create a directory to store the historical reservoir levels at Blue Mesa \n", "output_dir = os.path.join(data_dir, \"historic_reservoir_levels\")\n", "\n", "if not os.path.exists(output_dir):\n", @@ -133,20 +158,19 @@ "xre_file = os.path.join(data_dir, \"gm2015B.xre\")\n", "\n", "# structure ID for reservoir of interest\n", - "structure_ID = \"6203532\"\n", + "structure_ID = '6203532' \n", "\n", "# name of the reservoir\n", - "structure_name = \"Blue_Mesa\"\n", + "structure_name = 'Blue_Mesa' \n", "\n", "# extract the target info into a Pandas data frame\n", - "df = stm.extract_xre_data(\n", - " structure_name=structure_name,\n", - " structure_id=structure_ID,\n", - " input_file=xre_file,\n", - " basin_name=None,\n", - " output_directory=output_dir,\n", - " write_csv=True,\n", - " write_parquet=None,\n", + "df = stm.extract_xre_data(structure_name=structure_name,\n", + " structure_id=structure_ID,\n", + " input_file=xre_file,\n", + " basin_name=None,\n", + " output_directory=output_dir,\n", + " write_csv=True,\n", + " write_parquet=None\n", ")" ] }, @@ -170,12 +194,15 @@ "output_xre_file = os.path.join(output_dir, \"Blue_Mesa_xre_data.csv\")\n", "\n", "# read output data into a data frame\n", - "df = pd.read_csv(output_xre_file, usecols=[\"Year\", \"Init. Storage\"], index_col=False)\n", + "df = pd.read_csv(\n", + " output_xre_file, \n", + " usecols=['Year','Init. Storage'],\n", + " index_col=False) \n", "\n", "# calculate the annual average\n", - "df = df.groupby(\"Year\").mean().reset_index()\n", + "df = df.groupby('Year').mean().reset_index()\n", "\n", - "df" + "df\n" ] }, { @@ -197,11 +224,11 @@ "source": [ "fig, ax = plt.subplots()\n", "\n", - "plt.plot(df[\"Year\"], df[\"Init. Storage\"])\n", + "plt.plot(df['Year'], df['Init. Storage'])\n", "\n", "plt.title(\"Blue Mesa Storage\")\n", "plt.xlabel(\"Year\")\n", - "plt.ylabel(\"Reservoir Storage (AF)\")" + "plt.ylabel(\"Reservoir Storage (AF)\")\n" ] }, { @@ -217,7 +244,7 @@ "id": "6097aad9-56a4-4d43-872b-293299634ba6", "metadata": {}, "source": [ - "Now that we've run StateMod in baseline mode for the Gunnison, the next step is to run it in exploratory analysis mode. To do this, we need to create some plausible futures and adjust the input files for StateMod. In this step, we'll demonstrate Option 1 for `statemodify` adjustments using the `gm2015.eva` file as an example. Here we apply additives rather than multipliers. As done in Hadjimichael et al. (2020), we sample the change of evaporation between -15.24 and 30.46 cm/month (-0.5 to + 1 ft). The .eva file stores information for select larger reservoirs across all West Slope basins. We choose the ID that corresponds to Blue Mesa (10011). We create 2 alternative states of the world and store them in the `input_files` directory. " + "Now that we've run StateMod in baseline mode for the Gunnison, the next step is to run it in exploratory analysis mode. To do this, we need to create some plausible futures and adjust the input files for StateMod. In this step, we'll demonstrate Option 1 for `statemodify` adjustments using the `gm2015.eva` file as an example. Here we apply additives rather than multipliers. As done in Hadjimichael et al. (2020), we sample (using LHS) the change of evaporation between -15.24 and 30.46 cm/month (-0.5 to + 1 ft). The [`.eva`](https://opencdss.state.co.us/statemod/latest/doc-user/InputDescription/415/) file stores information for select larger reservoirs across all West Slope basins. We choose the ID that corresponds to Blue Mesa (10011). We create 2 alternative states of the world and store them in the `input_files` directory. " ] }, { @@ -229,8 +256,11 @@ }, "outputs": [], "source": [ - "# a dictionary to describe what you want to modify and the bounds for the LHS\n", - "setup_dict = {\"ids\": [\"10011\"], \"bounds\": [-0.5, 1.0]}\n", + "# a dictionary to describe what you want to modify and the bounds for the Latin hypercube sample. \n", + "setup_dict = {\n", + " \"ids\": ['10011'],\n", + " \"bounds\": [-0.5, 1.0]\n", + "}\n", "\n", "# create a directory to store the new files in if it does not exist\n", "output_directory = os.path.join(data_dir, \"input_files\")\n", @@ -259,24 +289,22 @@ "basin_name = \"Gunnison\"\n", "\n", "# generate a batch of files using generated LHS\n", - "stm.modify_eva(\n", - " modify_dict=setup_dict,\n", - " query_field=query_field,\n", - " output_dir=output_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " sampling_method=\"LHS\",\n", - " n_samples=n_samples,\n", - " skip_rows=skip_rows,\n", - " n_jobs=n_jobs,\n", - " seed_value=seed_value,\n", - " template_file=None,\n", - " factor_method=\"add\",\n", - " data_specification_file=None,\n", - " min_bound_value=-0.5,\n", - " max_bound_value=1.0,\n", - " save_sample=True,\n", - ")" + "stm.modify_eva(modify_dict=setup_dict,\n", + " query_field=query_field,\n", + " output_dir=output_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " sampling_method=\"LHS\",\n", + " n_samples=n_samples,\n", + " skip_rows=skip_rows,\n", + " n_jobs=n_jobs,\n", + " seed_value=seed_value,\n", + " template_file=None,\n", + " factor_method=\"add\",\n", + " data_specification_file=None,\n", + " min_bound_value=-0.5,\n", + " max_bound_value=1.0,\n", + " save_sample=True)\n" ] }, { @@ -299,10 +327,10 @@ "# path to the numpy file containing the samples\n", "eva_samples_file = os.path.join(output_directory, \"eva_2-samples_scenario-1.npy\")\n", "\n", - "# load samples\n", + "# load samples \n", "sample_array = np.load(eva_samples_file)\n", "\n", - "sample_array" + "sample_array\n" ] }, { @@ -318,7 +346,7 @@ "id": "13cefeb1-dc79-4b44-8e21-793af035322a", "metadata": {}, "source": [ - "Now that we have created the input files, the next step is to run StateMod with the new input files. We create a template .rsp file (`gm2015B_template_eva.rsp`) and swap in the path to the alternative `.eva` files that are created. Then we run StateMod for the two scenarios and extract the reservoir levels for Blue Mesa." + "Now that we have created the input files, the next step is to run StateMod with the new input files. We create a template `.rsp` file (`gm2015B_template_eva.rsp`) and swap in the path to the alternative `.eva` files that are created. Then we run StateMod for the two scenarios and extract the reservoir levels for Blue Mesa." ] }, { @@ -326,6 +354,7 @@ "execution_count": null, "id": "fc440b6f-e8ad-4863-a6ac-29c84ba724a0", "metadata": { + "scrolled": true, "tags": [] }, "outputs": [], @@ -336,41 +365,41 @@ "\n", "# read RSP template\n", "with open(eva_template_file) as template_obj:\n", + " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", + " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - "\n", + " \n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"EVA\": f\"../../input_files/gm2015B_{scenario}.eva\"}\n", - "\n", + " \n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - "\n", + " \n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - "\n", + " \n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"gm2015B_{scenario}.rsp\")\n", - "\n", + " \n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - "\n", + " \n", " # construct simulated basin path\n", - " simulated_basin_path = os.path.join(\n", - " simulated_scenario_dir, f\"gm2015B_{scenario}\"\n", - " )\n", + " simulated_basin_path = os.path.join(simulated_scenario_dir, f\"gm2015B_{scenario}\")\n", "\n", " # run StateMod\n", " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", - " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])" + " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n" ] }, { @@ -399,45 +428,43 @@ "outputs": [], "source": [ "# SOW 1\n", - "output_dir = os.path.join(scenarios_dir, \"S0_1\")\n", + "output_dir= os.path.join(scenarios_dir, \"S0_1\")\n", "\n", "# path the the xre file\n", - "xre_file = os.path.join(output_dir, \"gm2015B_S0_1.xre\")\n", + "xre_file = os.path.join(output_dir, \"gm2015B_S0_1.xre\") \n", "\n", "# structure ID for reservoir of interest\n", - "structure_ID = \"6203532\"\n", + "structure_ID = '6203532' \n", "\n", "# name of the reservoir\n", - "structure_name = \"Blue_Mesa\"\n", + "structure_name = 'Blue_Mesa' \n", "\n", "# extract the target info into a Pandas data frame\n", - "df = stm.extract_xre_data(\n", - " structure_name=structure_name,\n", - " structure_id=structure_ID,\n", - " input_file=xre_file,\n", - " basin_name=None,\n", - " output_directory=output_dir,\n", - " write_csv=True,\n", - " write_parquet=None,\n", + "df = stm.extract_xre_data(structure_name=structure_name,\n", + " structure_id=structure_ID,\n", + " input_file=xre_file,\n", + " basin_name=None,\n", + " output_directory=output_dir,\n", + " write_csv=True,\n", + " write_parquet=None\n", ")\n", "\n", "# SOW 2\n", - "output_dir = os.path.join(scenarios_dir, \"S1_1\")\n", + "output_dir= os.path.join(scenarios_dir, \"S1_1\")\n", "\n", "# path the the xre file\n", - "xre_file = os.path.join(output_dir, \"gm2015B_S1_1.xre\")\n", + "xre_file = os.path.join(output_dir, \"gm2015B_S1_1.xre\") \n", "\n", "\n", "# extract the target info into a Pandas data frame\n", - "df = stm.extract_xre_data(\n", - " structure_name=structure_name,\n", - " structure_id=structure_ID,\n", - " input_file=xre_file,\n", - " basin_name=None,\n", - " output_directory=output_dir,\n", - " write_csv=True,\n", - " write_parquet=None,\n", - ")" + "df = stm.extract_xre_data(structure_name=structure_name,\n", + " structure_id=structure_ID,\n", + " input_file=xre_file,\n", + " basin_name=None,\n", + " output_directory=output_dir,\n", + " write_csv=True,\n", + " write_parquet=None\n", + ")\n" ] }, { @@ -461,34 +488,32 @@ "historic_res_dir = os.path.join(data_dir, \"historic_reservoir_levels\")\n", "blue_mesa_file = os.path.join(historic_res_dir, \"Blue_Mesa_xre_data.csv\")\n", "\n", - "# Import baseline dataframe\n", - "baseline = pd.read_csv(\n", - " blue_mesa_file, index_col=False, usecols=[\"Year\", \"Init. Storage\"]\n", - ")\n", - "baseline = baseline.groupby(\"Year\").mean().reset_index()\n", + "# Import baseline dataframe \n", + "baseline = pd.read_csv(blue_mesa_file, index_col=False, usecols=['Year','Init. Storage']) \n", + "baseline = baseline.groupby('Year').mean().reset_index()\n", "\n", "# Import SOW1\n", "s0_1_file = os.path.join(scenarios_dir, \"S0_1\", \"Blue_Mesa_xre_data.csv\")\n", - "SOW1 = pd.read_csv(s0_1_file, index_col=False, usecols=[\"Year\", \"Init. Storage\"])\n", - "SOW1 = SOW1.groupby(\"Year\").mean().reset_index()\n", - "\n", + "SOW1 = pd.read_csv(s0_1_file, index_col=False, usecols=['Year','Init. Storage']) \n", + "SOW1 = SOW1.groupby('Year').mean().reset_index()\n", + " \n", "# Import SOW2\n", "s1_1_file = os.path.join(scenarios_dir, \"S1_1\", \"Blue_Mesa_xre_data.csv\")\n", - "SOW2 = pd.read_csv(s1_1_file, index_col=False, usecols=[\"Year\", \"Init. Storage\"])\n", - "SOW2 = SOW2.groupby(\"Year\").mean().reset_index()\n", - "\n", - "# Plot reservoir levels\n", + "SOW2 = pd.read_csv(s1_1_file, index_col=False, usecols=['Year','Init. Storage']) \n", + "SOW2 = SOW2.groupby('Year').mean().reset_index()\n", + " \n", + "# Plot reservoir levels \n", "fig, ax = plt.subplots()\n", "\n", - "plt.plot(baseline[\"Year\"], baseline[\"Init. Storage\"], label=\"Baseline\")\n", - "plt.plot(SOW1[\"Year\"], SOW1[\"Init. Storage\"], label=\"Reduced Evaporation\")\n", - "plt.plot(SOW2[\"Year\"], SOW2[\"Init. Storage\"], label=\"Increased Evaporation\")\n", + "plt.plot(baseline['Year'], baseline['Init. Storage'],label='Baseline')\n", + "plt.plot(SOW1['Year'], SOW1['Init. Storage'],label='Reduced Evaporation')\n", + "plt.plot(SOW2['Year'], SOW2['Init. Storage'],label='Increased Evaporation')\n", "\n", "plt.title(\"Blue Mesa Storage\")\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Reservoir Storage (AF)\")\n", "\n", - "plt.legend()" + "plt.legend()\n" ] }, { @@ -516,6 +541,33 @@ "\n", "Hadjimichael, A., Quinn, J., Wilson, E., Reed, P., Basdekas, L., Yates, D., & Garrison, M. (2020). Defining robustness, vulnerabilities, and consequential scenarios for diverse stakeholder interests in institutionally complex river basins. Earth's Future, 8(7), e2020EF001503." ] + }, + { + "cell_type": "markdown", + "id": "8863a968", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Tip: If you are interested in understanding how to apply `statemodify` functions to your own model, take a look at the source code found in the repository here: \n", + " \n", + " \n", + "
\n", + " 1. modify_eva()\n", + "\n", + "
\n", + " \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b972ae61", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -535,6 +587,35 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/notebooks/Quickstarter_Notebook_3_Final.ipynb b/notebooks/N3_Reservoir_File_Modification.ipynb similarity index 68% rename from notebooks/Quickstarter_Notebook_3_Final.ipynb rename to notebooks/N3_Reservoir_File_Modification.ipynb index 5f675bb..dbca4e1 100644 --- a/notebooks/Quickstarter_Notebook_3_Final.ipynb +++ b/notebooks/N3_Reservoir_File_Modification.ipynb @@ -37,16 +37,27 @@ "import logging\n", "import os\n", "import pickle\n", - "import subprocess\n", "from string import Template\n", + "import subprocess\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd\n", - "\n", + "import pandas as pd \n", "import statemodify as stm" ] }, + { + "cell_type": "markdown", + "id": "8f938dc6", + "metadata": {}, + "source": [ + "
\n", + " \n", + "NOTE: Each simulation in this notebook is run for the length of the historical period (from 1909-2013). If you want to reduce the length of the simulation, navigate to the `.ctl` file and adjust the `iystr` and `iyend` variables. For this notebook, this file is located in: `data/cm2015_StateMod/StateMod/cm2015.ctl` \n", + "\n", + "
" + ] + }, { "cell_type": "markdown", "id": "53074387-6935-497e-89a0-a35275d253f3", @@ -77,7 +88,12 @@ "statemod_exe = os.path.join(root_dir, \"statemod\")\n", "\n", "# data directory and root name for the target basin\n", - "data_dir = os.path.join(home_dir, \"data\", \"cm2015_StateMod\", \"StateMod\")\n", + "data_dir = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"cm2015_StateMod\",\n", + " \"StateMod\"\n", + ")\n", "\n", "# directory to the target basin input files with root name for the basin\n", "basin_path = os.path.join(data_dir, \"cm2015B\")\n", @@ -90,7 +106,11 @@ "\n", "\n", "# path to res template file\n", - "res_template_file = os.path.join(home_dir, \"data\", \"cm2015B_template_res.rsp\")" + "res_template_file = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"cm2015B_template_res.rsp\"\n", + ")" ] }, { @@ -98,7 +118,11 @@ "id": "1e5678df-0b96-4184-9c77-2b737e638669", "metadata": {}, "source": [ - "
NOTE In order to expedite simulations for the Upper Colorado dataset, make sure to turn off \"Reoperation\" mode. You can do so by opening `data/cm2015_StateMod/StateMod/cm2015.ctl`, navigating to the `ireopx` entry and changing the value from \"0\" to \"10\".
" + "
\n", + " \n", + "NOTE In order to expedite simulations for the Upper Colorado dataset, make sure to turn off \"Reoperation\" mode. You can do so by opening `/home/jovyan/data/cm2015_StateMod/StateMod/cm2015.ctl`, navigating to the `ireopx` entry and changing the value from \"0\" to \"10\". \n", + "\n", + "
" ] }, { @@ -106,15 +130,14 @@ "execution_count": null, "id": "d86d0e12-515a-4af5-94b6-0297b468de2f", "metadata": { + "scrolled": true, "tags": [] }, "outputs": [], "source": [ - "# Change directories first\n", - "os.chdir(\n", - " data_dir\n", - ") # This is needed specific to the Upper Colorado model as the path name is too long for the model to accept\n", - "subprocess.call([statemod_exe, \"cm2015B\", \"-simulate\"])" + "# Change directories first \n", + "os.chdir(data_dir) #This is needed specific to the Upper Colorado model as the path name is too long for the model to accept\n", + "subprocess.call([statemod_exe, \"cm2015B\", \"-simulate\"])\n" ] }, { @@ -136,9 +159,9 @@ }, "outputs": [], "source": [ - "# Extract shortages using statemodify convert_xdd() function\n", + "#Extract shortages using statemodify convert_xdd() function \n", "\n", - "# create a directory to store the historic shortages\n", + "# create a directory to store the historic shortages \n", "output_dir = os.path.join(data_dir, \"historic_shortages\")\n", "\n", "# create a directory to store the new files in if it does not exist\n", @@ -154,7 +177,7 @@ " # path, glob, or a list of paths/globs to the .xdd files you want to convert\n", " xdd_files=os.path.join(data_dir, \"*.xdd\"),\n", " # if the output .parquet files should only contain a subset of structure ids, list them here; None for all\n", - " id_subset=[\"3601008\"],\n", + " id_subset=['3601008'],\n", " # how many .xdd files to convert in parallel; optimally you will want 2-4 CPUs per parallel process\n", " parallel_jobs=2,\n", ")" @@ -177,16 +200,17 @@ }, "outputs": [], "source": [ - "data = pd.read_parquet(output_dir + \"/cm2015B.parquet\", engine=\"pyarrow\")\n", + "data=pd.read_parquet(output_dir +'/cm2015B.parquet',engine='pyarrow')\n", "\n", "\n", - "data[\"shortage_total\"] = data[\"shortage_total\"].astype(float)\n", - "data[\"year\"] = data[\"year\"].astype(int)\n", + "data['shortage_total']=data['shortage_total'].astype(float)\n", + "data['year']=data['year'].astype(int)\n", "\n", "fig, ax = plt.subplots()\n", "\n", - "for name, group in data.groupby(\"structure_id\"):\n", - " ax.scatter(group[\"year\"], group[\"shortage_total\"], label=name)\n", + "for name, group in data.groupby('structure_id'):\n", + " ax.scatter(\n", + " group['year'], group['shortage_total'], label=name)\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", @@ -215,7 +239,7 @@ "id": "2689a64a-07f8-4d0d-aaab-a8fc1ecc0624", "metadata": {}, "source": [ - "If we look at the `cm2015B.res` file, we see that Breckenridge has an account in the Clinton Gulch Reservoir, but a quick look in the `.opr` file also indicates that Breckenridge can receive water from the Dillon reservoir. Let’s investigate what happens to these shortages when storage at these two basins decreases using the `modify_res()` function. As done in Hadjimichael et al. (2020), we sample losses of up to 20% of the capacity of the reservoirs (informed by Graf et al. (2010)) which may be due to erosion and sedimentation of reservoirs in the UCRB, resulting in reduced storage. The accounts associated with the reservoirs are also reduced equally in order to accommodate the new storage level. For this example, we want to change the reservoir storage for a specific set of reservoirs by specifying the reservoir IDs for the `target_structure_id_list`. However, by setting `target_structure_id_list=None` we can decrease storage at all reservoirs in the basin." + "If we look at the `cm2015B.res` file (learn more about the [`.res`](https://opencdss.state.co.us/statemod/latest/doc-user/InputDescription/411/)file), we see that Breckenridge has an account in the Clinton Gulch Reservoir, but a quick look in the [`.opr`](https://opencdss.state.co.us/statemod/latest/doc-user/InputDescription/413/) file also indicates that Breckenridge can receive water from the Dillon reservoir. Let’s investigate what happens to these shortages when storage at these two basins decreases using the `modify_res()` function. As done in Hadjimichael et al. (2020), we sample losses using a Latin hypercube sampling of up to 20% of the capacity of the reservoirs (informed by Graf et al. (2010)) which may be due to erosion and sedimentation of reservoirs in the UCRB, resulting in reduced storage. The accounts associated with the reservoirs are also reduced equally in order to accommodate the new storage level. For this example, we want to change the reservoir storage for a specific set of reservoirs by specifying the reservoir IDs for the `target_structure_id_list`. However, by setting `target_structure_id_list=None` we can decrease storage at all reservoirs in the basin." ] }, { @@ -241,16 +265,14 @@ "# number of samples to generate\n", "n_samples = 1\n", "\n", - "stm.modify_res(\n", - " output_dir=output_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " target_structure_id_list=[\"3603575\", \"3604512\"],\n", - " seed_value=seed_value,\n", - " n_jobs=n_jobs,\n", - " n_samples=n_samples,\n", - " save_sample=True,\n", - ")" + "stm.modify_res(output_dir=output_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " target_structure_id_list=['3603575','3604512'],\n", + " seed_value=seed_value,\n", + " n_jobs=n_jobs,\n", + " n_samples=n_samples,\n", + " save_sample=True)\n" ] }, { @@ -271,8 +293,7 @@ "outputs": [], "source": [ "import numpy as np\n", - "\n", - "sample_array = np.load(output_directory + \"/res_1-samples_scenario-1.npy\")\n", + "sample_array = np.load(output_directory+'/res_1-samples_scenario-1.npy')\n", "sample_array" ] }, @@ -297,6 +318,7 @@ "execution_count": null, "id": "eaaec47d-0a39-4c7b-b8ed-54156d251a1f", "metadata": { + "scrolled": true, "tags": [] }, "outputs": [], @@ -307,31 +329,33 @@ "\n", "# read RSP template\n", "with open(res_template_file) as template_obj:\n", + " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", + " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - "\n", + " \n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"RES\": f\"../../input_files/cm2015B_{scenario}.res\"}\n", - "\n", + " \n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - "\n", + " \n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir_res, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - "\n", + " \n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"cm2015B_{scenario}.rsp\")\n", - "\n", + " \n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - "\n", + " \n", " # construct simulated basin path\n", " simulated_basin_path = f\"cm2015B_{scenario}\"\n", "\n", @@ -340,27 +364,16 @@ " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - "\n", - " # Save output to parquet files\n", - " print(\"creating parquet for \" + scenario)\n", - "\n", - " output_directory = os.path.join(parquet_dir_res + \"/scenario/\" + scenario)\n", - "\n", + " \n", + " #Save output to parquet files \n", + " print('creating parquet for ' + scenario)\n", + " \n", + " output_directory = os.path.join(parquet_dir_res+\"/scenario/\"+ scenario)\n", + " \n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - "\n", - " stm.xdd.convert_xdd(\n", - " output_path=output_directory,\n", - " allow_overwrite=True,\n", - " xdd_files=scenarios_dir_res\n", - " + \"/\"\n", - " + scenario\n", - " + \"/cm2015B_\"\n", - " + scenario\n", - " + \".xdd\",\n", - " id_subset=[\"3601008\"],\n", - " parallel_jobs=2,\n", - " )" + " \n", + " stm.xdd.convert_xdd(output_path=output_directory,allow_overwrite=True,xdd_files=scenarios_dir_res + \"/\"+ scenario + \"/cm2015B_\"+scenario+\".xdd\",id_subset=['3601008'],parallel_jobs=2)" ] }, { @@ -380,27 +393,21 @@ }, "outputs": [], "source": [ - "baseline = pd.read_parquet(\n", - " data_dir + \"/\" + \"historic_shortages/cm2015B.parquet\", engine=\"pyarrow\"\n", - ")\n", - "SOW_1 = pd.read_parquet(\n", - " parquet_dir_res + \"/scenario/S0_1/cm2015B_S0_1.parquet\", engine=\"pyarrow\"\n", - ")\n", - "\n", - "# Subtract shortages with respect to the baseline\n", - "subset_df = pd.concat(\n", - " [baseline[\"year\"], baseline[\"shortage_total\"], SOW_1[\"shortage_total\"]], axis=1\n", - ")\n", - "subset_df = subset_df.set_axis([\"Year\", \"Baseline\", \"SOW_1\"], axis=1)\n", - "subset_df[\"Baseline\"] = subset_df[\"Baseline\"].astype(float)\n", - "subset_df[\"SOW_1\"] = subset_df[\"SOW_1\"].astype(float)\n", - "subset_df[\"Year\"] = subset_df[\"Year\"].astype(int)\n", - "subset_df[\"SOW_1_diff\"] = subset_df[\"SOW_1\"] - subset_df[\"Baseline\"]\n", - "\n", - "# Plot shortages\n", + "baseline=pd.read_parquet(data_dir+'/'+'historic_shortages/cm2015B.parquet',engine='pyarrow')\n", + "SOW_1=pd.read_parquet(parquet_dir_res+'/scenario/S0_1/cm2015B_S0_1.parquet',engine='pyarrow')\n", + "\n", + "#Subtract shortages with respect to the baseline\n", + "subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total']],axis=1)\n", + "subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1'], axis=1)\n", + "subset_df['Baseline']=subset_df['Baseline'].astype(float)\n", + "subset_df['SOW_1']=subset_df['SOW_1'].astype(float)\n", + "subset_df['Year']=subset_df['Year'].astype(int)\n", + "subset_df['SOW_1_diff']=subset_df['SOW_1']-subset_df['Baseline']\n", + "\n", + "#Plot shortages\n", "fig, ax = plt.subplots()\n", "\n", - "ax.scatter(subset_df[\"Year\"], subset_df[\"SOW_1_diff\"])\n", + "ax.scatter(subset_df['Year'], subset_df['SOW_1_diff'])\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", @@ -434,9 +441,35 @@ "Graf, W. L., Wohl, E., Sinha, T., & Sabo, J. L. (2010). Sedimentation and sustainability of western American reservoirs. Water Resources Research, 46, W12535. https://doi.org/10.1029/2009WR008836\n", "\n", "\n", - "Hadjimichael, A., Quinn, J., Wilson, E., Reed, P., Basdekas, L., Yates, D., & Garrison, M. (2020). Defining robustness, vulnerabilities, and consequential scenarios for diverse stakeholder interests in institutionally complex river basins. Earth's Future, 8(7), e2020EF001503.\n", - "\n" + "Hadjimichael, A., Quinn, J., Wilson, E., Reed, P., Basdekas, L., Yates, D., & Garrison, M. (2020). Defining robustness, vulnerabilities, and consequential scenarios for diverse stakeholder interests in institutionally complex river basins. Earth's Future, 8(7), e2020EF001503.\n" + ] + }, + { + "cell_type": "markdown", + "id": "6515b2bf", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Tip: If you are interested in understanding how to apply `statemodify` functions to your own model, take a look at the source code found in the repository here: \n", + " \n", + " \n", + "
\n", + " 1. modify_res()\n", + "\n", + "
\n", + " \n", + "\n", + "
\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c0e2194", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -456,6 +489,35 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/notebooks/Quickstarter_Notebook_4_Final.ipynb b/notebooks/N4_Streamflow_File_Modification.ipynb similarity index 68% rename from notebooks/Quickstarter_Notebook_4_Final.ipynb rename to notebooks/N4_Streamflow_File_Modification.ipynb index 0158490..b341b94 100644 --- a/notebooks/Quickstarter_Notebook_4_Final.ipynb +++ b/notebooks/N4_Streamflow_File_Modification.ipynb @@ -34,6 +34,18 @@ "By fitting the HMM on the historical time series of the basins, we create synthetic traces that are stationary and thus give an indication of the extent of natural variability that characterizes the region. The HMM can be extended to create non-stationary traces, as shown in Hadjimichael et al. (2020), but that version of the model is not currently included in `statemodify`. " ] }, + { + "cell_type": "markdown", + "id": "b434d9ed", + "metadata": {}, + "source": [ + "
\n", + " \n", + "NOTE: Each simulation in this notebook is run for the length of the historical period (from 1909-2013). If you want to reduce the length of the simulation, navigate to the `.ctl` file and adjust the `iystr` and `iyend` variables. For this notebook, this file is located in: `data/cm2015_StateMod/StateMod/cm2015.ctl` \n", + "\n", + "
" + ] + }, { "cell_type": "markdown", "id": "93e6f259-5b24-49e7-9ab1-ad26841fe200", @@ -55,13 +67,12 @@ "import logging\n", "import os\n", "import pickle\n", - "import subprocess\n", "from string import Template\n", + "import subprocess\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd\n", - "\n", + "import pandas as pd \n", "import statemodify as stm" ] }, @@ -95,7 +106,12 @@ "statemod_exe = os.path.join(root_dir, \"statemod\")\n", "\n", "# data directory and root name for the target basin\n", - "data_dir = os.path.join(home_dir, \"data\", \"cm2015_StateMod\", \"StateMod\")\n", + "data_dir = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"cm2015_StateMod\",\n", + " \"StateMod\"\n", + ")\n", "\n", "# directory to the target basin input files with root name for the basin\n", "basin_path = os.path.join(data_dir, \"cm2015B\")\n", @@ -103,8 +119,12 @@ "# scenarios output directory\n", "scenarios_dir = os.path.join(data_dir, \"scenarios\")\n", "\n", - "# path to iwr/xbm file\n", - "xbm_iwr_template_file = os.path.join(home_dir, \"data\", \"cm2015B_template_xbm_iwr.rsp\")" + "# path to iwr/xbm file \n", + "xbm_iwr_template_file = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"cm2015B_template_xbm_iwr.rsp\"\n", + ")" ] }, { @@ -124,7 +144,7 @@ }, "outputs": [], "source": [ - "# Make directory to store HMM parameters\n", + "#Make directory to store HMM parameters\n", "\n", "output_dir = os.path.join(data_dir, \"HMM_parameters\")\n", "\n", @@ -136,9 +156,9 @@ "# choice to save parameters to NumPy array files\n", "save_parameters = True\n", "\n", - "fit_array_dict = stm.hmm_multisite_fit(\n", - " n_basins=n_basins, save_parameters=save_parameters, output_directory=output_dir\n", - ")\n", + "fit_array_dict = stm.hmm_multisite_fit(n_basins=n_basins,\n", + " save_parameters=save_parameters,\n", + " output_directory=output_dir)\n", "\n", "# unpack output dictionary\n", "unconditional_dry = fit_array_dict[\"unconditional_dry\"]\n", @@ -148,7 +168,7 @@ "covariance_matrix_wet = fit_array_dict[\"covariance_matrix_wet\"]\n", "covariance_matrix_dry = fit_array_dict[\"covariance_matrix_dry\"]\n", "wet_state_means = fit_array_dict[\"wet_state_means\"]\n", - "dry_state_means = fit_array_dict[\"dry_state_means\"]" + "dry_state_means = fit_array_dict[\"dry_state_means\"]\n" ] }, { @@ -176,7 +196,7 @@ }, "outputs": [], "source": [ - "# Create a folder to store the runs\n", + "#Create a folder to store the runs\n", "\n", "output_dir = os.path.join(data_dir, \"HMM_Runs\")\n", "\n", @@ -184,17 +204,15 @@ " os.makedirs(output_dir)\n", "\n", "# using the outputs of the fit function above; this function write output sample files to the output directory\n", - "stm.hmm_multisite_sample(\n", - " logAnnualQ_h,\n", - " transition_matrix,\n", - " unconditional_dry,\n", - " dry_state_means,\n", - " wet_state_means,\n", - " covariance_matrix_dry,\n", - " covariance_matrix_wet,\n", - " n_basins=n_basins,\n", - " output_directory=output_dir,\n", - ")" + "stm.hmm_multisite_sample(logAnnualQ_h,\n", + " transition_matrix,\n", + " unconditional_dry,\n", + " dry_state_means,\n", + " wet_state_means,\n", + " covariance_matrix_dry,\n", + " covariance_matrix_wet,\n", + " n_basins=n_basins,\n", + " output_directory=output_dir)" ] }, { @@ -214,13 +232,10 @@ }, "outputs": [], "source": [ - "stm.plot_flow_duration_curves(\n", - " flow_realizations_directory=output_dir,\n", - " save_figure=True,\n", - " output_directory=output_dir,\n", - " figure_name=\"FDC\",\n", - " dpi=300,\n", - ")" + "stm.plot_flow_duration_curves(flow_realizations_directory=output_dir,\n", + " save_figure=True,output_directory=output_dir,\n", + " figure_name= 'FDC',\n", + " dpi= 300)" ] }, { @@ -238,7 +253,7 @@ "id": "aa178c4c-dfd6-4e92-b07d-2fecef231212", "metadata": {}, "source": [ - "In order for the HMM to be used in conjunction with StateMod, we utilize a statistical disaggregation technique to disaggregate the synthetically generated outlet gauge flow to the upstream nodes and also from an annual to monthly time scale. The synthetic log-space annual flows are converted to real space and temporally downscaled to monthly flows using a modification of the proportional scaling method used by Nowak et al. (2010). First, a historical year is probabilistically selected based on its “nearness” to the synthetic flow at the last node in terms of annual total. The shifted monthly flows at the last node are then downscaled to all other nodes using the same ratios of monthly flows at the upstream nodes to the last node as in the historically selected year.Though not demonstrated in this notebook, the irrigation demands (in the `.iwr` file) are also inherently tied to the generation of the streamflow, as irrigation demands will increase in dry years. Thus, a regression is fit across historical irrigation anomalies and historical annual flow anomalies and the appropriate irrigation anomaly is determined from this regression for every synthetically generated flow anomaly. More information on this method can be found in Hadjimichael et al., 2020. All of this functionality is embedded in the `modify_xbm_iwr()` function. " + "In order for the HMM to be used in conjunction with StateMod, we utilize a statistical disaggregation technique to disaggregate the synthetically generated outlet gauge flow to the upstream nodes and also from an annual to monthly time scale. The synthetic log-space annual flows are converted to real space and temporally downscaled to monthly flows using a modification of the proportional scaling method used by Nowak et al. (2010). First, a historical year is probabilistically selected based on its “nearness” to the synthetic flow at the last node in terms of annual total. The shifted monthly flows at the last node are then downscaled to all other nodes using the same ratios of monthly flows at the upstream nodes to the last node as in the historically selected year.Though not demonstrated in this notebook, the irrigation demands (in the [`.iwr`](https://opencdss.state.co.us/statemod/latest/doc-user/InputDescription/41/) file) are also inherently tied to the generation of the streamflow, as irrigation demands will increase in dry years. Thus, a regression is fit across historical irrigation anomalies and historical annual flow anomalies and the appropriate irrigation anomaly is determined from this regression for every synthetically generated flow anomaly. More information on this method can be found in Hadjimichael et al., 2020. All of this functionality is embedded in the `modify_xbm_iwr()` function. " ] }, { @@ -250,16 +265,16 @@ }, "outputs": [], "source": [ - "# Make directory to store input files\n", + "#Make directory to store input files \n", "\n", "output_dir = os.path.join(data_dir, \"input_files\")\n", "\n", "if not os.path.exists(output_dir):\n", " os.makedirs(output_dir)\n", - "\n", + " \n", "\n", "flow_realizations_directory = os.path.join(data_dir, \"HMM_Runs\")\n", - "\n", + " \n", "scenario = \"1\"\n", "\n", "# basin name to process\n", @@ -275,17 +290,15 @@ "n_samples = 1\n", "\n", "# generate a batch of files using generated LHS\n", - "stm.modify_xbm_iwr(\n", - " output_dir=output_dir,\n", - " flow_realizations_directory=flow_realizations_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " seed_value=seed_value,\n", - " n_jobs=n_jobs,\n", - " n_samples=n_samples,\n", - " save_sample=True,\n", - " randomly_select_flow_sample=True,\n", - ")" + "stm.modify_xbm_iwr(output_dir=output_dir,\n", + " flow_realizations_directory=flow_realizations_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " seed_value=seed_value,\n", + " n_jobs=n_jobs,\n", + " n_samples=n_samples,\n", + " save_sample=True,\n", + " randomly_select_flow_sample=True) " ] }, { @@ -301,7 +314,7 @@ "id": "77a86281-2bda-4f2e-8db3-62d28c514142", "metadata": {}, "source": [ - "Now that we have created the new files, the next step is to run them through StateMod. We create a template `.rsp` file (`cm2015B_template_xbm_iwr.rsp`) and swap in the path to the alternative `.xbm` and `.iwr` files that are created. Then we run StateMod for the single scenario and one can then go on and extract shortages or reservoir levels." + "Now that we have created the new files, the next step is to run them through StateMod. We create a template `.rsp` file (`cm2015B_template_xbm_iwr.rsp`) and swap in the path to the alternative [`.xbm`](https://opencdss.state.co.us/statemod/latest/doc-user/OutputDescription/513/) and `.iwr` files that are created. Then we run StateMod for the single scenario and one can then go on and extract shortages or reservoir levels." ] }, { @@ -311,7 +324,11 @@ "tags": [] }, "source": [ - "
NOTE In order to expedite simulations for the Upper Colorado dataset, make sure to turn off \"Reoperation\" mode. You can do so by opening /home/jovyan/data/cm2015_StateMod/StateMod/cm2015.ctl, navigating to the \"ireopx\" entry and changing the value from \"0\" to \"10\".
\n" + "
\n", + " \n", + "NOTE In order to expedite simulations for the Upper Colorado dataset, make sure to turn off \"Reoperation\" mode. You can do so by opening `/home/jovyan/data/cm2015_StateMod/StateMod/cm2015.ctl`, navigating to the `ireopx` entry and changing the value from \"0\" to \"10\". \n", + "\n", + "
" ] }, { @@ -319,6 +336,7 @@ "execution_count": null, "id": "1a2e87bb-5676-4a51-ad85-e0b88ab0437d", "metadata": { + "scrolled": true, "tags": [] }, "outputs": [], @@ -329,34 +347,33 @@ "\n", "# read RSP template\n", "with open(xbm_iwr_template_file) as template_obj:\n", + " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", + " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - "\n", + " \n", " # dictionary holding search keys and replacement values to update the template file\n", - " d = {\n", - " \"XBM\": f\"../../input_files/cm2015B_{scenario}.xbm\",\n", - " \"IWR\": f\"../../input_files/cm2015B_{scenario}.iwr\",\n", - " }\n", - "\n", + " d = {\"XBM\": f\"../../input_files/cm2015B_{scenario}.xbm\",\"IWR\": f\"../../input_files/cm2015B_{scenario}.iwr\"}\n", + " \n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - "\n", + " \n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - "\n", + " \n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"cm2015B_{scenario}.rsp\")\n", - "\n", + " \n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - "\n", + " \n", " # construct simulated basin path\n", " simulated_basin_path = f\"cm2015B_{scenario}\"\n", "\n", @@ -364,7 +381,7 @@ " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", - " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])" + " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n" ] }, { @@ -385,16 +402,14 @@ "outputs": [], "source": [ "# Example with Granby Lake\n", - "zip_file_path = os.path.join(home_dir, \"data\", \"Granby_Dataset.zip\")\n", - "final_directory = os.path.join(home_dir, \"data/\")\n", + "zip_file_path = os.path.join(home_dir, 'data', 'Granby_Dataset.zip')\n", + "final_directory = os.path.join(home_dir, 'data/')\n", "\n", - "!unzip $zip_file_path -d $final_directory\n", - "granby_hmm, granby_hist, granby_hist_mean, granby_hist_1p = stm.read_xre(\n", - " os.path.join(home_dir, \"data/Upper_Colorado/\"), \"Granby\"\n", - ")\n", + "!unzip $zip_file_path -d $final_directory \n", + "granby_hmm, granby_hist, granby_hist_mean, granby_hist_1p = stm.read_xre(os.path.join(home_dir,\"data/Upper_Colorado/\"), 'Granby')\n", "\n", "# Plot quantiles\n", - "stm.plot_res_quantiles(granby_hmm, granby_hist_mean, \"Lake Granby\")" + "stm.plot_res_quantiles(granby_hmm, granby_hist_mean, 'Lake Granby')" ] }, { @@ -423,7 +438,7 @@ "outputs": [], "source": [ "# using the output of the above `read_xre` function as inputs\n", - "stm.plot_reservoir_boxes(granby_hmm, granby_hist, \"Lake Granby\")" + "stm.plot_reservoir_boxes(granby_hmm, granby_hist, 'Lake Granby')" ] }, { @@ -434,6 +449,34 @@ "Here, the blue box plots correspond to the HMM-generated reservoir storages and the orange box plots correspond to the historical monthly dataset. The black circles represent outliers. As illustrated in the quantile plot above as well, for all months, the HMM is creating a wider distribution of reservoir storages, and tends to be able to encompass even historical outliers. Remember that the HMM has only been fit on the historical dataset. Thus, the HMM can provide an estimate of the expanse of reservoir storages that can be expected just within the range of natural variability, which is quite large! Particularly, the HMM is creating many more instances of drier scenarios and lower reservoir levels which can be very useful for informing drought vulnerability assessments. " ] }, + { + "cell_type": "markdown", + "id": "3d4b8467", + "metadata": {}, + "source": [ + "
\n", + " \n", + "NOTE: If you are curious to learn more about HMM-based synthetic streamflow generation, including model fitting, validation, and applications, please refer to the following resources:\n", + " \n", + " \n", + "
\n", + " 1. Fitting Hidden Markov Models: Background and Methods\n", + "\n", + "
\n", + "\n", + "
\n", + " 2. Fitting Hidden Markov Models: Sample Scripts \n", + "\n", + "
\n", + " \n", + "
\n", + " 3. A Hidden-Markov Modeling Approach to Creating Synthetic Streamflow Scenarios Tutorial \n", + "\n", + "
\n", + "\n", + "
\n" + ] + }, { "cell_type": "markdown", "id": "3b4b9bfa-790d-4e2a-9e50-229811da69bf", @@ -441,8 +484,37 @@ "source": [ "### References\n", "\n", - "Nowak, K., Prairie, J., Rajagopalan, B., & Lall, U. (2010). A nonparametric stochastic approach for multisite disaggregation of annual to daily streamflow. Water resources research, 46(8)." + "Nowak, K., Prairie, J., Rajagopalan, B., & Lall, U. (2010). A nonparametric stochastic approach for multisite disaggregation of annual to daily streamflow. Water resources research, 46(8).\n", + "\n", + "Hadjimichael, A., Quinn, J., Wilson, E., Reed, P., Basdekas, L., Yates, D., & Garrison, M. (2020). Defining robustness, vulnerabilities, and consequential scenarios for diverse stakeholder interests in institutionally complex river basins. Earth's Future, 8(7), e2020EF001503." + ] + }, + { + "cell_type": "markdown", + "id": "09acc4e8", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Tip: If you are interested in understanding how to apply `statemodify` functions to your own model, take a look at the source code found in the repository here: \n", + " \n", + " \n", + "
\n", + " 1. modify_xbm_iwr()\n", + "\n", + "
\n", + " \n", + "\n", + "
" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25fd1a68", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -462,6 +534,35 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/notebooks/Quickstarter_Notebook_5_Final.ipynb b/notebooks/N5_Batch_Modification.ipynb similarity index 96% rename from notebooks/Quickstarter_Notebook_5_Final.ipynb rename to notebooks/N5_Batch_Modification.ipynb index 533f0d1..9099c0a 100644 --- a/notebooks/Quickstarter_Notebook_5_Final.ipynb +++ b/notebooks/N5_Batch_Modification.ipynb @@ -13,14 +13,18 @@ "id": "d8663c59-537b-47c5-ab92-8ffecf19d01a", "metadata": {}, "source": [ - "In the prior notebooks, we sampled just one type of uncertainty at a time to demonstrate how a single adjustment in the selected input file leads to a verifiable change in output so that we can demonstrate that `statemodify` is working as expected. It's much harder to make sense of the results when we sample multiple uncertainties unless we use a formal sensitivity analysis, which lies outside of the bounds of this tutorial. However, it is very likely that many of these uncertainties will be present in any given future for the region. Thus, this notebook is used to demonstrate how to do a sample across multiple uncertainties in a given basin using the `modify_batch()` function." + "In the prior notebooks, we sampled just one type of uncertainty at a time to demonstrate how a single adjustment in the selected input file leads to a verifiable change in output so that we can demonstrate that `statemodify` is working as expected. It is much harder to make sense of the relative importance of uncertain drivers unless we conduct a formal sensitivity analysis, which lies outside of the bounds of this tutorial. However, it is very likely that many of these uncertainties will be present and of interest in any given future for the region. Thus, this notebook is used to demonstrate how to do a Latin hypercube sample simultaneously across multiple uncertainties in a given basin using the `modify_batch()` function. " ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "id": "fbd29da0-2275-4bee-b232-26f6718aba49", "metadata": { + "ExecuteTime": { + "end_time": "2024-03-22T18:38:57.615968Z", + "start_time": "2024-03-22T18:38:57.611752Z" + }, "tags": [] }, "outputs": [], @@ -29,21 +33,36 @@ "import logging\n", "import os\n", "import pickle\n", - "import subprocess\n", "from string import Template\n", + "import subprocess\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd\n", - "\n", + "import pandas as pd \n", "import statemodify as stm" ] }, + { + "cell_type": "markdown", + "id": "0878fd2c", + "metadata": {}, + "source": [ + "
\n", + " \n", + "NOTE: Each simulation in this notebook is run for the length of the historical period (from 1909-2013). If you want to reduce the length of the simulation, navigate to the `.ctl` file and adjust the `iystr` and `iyend` variables. For this notebook, this file is located in: `data/cm2015_StateMod/StateMod/cm2015.ctl` \n", + "\n", + "
" + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "id": "f0804d05-d034-4c0d-af23-e6e8211a7afe", "metadata": { + "ExecuteTime": { + "end_time": "2024-03-22T18:38:59.943549Z", + "start_time": "2024-03-22T18:38:59.932554Z" + }, "tags": [] }, "outputs": [], @@ -61,7 +80,12 @@ "statemod_exe = os.path.join(root_dir, \"statemod\")\n", "\n", "# data directory and root name for the target basin\n", - "data_dir = os.path.join(home_dir, \"data\", \"cm2015_StateMod\", \"StateMod\")\n", + "data_dir = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"cm2015_StateMod\",\n", + " \"StateMod\"\n", + ")\n", "\n", "# directory to the target basin input files with root name for the basin\n", "basin_path = os.path.join(data_dir, \"cm2015B\")\n", @@ -69,12 +93,16 @@ "# scenarios output directory\n", "scenarios_dir = os.path.join(data_dir, \"scenarios\")\n", "\n", - "# parquet output directory\n", - "parquet_dir = os.path.join(data_dir, \"parquet\")\n", + "#parquet output directory\n", + "parquet_dir=os.path.join(data_dir, \"parquet\")\n", "\n", "\n", "# path to template file\n", - "multi_template_file = os.path.join(home_dir, \"data\", \"cm2015B_template_multi.rsp\")" + "multi_template_file = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"cm2015B_template_multi.rsp\"\n", + ")" ] }, { @@ -90,7 +118,19 @@ "id": "b7a46d6a-e87f-4c9f-be4b-6be7fd43924f", "metadata": {}, "source": [ - "In this example, we create a global Latin hypercube sample across 3 example uncertainties that we are interested in for the Upper Colorado: evaporation at reservoirs, modification of water rights, and demands. The form of the function is similiar to those presented in the last notebooks. Note that if you are interested in creating an alternative `.ddr` file that does not sample decrees, only adjusts the water rights, then you will need to write `None` in the \"bounds\" parameter as we do below. If you include bounds, then decrees as well as water rights will be adjusted simultaneously. " + "In this example, we create a global Latin hypercube sample across 3 example uncertainties that we are interested in for the Upper Colorado: evaporation at reservoirs, modification of water rights, and demands. The form of the `modify_batch()` function is similiar to those presented in the last notebooks; however, now, a problem dictionary stores the names of the respective `statemodify` functions (`modify_eva`, `modify_ddr`, `modify_ddm`) that will now be applied simultaneously. The Latin hypercube sample is conducted with respect to the `bounds` listed and the resulting multipliers or additives are applied to the target IDs listed." + ] + }, + { + "cell_type": "markdown", + "id": "27ca5c76", + "metadata": {}, + "source": [ + "
\n", + " \n", + "NOTE: If you are interested in creating an alternative `.ddr` file that does not sample decrees, only adjusts the water rights, then you will need to write `None` in the \"bounds\" parameter as we do below. If you include bounds, then decrees as well as water rights will be adjusted simultaneously. \n", + "\n", + "
" ] }, { @@ -98,16 +138,22 @@ "execution_count": 3, "id": "edbbf05b-5723-40dd-9dac-5d3ca2d40ffa", "metadata": { + "ExecuteTime": { + "end_time": "2024-03-22T18:38:52.723784Z", + "start_time": "2024-03-22T18:38:52.284720Z" + }, "tags": [] }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running modify_eva\n", - "Running modify_ddr\n", - "Running modify_ddm\n" + "ename": "AttributeError", + "evalue": "module 'statemodify' has no attribute 'modify_batch'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[3], line 49\u001b[0m\n\u001b[0;32m 10\u001b[0m problem_dict \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 11\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_samples\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m 12\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnum_vars\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m3\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 45\u001b[0m }\n\u001b[0;32m 46\u001b[0m }\n\u001b[0;32m 48\u001b[0m \u001b[38;5;66;03m# run in batch\u001b[39;00m\n\u001b[1;32m---> 49\u001b[0m fn_parameter_dict \u001b[38;5;241m=\u001b[39m \u001b[43mstm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodify_batch\u001b[49m(problem_dict\u001b[38;5;241m=\u001b[39mproblem_dict)\n", + "\u001b[1;31mAttributeError\u001b[0m: module 'statemodify' has no attribute 'modify_batch'" ] } ], @@ -123,9 +169,13 @@ "# problem dictionary\n", "problem_dict = {\n", " \"n_samples\": 1,\n", - " \"num_vars\": 3,\n", - " \"names\": [\"modify_eva\", \"modify_ddr\", \"modify_ddm\"],\n", - " \"bounds\": [[-0.5, 1.0], None, [0.5, 1.0]],\n", + " 'num_vars': 3,\n", + " 'names': ['modify_eva', 'modify_ddr', 'modify_ddm'],\n", + " 'bounds': [\n", + " [-0.5, 1.0],\n", + " None,\n", + " [0.5, 1.0]\n", + " ],\n", " # additional settings for each function\n", " \"modify_eva\": {\n", " \"seed_value\": seed_value,\n", @@ -133,7 +183,7 @@ " \"scenario\": scenario,\n", " \"basin_name\": basin_name,\n", " \"query_field\": \"id\",\n", - " \"ids\": [\"10008\", \"10009\"],\n", + " \"ids\": [\"10008\", \"10009\"]\n", " },\n", " \"modify_ddr\": {\n", " \"seed_value\": seed_value,\n", @@ -143,7 +193,7 @@ " \"query_field\": \"id\",\n", " \"ids\": [\"3600507.01\", \"3600507.02\"],\n", " \"admin\": [1, None],\n", - " \"on_off\": [1, 1],\n", + " \"on_off\": [1, 1]\n", " },\n", " \"modify_ddm\": {\n", " \"seed_value\": seed_value,\n", @@ -151,8 +201,8 @@ " \"scenario\": scenario,\n", " \"basin_name\": basin_name,\n", " \"query_field\": \"id\",\n", - " \"ids\": [\"3600507\", \"3600603\"],\n", - " },\n", + " \"ids\": [\"3600507\", \"3600603\"]\n", + " }\n", "}\n", "\n", "# run in batch\n", @@ -172,7 +222,7 @@ "id": "a0b6a4d4-ca0d-403c-a8e7-4a5427af7299", "metadata": {}, "source": [ - "Now that we have developed the samples, we need to adjust our template file to take in the additional uncertainties and then we can run our simulation!" + "Now that we have developed the samples, we need to adjust our template file to take in the additional uncertainties and then we can run our simulation! Note that in contrast to the other notebooks, we are changing the \"EVA\", \"DDM\", and \"DDR\" entries in the `.rsp` file at the same time, running the simulation, and then extracting the shortages for a specific user (ID: 3601008)." ] }, { @@ -180,7 +230,11 @@ "id": "f0d31cc7-9dde-419e-8ef1-93d9e18bbe0d", "metadata": {}, "source": [ - "
NOTE In order to expedite simulations for the Upper Colorado dataset, make sure to turn off \"Reoperation\" mode. You can do so by opening `data/cm2015_StateMod/StateMod/cm2015.ctl`, navigating to the `ireopx` entry and changing the value from \"0\" to \"10\".
" + "
\n", + " \n", + "NOTE In order to expedite simulations for the Upper Colorado dataset, make sure to turn off \"Reoperation\" mode. You can do so by opening `/home/jovyan/data/cm2015_StateMod/StateMod/cm2015.ctl`, navigating to the `ireopx` entry and changing the value from \"0\" to \"10\". \n", + "\n", + "
" ] }, { @@ -188,6 +242,7 @@ "execution_count": 4, "id": "ff69ed3a-8a37-493a-93f6-c3f2fcd8417b", "metadata": { + "scrolled": true, "tags": [] }, "outputs": [ @@ -2973,35 +3028,33 @@ "\n", "# read RSP template\n", "with open(multi_template_file) as template_obj:\n", + " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", + " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - "\n", + " \n", " # dictionary holding search keys and replacement values to update the template file\n", - " d = {\n", - " \"EVA\": f\"../../input_files/cm2015B_{scenario}.eva\",\n", - " \"DDM\": f\"../../input_files/cm2015B_{scenario}.ddm\",\n", - " \"DDR\": f\"../../input_files/cm2015B_{scenario}.ddr\",\n", - " }\n", - "\n", + " d = {\"EVA\": f\"../../input_files/cm2015B_{scenario}.eva\",\"DDM\": f\"../../input_files/cm2015B_{scenario}.ddm\",\"DDR\": f\"../../input_files/cm2015B_{scenario}.ddr\"}\n", + " \n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - "\n", + " \n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - "\n", + " \n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"cm2015B_{scenario}.rsp\")\n", - "\n", + " \n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - "\n", + " \n", " # construct simulated basin path\n", " simulated_basin_path = f\"cm2015B_{scenario}\"\n", "\n", @@ -3010,22 +3063,16 @@ " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - "\n", - " # Save output to parquet files\n", - " print(\"creating parquet for \" + scenario)\n", - "\n", - " output_directory = os.path.join(parquet_dir + \"/scenario/\" + scenario)\n", - "\n", + " \n", + " #Save output to parquet files \n", + " print('creating parquet for ' + scenario)\n", + " \n", + " output_directory = os.path.join(parquet_dir+ \"/scenario/\"+ scenario)\n", + " \n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - "\n", - " stm.xdd.convert_xdd(\n", - " output_path=output_directory,\n", - " allow_overwrite=False,\n", - " xdd_files=scenarios_dir + \"/\" + scenario + \"/cm2015B_\" + scenario + \".xdd\",\n", - " id_subset=[\"3601008\"],\n", - " parallel_jobs=2,\n", - " )" + " \n", + " stm.xdd.convert_xdd(output_path=output_directory,allow_overwrite=False,xdd_files=scenarios_dir + \"/\"+ scenario + \"/cm2015B_\"+scenario+\".xdd\",id_subset=['3601008'],parallel_jobs=2)\n" ] }, { @@ -3033,8 +3080,34 @@ "id": "bb512da8-972d-4042-a30f-c892e46dadaa", "metadata": {}, "source": [ - "The user can then proceed to do similiar analyses on water shortages for selected users. " + "At the end of the simulation, the output is the file, `cm2015B_S0_1.parquet`, which now contains the shortages for the target ID for the length of the simulation. The user can then proceed to do similiar analyses on water shortages that have been demonstrated in the prior notebooks." ] + }, + { + "cell_type": "markdown", + "id": "a294020a", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Tip: If you are interested in understanding how to apply `statemodify` functions to your own model, take a look at the source code found in the repository here: \n", + " \n", + " \n", + "
\n", + " 1. modify_batch()\n", + "\n", + "
\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36b357a3", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -3054,6 +3127,35 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, From f21595fe91bb5c593932f90ebc05aae0b63225cb Mon Sep 17 00:00:00 2001 From: crvernon Date: Mon, 8 Apr 2024 14:27:20 -0400 Subject: [PATCH 02/12] remove relect notebook --- notebooks/quickstarter.ipynb | 241 ----------------------------------- 1 file changed, 241 deletions(-) delete mode 100644 notebooks/quickstarter.ipynb diff --git a/notebooks/quickstarter.ipynb b/notebooks/quickstarter.ipynb deleted file mode 100644 index 74ab5f7..0000000 --- a/notebooks/quickstarter.ipynb +++ /dev/null @@ -1,241 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8363764e-8c63-40b4-8991-7d02f106b19c", - "metadata": {}, - "source": [ - "# `statemodify` quickstarter tutorial\n" - ] - }, - { - "cell_type": "markdown", - "id": "1574d6c8-8c94-4be8-8e74-c5b3ee719c0a", - "metadata": {}, - "source": [ - "## Getting started with `statemodify`" - ] - }, - { - "cell_type": "markdown", - "id": "37878833-0f3f-4616-a8cd-d4ef5082f9e5", - "metadata": {}, - "source": [ - "### Background" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a926309-1c4a-4d55-a54f-e9b7ef057254", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "df8ac670-24b9-4a77-8fe0-46e6004c05d5", - "metadata": {}, - "source": [ - "### Installation" - ] - }, - { - "cell_type": "markdown", - "id": "b7985d48-4dfb-4857-a079-d64093db1332", - "metadata": {}, - "source": [ - "See instructions to install `statemodify` here: [install statemodify](https://immm-sfa.github.io/statemodify/getting-started/installation.html)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae225f46-70f8-4531-8be0-cf6afe0850cf", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "ec4e2571-a38c-4f95-ab0b-c39f01501a4b", - "metadata": {}, - "source": [ - "## Generate modified inputs for StateMod" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ccb1516a-0a46-4cf8-b323-79124f53e99d", - "metadata": {}, - "outputs": [], - "source": [ - "import statemodify as stm\n", - "\n", - "# setup\n", - "output_directory = \".\"\n", - "scenario = \"quickstarter\"\n", - "\n", - "# the number of samples you wish to generate\n", - "n_samples = 4\n", - "\n", - "# seed value for reproducibility if so desired\n", - "seed_value = 777" - ] - }, - { - "cell_type": "markdown", - "id": "296aebba-b366-498f-bd6d-56eaa3aaf639", - "metadata": {}, - "source": [ - "### Modify EVA files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2c6be94f-f0e8-41c6-a66d-c57ad64994db", - "metadata": {}, - "outputs": [], - "source": [ - "# a dictionary to describe what you want to modify and the bounds for the LHS\n", - "modify_dict = {\n", - " \"ids\": [[\"10001\", \"10004\"], [\"10005\", \"10006\"]],\n", - " \"bounds\": [[-0.5, 1.0], [-0.5, 1.0]],\n", - "}\n", - "\n", - "# number of rows to skip in file after comment\n", - "skip_rows = 1\n", - "\n", - "# name of field to query\n", - "query_field = \"id\"\n", - "\n", - "# number of jobs to launch in parallel; -1 is all but 1 processor used\n", - "n_jobs = -1\n", - "\n", - "# generate a batch of files using generated LHS\n", - "stm.modify_eva(\n", - " modify_dict=modify_dict,\n", - " query_field=query_field,\n", - " output_dir=output_dir,\n", - " scenario=scenario,\n", - " sampling_method=\"LHS\",\n", - " n_samples=n_samples,\n", - " skip_rows=skip_rows,\n", - " n_jobs=n_jobs,\n", - " seed_value=seed_value,\n", - " template_file=None,\n", - " factor_method=\"add\",\n", - " data_specification_file=None,\n", - " min_bound_value=-0.5,\n", - " max_bound_value=1.0,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "e24dfbc7-f644-4d97-9314-2b0be0633698", - "metadata": {}, - "source": [ - "## Run StateMod \n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "86cf7cf4-680a-4f61-9947-132ae3effdf5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "quickstarter.ipynb\n" - ] - } - ], - "source": [ - "! ls" - ] - }, - { - "cell_type": "markdown", - "id": "72733853-6de6-46a4-842c-ff5606c7f72f", - "metadata": {}, - "source": [ - "## Compile outputs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee2f91e6-ae65-4239-be46-7724eb0d26b7", - "metadata": {}, - "outputs": [], - "source": [ - "import statemodify as stm\n", - "\n", - "stm.xdd.convert_xdd(\n", - " # path to a directory where output .parquet files should be written\n", - " output_path=\"./output\",\n", - " # whether to abort if .parquet files already exist at the output_path\n", - " allow_overwrite=False,\n", - " # path, glob, or a list of paths/globs to the .xdd files you want to convert\n", - " xdd_files=\"**/*.xdd\",\n", - " # if the output .parquet files should only contain a subset of structure ids, list them here; None for all\n", - " id_subset=None,\n", - " # how many .xdd files to convert in paralllel; optimally you will want 2-4 CPUs per parallel process\n", - " parallel_jobs=4,\n", - ")\n", - "\n", - "# look for your output .parquet files at the output_path!" - ] - }, - { - "cell_type": "markdown", - "id": "f2080889-215c-4144-a2e7-a15b6e3658aa", - "metadata": {}, - "source": [ - "## Visualize outputs\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b422f44b-f6e8-4988-926d-1d5238de0d59", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "abb16793-ab6a-4b75-8086-e272aaf50d94", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py3.9.4_statemodify", - "language": "python", - "name": "py3.9.4_statemodify" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 1cf7c799f22b985efc11c45a12fef4a724200342 Mon Sep 17 00:00:00 2001 From: crvernon Date: Mon, 8 Apr 2024 14:27:57 -0400 Subject: [PATCH 03/12] limit numpy to <2 for pyarrow --- requirements.txt | 4 ++-- setup.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index 3890cf0..d79b382 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ joblib>=1.1.0 -numpy>=1.22.3 -scipy>=1.8.0 +numpy>=1.22.3,<2 +scipy>=1.8.0,<2 pandas>=1.4.2 matplotlib>=3.5.1 pyarrow>=10.0.1 diff --git a/setup.py b/setup.py index 45e044e..b88a99c 100644 --- a/setup.py +++ b/setup.py @@ -30,7 +30,7 @@ def readme(): include_package_data=True, install_requires=[ "joblib>=1.1.0", - "numpy>=1.22.3", + "numpy>=1.22.3,<2", "pandas>=1.4.2", "joblib>=1.1.0", "SALib>1.4.5", From 55d9e5863e917cbb59c5544960011c9013a87db0 Mon Sep 17 00:00:00 2001 From: crvernon Date: Mon, 8 Apr 2024 14:41:34 -0400 Subject: [PATCH 04/12] remove codecov moving to paid plan --- .github/workflows/build.yml | 6 ------ 1 file changed, 6 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 0000b6b..935a760 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -38,9 +38,3 @@ jobs: pip install pytest pip install pytest-cov pytest --cov=./ --cov-report=xml - - name: Upload coverage to Codecov - if: matrix.os == 'ubuntu-latest' - uses: codecov/codecov-action@v1 - with: - file: ./coverage.xml - fail_ci_if_error: true From 7aea2dfc6e4147863701c8ac29d8e5484b50efb2 Mon Sep 17 00:00:00 2001 From: crvernon Date: Mon, 8 Apr 2024 15:15:51 -0400 Subject: [PATCH 05/12] pre-commit fixes to nbs --- ...Demand_WaterRights_File_Modification.ipynb | 290 ++++++++++-------- .../N2_Evaporation_File_Modification.ipynb | 192 ++++++------ .../N3_Reservoir_File_Modification.ipynb | 137 +++++---- .../N4_Streamflow_File_Modification.ipynb | 124 ++++---- notebooks/N5_Batch_Modification.ipynb | 82 +++-- 5 files changed, 429 insertions(+), 396 deletions(-) diff --git a/notebooks/N1_Demand_WaterRights_File_Modification.ipynb b/notebooks/N1_Demand_WaterRights_File_Modification.ipynb index 9169a91..63dd110 100644 --- a/notebooks/N1_Demand_WaterRights_File_Modification.ipynb +++ b/notebooks/N1_Demand_WaterRights_File_Modification.ipynb @@ -77,12 +77,13 @@ "import logging\n", "import os\n", "import pickle\n", - "from string import Template\n", "import subprocess\n", + "from string import Template\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd \n", + "import pandas as pd\n", + "\n", "import statemodify as stm" ] }, @@ -122,11 +123,7 @@ "\n", "# data directory and root name for the target basin\n", "data_dir = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"sj2015_StateMod_modified\",\n", - " \"sj2015_StateMod_modified\",\n", - " \"StateMod\"\n", + " home_dir, \"data\", \"sj2015_StateMod_modified\", \"sj2015_StateMod_modified\", \"StateMod\"\n", ")\n", "\n", "# directory to the target basin input files with root name for the basin\n", @@ -141,17 +138,9 @@ "parquet_dir_ddr = os.path.join(data_dir, \"parquet_ddr\")\n", "\n", "# path to ddm and ddr template file\n", - "ddm_template_file = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"sj2015B_template_ddm.rsp\"\n", - ")\n", + "ddm_template_file = os.path.join(home_dir, \"data\", \"sj2015B_template_ddm.rsp\")\n", "\n", - "ddr_template_file = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"sj2015B_template_ddr.rsp\"\n", - ")" + "ddr_template_file = os.path.join(home_dir, \"data\", \"sj2015B_template_ddr.rsp\")" ] }, { @@ -165,7 +154,7 @@ "outputs": [], "source": [ "# run statemod\n", - "subprocess.call([statemod_exe, basin_path, \"-simulate\"])\n" + "subprocess.call([statemod_exe, basin_path, \"-simulate\"])" ] }, { @@ -185,9 +174,9 @@ }, "outputs": [], "source": [ - "#Extract shortages using statemodify convert_xdd() function \n", + "# Extract shortages using statemodify convert_xdd() function\n", "\n", - "# create a directory to store the historical shortages \n", + "# create a directory to store the historical shortages\n", "output_dir = os.path.join(data_dir, \"historic_shortages\")\n", "\n", "# create a directory to store the new files in if it does not exist\n", @@ -203,10 +192,10 @@ " # path, glob, or a list of paths/globs to the .xdd files you want to convert\n", " xdd_files=os.path.join(data_dir, \"*.xdd\"),\n", " # if the output .parquet files should only contain a subset of structure ids, list them here; None for all\n", - " id_subset=['2900501','2900519','2900555'],\n", + " id_subset=[\"2900501\", \"2900519\", \"2900555\"],\n", " # how many .xdd files to convert in parallel; optimally you will want 2-4 CPUs per parallel process\n", " parallel_jobs=4,\n", - ")\n" + ")" ] }, { @@ -218,7 +207,7 @@ }, "outputs": [], "source": [ - "data=pd.read_parquet(os.path.join(output_dir,'sj2015B.parquet'),engine='pyarrow')\n", + "data = pd.read_parquet(os.path.join(output_dir, \"sj2015B.parquet\"), engine=\"pyarrow\")\n", "data" ] }, @@ -249,14 +238,13 @@ }, "outputs": [], "source": [ - "data['shortage_total']=data['shortage_total'].astype(float)\n", - "data['year']=data['year'].astype(int)\n", + "data[\"shortage_total\"] = data[\"shortage_total\"].astype(float)\n", + "data[\"year\"] = data[\"year\"].astype(int)\n", "\n", "fig, ax = plt.subplots()\n", "\n", - "for name, group in data.groupby('structure_id'):\n", - " ax.scatter(\n", - " group['year'], group['shortage_total'], label=name)\n", + "for name, group in data.groupby(\"structure_id\"):\n", + " ax.scatter(group[\"year\"], group[\"shortage_total\"], label=name)\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", @@ -301,10 +289,7 @@ "outputs": [], "source": [ "# a dictionary to describe what users you want to modify and the bounds for the LHS\n", - "setup_dict = {\n", - " \"ids\": [\"2900501\", \"2900519\",\"2900555\"],\n", - " \"bounds\": [0.5, 1.5]\n", - "}\n", + "setup_dict = {\"ids\": [\"2900501\", \"2900519\", \"2900555\"], \"bounds\": [0.5, 1.5]}\n", "\n", "output_directory = output_dir = os.path.join(data_dir, \"input_files\")\n", "\n", @@ -329,22 +314,24 @@ "basin_name = \"San_Juan\"\n", "\n", "# generate a batch of files using generated LHS\n", - "stm.modify_ddm(modify_dict=setup_dict,\n", - " query_field=query_field,\n", - " output_dir=output_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " sampling_method=\"LHS\",\n", - " n_samples=n_samples,\n", - " skip_rows=skip_rows,\n", - " n_jobs=n_jobs,\n", - " seed_value=seed_value,\n", - " template_file=None,\n", - " factor_method=\"multiply\",\n", - " data_specification_file=None,\n", - " min_bound_value=-0.5,\n", - " max_bound_value=1.5,\n", - " save_sample=True)" + "stm.modify_ddm(\n", + " modify_dict=setup_dict,\n", + " query_field=query_field,\n", + " output_dir=output_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " sampling_method=\"LHS\",\n", + " n_samples=n_samples,\n", + " skip_rows=skip_rows,\n", + " n_jobs=n_jobs,\n", + " seed_value=seed_value,\n", + " template_file=None,\n", + " factor_method=\"multiply\",\n", + " data_specification_file=None,\n", + " min_bound_value=-0.5,\n", + " max_bound_value=1.5,\n", + " save_sample=True,\n", + ")" ] }, { @@ -365,7 +352,8 @@ "outputs": [], "source": [ "import numpy as np\n", - "sample_array=np.load(output_directory+'/ddm_2-samples_scenario-1.npy')\n", + "\n", + "sample_array = np.load(output_directory + \"/ddm_2-samples_scenario-1.npy\")\n", "sample_array" ] }, @@ -405,51 +393,62 @@ "\n", "# read RSP template\n", "with open(ddm_template_file) as template_obj:\n", - " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", - " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - " \n", + "\n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"DDM\": f\"../../input_files/sj2015B_{scenario}.ddm\"}\n", - " \n", + "\n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - " \n", + "\n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir_ddm, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - " \n", + "\n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}.rsp\")\n", - " \n", + "\n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - " \n", + "\n", " # construct simulated basin path\n", - " simulated_basin_path = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}\")\n", + " simulated_basin_path = os.path.join(\n", + " simulated_scenario_dir, f\"sj2015B_{scenario}\"\n", + " )\n", "\n", " # run StateMod\n", " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - " \n", - " #Save output to parquet files \n", - " print('creating parquet for ' + scenario)\n", - " \n", - " output_directory = os.path.join(parquet_dir_ddm+\"/scenario/\"+ scenario)\n", - " \n", + "\n", + " # Save output to parquet files\n", + " print(\"creating parquet for \" + scenario)\n", + "\n", + " output_directory = os.path.join(parquet_dir_ddm + \"/scenario/\" + scenario)\n", + "\n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - " \n", - " stm.xdd.convert_xdd(output_path=output_directory,allow_overwrite=False,xdd_files=scenarios_dir_ddm + \"/\"+ scenario + \"/sj2015B_\"+scenario+\".xdd\",id_subset=['2900501','2900519','2900555'],parallel_jobs=4)" + "\n", + " stm.xdd.convert_xdd(\n", + " output_path=output_directory,\n", + " allow_overwrite=False,\n", + " xdd_files=scenarios_dir_ddm\n", + " + \"/\"\n", + " + scenario\n", + " + \"/sj2015B_\"\n", + " + scenario\n", + " + \".xdd\",\n", + " id_subset=[\"2900501\", \"2900519\", \"2900555\"],\n", + " parallel_jobs=4,\n", + " )" ] }, { @@ -482,7 +481,7 @@ "outputs": [], "source": [ "output_directory = os.path.join(data_dir, \"input_files\")\n", - "sample_array = np.load(output_directory+'/ddm_2-samples_scenario-1.npy')\n", + "sample_array = np.load(output_directory + \"/ddm_2-samples_scenario-1.npy\")\n", "sample_array" ] }, @@ -503,27 +502,41 @@ }, "outputs": [], "source": [ - "#Read in raw parquet files\n", - "baseline=pd.read_parquet(data_dir+'/historic_shortages/sj2015B.parquet',engine='pyarrow')\n", - "SOW_1=pd.read_parquet(parquet_dir_ddm+'/scenario/S0_1/sj2015B_S0_1.parquet',engine='pyarrow')\n", - "SOW_2=pd.read_parquet(parquet_dir_ddm+'/scenario/S1_1/sj2015B_S1_1.parquet',engine='pyarrow')\n", + "# Read in raw parquet files\n", + "baseline = pd.read_parquet(\n", + " data_dir + \"/historic_shortages/sj2015B.parquet\", engine=\"pyarrow\"\n", + ")\n", + "SOW_1 = pd.read_parquet(\n", + " parquet_dir_ddm + \"/scenario/S0_1/sj2015B_S0_1.parquet\", engine=\"pyarrow\"\n", + ")\n", + "SOW_2 = pd.read_parquet(\n", + " parquet_dir_ddm + \"/scenario/S1_1/sj2015B_S1_1.parquet\", engine=\"pyarrow\"\n", + ")\n", "\n", - "#Subtract shortages with respect to the baseline\n", - "subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total'],SOW_2['shortage_total']],axis=1)\n", - "subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1','SOW_2'], axis=1)\n", - "subset_df['Baseline']=subset_df['Baseline'].astype(float)\n", - "subset_df['SOW_1']=subset_df['SOW_1'].astype(float)\n", - "subset_df['SOW_2']=subset_df['SOW_2'].astype(float)\n", - "subset_df['Year']=subset_df['Year'].astype(int)\n", - "subset_df['SOW_1_diff']=subset_df['SOW_1']-subset_df['Baseline']\n", - "subset_df['SOW_2_diff']=subset_df['SOW_2']-subset_df['Baseline']\n", + "# Subtract shortages with respect to the baseline\n", + "subset_df = pd.concat(\n", + " [\n", + " baseline[\"year\"],\n", + " baseline[\"shortage_total\"],\n", + " SOW_1[\"shortage_total\"],\n", + " SOW_2[\"shortage_total\"],\n", + " ],\n", + " axis=1,\n", + ")\n", + "subset_df = subset_df.set_axis([\"Year\", \"Baseline\", \"SOW_1\", \"SOW_2\"], axis=1)\n", + "subset_df[\"Baseline\"] = subset_df[\"Baseline\"].astype(float)\n", + "subset_df[\"SOW_1\"] = subset_df[\"SOW_1\"].astype(float)\n", + "subset_df[\"SOW_2\"] = subset_df[\"SOW_2\"].astype(float)\n", + "subset_df[\"Year\"] = subset_df[\"Year\"].astype(int)\n", + "subset_df[\"SOW_1_diff\"] = subset_df[\"SOW_1\"] - subset_df[\"Baseline\"]\n", + "subset_df[\"SOW_2_diff\"] = subset_df[\"SOW_2\"] - subset_df[\"Baseline\"]\n", "\n", "\n", - "#Plot shortages\n", + "# Plot shortages\n", "fig, ax = plt.subplots()\n", "\n", - "ax.scatter(subset_df['Year'], subset_df['SOW_1_diff'],label='Decreased Demand')\n", - "ax.scatter(subset_df['Year'], subset_df['SOW_2_diff'],label='Increased Demand')\n", + "ax.scatter(subset_df[\"Year\"], subset_df[\"SOW_1_diff\"], label=\"Decreased Demand\")\n", + "ax.scatter(subset_df[\"Year\"], subset_df[\"SOW_2_diff\"], label=\"Increased Demand\")\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", @@ -568,11 +581,9 @@ "setup_dict = {\n", " # ids can either be 'struct' or 'id' values\n", " \"ids\": [\"2900501\"],\n", - "\n", " # turn id on or off completely or for a given period\n", " # if 0 = off, 1 = on, YYYY = on for years >= YYYY, -YYYY = off for years > YYYY; see file header\n", " \"on_off\": [1],\n", - "\n", " # apply rank of administrative order where 0 is lowest (senior) and n is highest (junior); None is no change\n", " \"admin\": [1],\n", "}\n", @@ -599,22 +610,24 @@ "basin_name = \"San_Juan\"\n", "\n", "# generate a batch of files using generated LHS\n", - "stm.modify_ddr(modify_dict=setup_dict,\n", - " query_field=query_field,\n", - " output_dir=output_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " sampling_method=\"LHS\",\n", - " n_samples=n_samples,\n", - " skip_rows=skip_rows,\n", - " n_jobs=n_jobs,\n", - " seed_value=seed_value,\n", - " template_file=None,\n", - " factor_method=\"multiply\",\n", - " data_specification_file=None,\n", - " min_bound_value=-0.5,\n", - " max_bound_value=1.5,\n", - " save_sample=True)" + "stm.modify_ddr(\n", + " modify_dict=setup_dict,\n", + " query_field=query_field,\n", + " output_dir=output_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " sampling_method=\"LHS\",\n", + " n_samples=n_samples,\n", + " skip_rows=skip_rows,\n", + " n_jobs=n_jobs,\n", + " seed_value=seed_value,\n", + " template_file=None,\n", + " factor_method=\"multiply\",\n", + " data_specification_file=None,\n", + " min_bound_value=-0.5,\n", + " max_bound_value=1.5,\n", + " save_sample=True,\n", + ")" ] }, { @@ -641,51 +654,62 @@ "\n", "# read RSP template\n", "with open(ddr_template_file) as template_obj:\n", - " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", - " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - " \n", + "\n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"DDR\": f\"../../input_files/sj2015B_{scenario}.ddr\"}\n", - " \n", + "\n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - " \n", + "\n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir_ddr, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - " \n", + "\n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}.rsp\")\n", - " \n", + "\n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - " \n", + "\n", " # construct simulated basin path\n", - " simulated_basin_path = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}\")\n", + " simulated_basin_path = os.path.join(\n", + " simulated_scenario_dir, f\"sj2015B_{scenario}\"\n", + " )\n", "\n", " # run StateMod\n", " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - " \n", - " #Save output to parquet files \n", - " print('creating parquet for ' + scenario)\n", - " \n", - " output_directory = os.path.join(parquet_dir_ddr+\"/scenario/\"+ scenario)\n", - " \n", + "\n", + " # Save output to parquet files\n", + " print(\"creating parquet for \" + scenario)\n", + "\n", + " output_directory = os.path.join(parquet_dir_ddr + \"/scenario/\" + scenario)\n", + "\n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - " \n", - " stm.xdd.convert_xdd(output_path=output_directory,allow_overwrite=False,xdd_files=scenarios_dir_ddr + \"/\"+ scenario + \"/sj2015B_\"+scenario+\".xdd\",id_subset=['2900501'],parallel_jobs=2)" + "\n", + " stm.xdd.convert_xdd(\n", + " output_path=output_directory,\n", + " allow_overwrite=False,\n", + " xdd_files=scenarios_dir_ddr\n", + " + \"/\"\n", + " + scenario\n", + " + \"/sj2015B_\"\n", + " + scenario\n", + " + \".xdd\",\n", + " id_subset=[\"2900501\"],\n", + " parallel_jobs=2,\n", + " )" ] }, { @@ -705,22 +729,28 @@ }, "outputs": [], "source": [ - "#Read in raw parquet files\n", - "baseline=pd.read_parquet(data_dir+'/historic_shortages/sj2015B.parquet',engine='pyarrow')\n", - "SOW_1=pd.read_parquet(parquet_dir_ddr+ '/scenario/S0_1/sj2015B_S0_1.parquet',engine='pyarrow')\n", + "# Read in raw parquet files\n", + "baseline = pd.read_parquet(\n", + " data_dir + \"/historic_shortages/sj2015B.parquet\", engine=\"pyarrow\"\n", + ")\n", + "SOW_1 = pd.read_parquet(\n", + " parquet_dir_ddr + \"/scenario/S0_1/sj2015B_S0_1.parquet\", engine=\"pyarrow\"\n", + ")\n", "\n", - "#Subtract shortages with respect to the baseline\n", - "subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total']],axis=1)\n", - "subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1'], axis=1)\n", - "subset_df['Baseline']=subset_df['Baseline'].astype(float)\n", - "subset_df['SOW_1']=subset_df['SOW_1'].astype(float)\n", - "subset_df['Year']=subset_df['Year'].astype(int)\n", - "subset_df['diff']=subset_df['SOW_1']-subset_df['Baseline']\n", + "# Subtract shortages with respect to the baseline\n", + "subset_df = pd.concat(\n", + " [baseline[\"year\"], baseline[\"shortage_total\"], SOW_1[\"shortage_total\"]], axis=1\n", + ")\n", + "subset_df = subset_df.set_axis([\"Year\", \"Baseline\", \"SOW_1\"], axis=1)\n", + "subset_df[\"Baseline\"] = subset_df[\"Baseline\"].astype(float)\n", + "subset_df[\"SOW_1\"] = subset_df[\"SOW_1\"].astype(float)\n", + "subset_df[\"Year\"] = subset_df[\"Year\"].astype(int)\n", + "subset_df[\"diff\"] = subset_df[\"SOW_1\"] - subset_df[\"Baseline\"]\n", "\n", - "#Plot shortages\n", + "# Plot shortages\n", "fig, ax = plt.subplots()\n", "\n", - "ax.scatter(subset_df['Year'], subset_df['diff'])\n", + "ax.scatter(subset_df[\"Year\"], subset_df[\"diff\"])\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", diff --git a/notebooks/N2_Evaporation_File_Modification.ipynb b/notebooks/N2_Evaporation_File_Modification.ipynb index 4b39ff8..924ee6a 100644 --- a/notebooks/N2_Evaporation_File_Modification.ipynb +++ b/notebooks/N2_Evaporation_File_Modification.ipynb @@ -47,12 +47,13 @@ "import logging\n", "import os\n", "import pickle\n", - "from string import Template\n", "import subprocess\n", + "from string import Template\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd \n", + "import pandas as pd\n", + "\n", "import statemodify as stm" ] }, @@ -96,11 +97,7 @@ "\n", "# data directory and root name for the target basin\n", "data_dir = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"gm2015_StateMod_modified\",\n", - " \"gm2015_StateMod_modified\",\n", - " \"StateMod\"\n", + " home_dir, \"data\", \"gm2015_StateMod_modified\", \"gm2015_StateMod_modified\", \"StateMod\"\n", ")\n", "\n", "# directory to the target basin input files with root name for the basin\n", @@ -110,11 +107,7 @@ "scenarios_dir = os.path.join(data_dir, \"scenarios\")\n", "\n", "# path to eva template file\n", - "eva_template_file = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"gm2015B_template_eva.rsp\"\n", - ")" + "eva_template_file = os.path.join(home_dir, \"data\", \"gm2015B_template_eva.rsp\")" ] }, { @@ -128,7 +121,7 @@ "outputs": [], "source": [ "# run statemod\n", - "subprocess.call([statemod_exe, basin_path, \"-simulate\"])\n" + "subprocess.call([statemod_exe, basin_path, \"-simulate\"])" ] }, { @@ -148,7 +141,7 @@ }, "outputs": [], "source": [ - "# create a directory to store the historical reservoir levels at Blue Mesa \n", + "# create a directory to store the historical reservoir levels at Blue Mesa\n", "output_dir = os.path.join(data_dir, \"historic_reservoir_levels\")\n", "\n", "if not os.path.exists(output_dir):\n", @@ -158,19 +151,20 @@ "xre_file = os.path.join(data_dir, \"gm2015B.xre\")\n", "\n", "# structure ID for reservoir of interest\n", - "structure_ID = '6203532' \n", + "structure_ID = \"6203532\"\n", "\n", "# name of the reservoir\n", - "structure_name = 'Blue_Mesa' \n", + "structure_name = \"Blue_Mesa\"\n", "\n", "# extract the target info into a Pandas data frame\n", - "df = stm.extract_xre_data(structure_name=structure_name,\n", - " structure_id=structure_ID,\n", - " input_file=xre_file,\n", - " basin_name=None,\n", - " output_directory=output_dir,\n", - " write_csv=True,\n", - " write_parquet=None\n", + "df = stm.extract_xre_data(\n", + " structure_name=structure_name,\n", + " structure_id=structure_ID,\n", + " input_file=xre_file,\n", + " basin_name=None,\n", + " output_directory=output_dir,\n", + " write_csv=True,\n", + " write_parquet=None,\n", ")" ] }, @@ -194,15 +188,12 @@ "output_xre_file = os.path.join(output_dir, \"Blue_Mesa_xre_data.csv\")\n", "\n", "# read output data into a data frame\n", - "df = pd.read_csv(\n", - " output_xre_file, \n", - " usecols=['Year','Init. Storage'],\n", - " index_col=False) \n", + "df = pd.read_csv(output_xre_file, usecols=[\"Year\", \"Init. Storage\"], index_col=False)\n", "\n", "# calculate the annual average\n", - "df = df.groupby('Year').mean().reset_index()\n", + "df = df.groupby(\"Year\").mean().reset_index()\n", "\n", - "df\n" + "df" ] }, { @@ -224,11 +215,11 @@ "source": [ "fig, ax = plt.subplots()\n", "\n", - "plt.plot(df['Year'], df['Init. Storage'])\n", + "plt.plot(df[\"Year\"], df[\"Init. Storage\"])\n", "\n", "plt.title(\"Blue Mesa Storage\")\n", "plt.xlabel(\"Year\")\n", - "plt.ylabel(\"Reservoir Storage (AF)\")\n" + "plt.ylabel(\"Reservoir Storage (AF)\")" ] }, { @@ -256,11 +247,8 @@ }, "outputs": [], "source": [ - "# a dictionary to describe what you want to modify and the bounds for the Latin hypercube sample. \n", - "setup_dict = {\n", - " \"ids\": ['10011'],\n", - " \"bounds\": [-0.5, 1.0]\n", - "}\n", + "# a dictionary to describe what you want to modify and the bounds for the Latin hypercube sample.\n", + "setup_dict = {\"ids\": [\"10011\"], \"bounds\": [-0.5, 1.0]}\n", "\n", "# create a directory to store the new files in if it does not exist\n", "output_directory = os.path.join(data_dir, \"input_files\")\n", @@ -289,22 +277,24 @@ "basin_name = \"Gunnison\"\n", "\n", "# generate a batch of files using generated LHS\n", - "stm.modify_eva(modify_dict=setup_dict,\n", - " query_field=query_field,\n", - " output_dir=output_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " sampling_method=\"LHS\",\n", - " n_samples=n_samples,\n", - " skip_rows=skip_rows,\n", - " n_jobs=n_jobs,\n", - " seed_value=seed_value,\n", - " template_file=None,\n", - " factor_method=\"add\",\n", - " data_specification_file=None,\n", - " min_bound_value=-0.5,\n", - " max_bound_value=1.0,\n", - " save_sample=True)\n" + "stm.modify_eva(\n", + " modify_dict=setup_dict,\n", + " query_field=query_field,\n", + " output_dir=output_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " sampling_method=\"LHS\",\n", + " n_samples=n_samples,\n", + " skip_rows=skip_rows,\n", + " n_jobs=n_jobs,\n", + " seed_value=seed_value,\n", + " template_file=None,\n", + " factor_method=\"add\",\n", + " data_specification_file=None,\n", + " min_bound_value=-0.5,\n", + " max_bound_value=1.0,\n", + " save_sample=True,\n", + ")" ] }, { @@ -327,10 +317,10 @@ "# path to the numpy file containing the samples\n", "eva_samples_file = os.path.join(output_directory, \"eva_2-samples_scenario-1.npy\")\n", "\n", - "# load samples \n", + "# load samples\n", "sample_array = np.load(eva_samples_file)\n", "\n", - "sample_array\n" + "sample_array" ] }, { @@ -365,41 +355,41 @@ "\n", "# read RSP template\n", "with open(eva_template_file) as template_obj:\n", - " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", - " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - " \n", + "\n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"EVA\": f\"../../input_files/gm2015B_{scenario}.eva\"}\n", - " \n", + "\n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - " \n", + "\n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - " \n", + "\n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"gm2015B_{scenario}.rsp\")\n", - " \n", + "\n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - " \n", + "\n", " # construct simulated basin path\n", - " simulated_basin_path = os.path.join(simulated_scenario_dir, f\"gm2015B_{scenario}\")\n", + " simulated_basin_path = os.path.join(\n", + " simulated_scenario_dir, f\"gm2015B_{scenario}\"\n", + " )\n", "\n", " # run StateMod\n", " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", - " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n" + " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])" ] }, { @@ -428,43 +418,45 @@ "outputs": [], "source": [ "# SOW 1\n", - "output_dir= os.path.join(scenarios_dir, \"S0_1\")\n", + "output_dir = os.path.join(scenarios_dir, \"S0_1\")\n", "\n", "# path the the xre file\n", - "xre_file = os.path.join(output_dir, \"gm2015B_S0_1.xre\") \n", + "xre_file = os.path.join(output_dir, \"gm2015B_S0_1.xre\")\n", "\n", "# structure ID for reservoir of interest\n", - "structure_ID = '6203532' \n", + "structure_ID = \"6203532\"\n", "\n", "# name of the reservoir\n", - "structure_name = 'Blue_Mesa' \n", + "structure_name = \"Blue_Mesa\"\n", "\n", "# extract the target info into a Pandas data frame\n", - "df = stm.extract_xre_data(structure_name=structure_name,\n", - " structure_id=structure_ID,\n", - " input_file=xre_file,\n", - " basin_name=None,\n", - " output_directory=output_dir,\n", - " write_csv=True,\n", - " write_parquet=None\n", + "df = stm.extract_xre_data(\n", + " structure_name=structure_name,\n", + " structure_id=structure_ID,\n", + " input_file=xre_file,\n", + " basin_name=None,\n", + " output_directory=output_dir,\n", + " write_csv=True,\n", + " write_parquet=None,\n", ")\n", "\n", "# SOW 2\n", - "output_dir= os.path.join(scenarios_dir, \"S1_1\")\n", + "output_dir = os.path.join(scenarios_dir, \"S1_1\")\n", "\n", "# path the the xre file\n", - "xre_file = os.path.join(output_dir, \"gm2015B_S1_1.xre\") \n", + "xre_file = os.path.join(output_dir, \"gm2015B_S1_1.xre\")\n", "\n", "\n", "# extract the target info into a Pandas data frame\n", - "df = stm.extract_xre_data(structure_name=structure_name,\n", - " structure_id=structure_ID,\n", - " input_file=xre_file,\n", - " basin_name=None,\n", - " output_directory=output_dir,\n", - " write_csv=True,\n", - " write_parquet=None\n", - ")\n" + "df = stm.extract_xre_data(\n", + " structure_name=structure_name,\n", + " structure_id=structure_ID,\n", + " input_file=xre_file,\n", + " basin_name=None,\n", + " output_directory=output_dir,\n", + " write_csv=True,\n", + " write_parquet=None,\n", + ")" ] }, { @@ -488,32 +480,34 @@ "historic_res_dir = os.path.join(data_dir, \"historic_reservoir_levels\")\n", "blue_mesa_file = os.path.join(historic_res_dir, \"Blue_Mesa_xre_data.csv\")\n", "\n", - "# Import baseline dataframe \n", - "baseline = pd.read_csv(blue_mesa_file, index_col=False, usecols=['Year','Init. Storage']) \n", - "baseline = baseline.groupby('Year').mean().reset_index()\n", + "# Import baseline dataframe\n", + "baseline = pd.read_csv(\n", + " blue_mesa_file, index_col=False, usecols=[\"Year\", \"Init. Storage\"]\n", + ")\n", + "baseline = baseline.groupby(\"Year\").mean().reset_index()\n", "\n", "# Import SOW1\n", "s0_1_file = os.path.join(scenarios_dir, \"S0_1\", \"Blue_Mesa_xre_data.csv\")\n", - "SOW1 = pd.read_csv(s0_1_file, index_col=False, usecols=['Year','Init. Storage']) \n", - "SOW1 = SOW1.groupby('Year').mean().reset_index()\n", - " \n", + "SOW1 = pd.read_csv(s0_1_file, index_col=False, usecols=[\"Year\", \"Init. Storage\"])\n", + "SOW1 = SOW1.groupby(\"Year\").mean().reset_index()\n", + "\n", "# Import SOW2\n", "s1_1_file = os.path.join(scenarios_dir, \"S1_1\", \"Blue_Mesa_xre_data.csv\")\n", - "SOW2 = pd.read_csv(s1_1_file, index_col=False, usecols=['Year','Init. Storage']) \n", - "SOW2 = SOW2.groupby('Year').mean().reset_index()\n", - " \n", - "# Plot reservoir levels \n", + "SOW2 = pd.read_csv(s1_1_file, index_col=False, usecols=[\"Year\", \"Init. Storage\"])\n", + "SOW2 = SOW2.groupby(\"Year\").mean().reset_index()\n", + "\n", + "# Plot reservoir levels\n", "fig, ax = plt.subplots()\n", "\n", - "plt.plot(baseline['Year'], baseline['Init. Storage'],label='Baseline')\n", - "plt.plot(SOW1['Year'], SOW1['Init. Storage'],label='Reduced Evaporation')\n", - "plt.plot(SOW2['Year'], SOW2['Init. Storage'],label='Increased Evaporation')\n", + "plt.plot(baseline[\"Year\"], baseline[\"Init. Storage\"], label=\"Baseline\")\n", + "plt.plot(SOW1[\"Year\"], SOW1[\"Init. Storage\"], label=\"Reduced Evaporation\")\n", + "plt.plot(SOW2[\"Year\"], SOW2[\"Init. Storage\"], label=\"Increased Evaporation\")\n", "\n", "plt.title(\"Blue Mesa Storage\")\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Reservoir Storage (AF)\")\n", "\n", - "plt.legend()\n" + "plt.legend()" ] }, { diff --git a/notebooks/N3_Reservoir_File_Modification.ipynb b/notebooks/N3_Reservoir_File_Modification.ipynb index dbca4e1..2798178 100644 --- a/notebooks/N3_Reservoir_File_Modification.ipynb +++ b/notebooks/N3_Reservoir_File_Modification.ipynb @@ -37,12 +37,13 @@ "import logging\n", "import os\n", "import pickle\n", - "from string import Template\n", "import subprocess\n", + "from string import Template\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd \n", + "import pandas as pd\n", + "\n", "import statemodify as stm" ] }, @@ -88,12 +89,7 @@ "statemod_exe = os.path.join(root_dir, \"statemod\")\n", "\n", "# data directory and root name for the target basin\n", - "data_dir = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"cm2015_StateMod\",\n", - " \"StateMod\"\n", - ")\n", + "data_dir = os.path.join(home_dir, \"data\", \"cm2015_StateMod\", \"StateMod\")\n", "\n", "# directory to the target basin input files with root name for the basin\n", "basin_path = os.path.join(data_dir, \"cm2015B\")\n", @@ -106,11 +102,7 @@ "\n", "\n", "# path to res template file\n", - "res_template_file = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"cm2015B_template_res.rsp\"\n", - ")" + "res_template_file = os.path.join(home_dir, \"data\", \"cm2015B_template_res.rsp\")" ] }, { @@ -135,9 +127,11 @@ }, "outputs": [], "source": [ - "# Change directories first \n", - "os.chdir(data_dir) #This is needed specific to the Upper Colorado model as the path name is too long for the model to accept\n", - "subprocess.call([statemod_exe, \"cm2015B\", \"-simulate\"])\n" + "# Change directories first\n", + "os.chdir(\n", + " data_dir\n", + ") # This is needed specific to the Upper Colorado model as the path name is too long for the model to accept\n", + "subprocess.call([statemod_exe, \"cm2015B\", \"-simulate\"])" ] }, { @@ -159,9 +153,9 @@ }, "outputs": [], "source": [ - "#Extract shortages using statemodify convert_xdd() function \n", + "# Extract shortages using statemodify convert_xdd() function\n", "\n", - "# create a directory to store the historic shortages \n", + "# create a directory to store the historic shortages\n", "output_dir = os.path.join(data_dir, \"historic_shortages\")\n", "\n", "# create a directory to store the new files in if it does not exist\n", @@ -177,7 +171,7 @@ " # path, glob, or a list of paths/globs to the .xdd files you want to convert\n", " xdd_files=os.path.join(data_dir, \"*.xdd\"),\n", " # if the output .parquet files should only contain a subset of structure ids, list them here; None for all\n", - " id_subset=['3601008'],\n", + " id_subset=[\"3601008\"],\n", " # how many .xdd files to convert in parallel; optimally you will want 2-4 CPUs per parallel process\n", " parallel_jobs=2,\n", ")" @@ -200,17 +194,16 @@ }, "outputs": [], "source": [ - "data=pd.read_parquet(output_dir +'/cm2015B.parquet',engine='pyarrow')\n", + "data = pd.read_parquet(output_dir + \"/cm2015B.parquet\", engine=\"pyarrow\")\n", "\n", "\n", - "data['shortage_total']=data['shortage_total'].astype(float)\n", - "data['year']=data['year'].astype(int)\n", + "data[\"shortage_total\"] = data[\"shortage_total\"].astype(float)\n", + "data[\"year\"] = data[\"year\"].astype(int)\n", "\n", "fig, ax = plt.subplots()\n", "\n", - "for name, group in data.groupby('structure_id'):\n", - " ax.scatter(\n", - " group['year'], group['shortage_total'], label=name)\n", + "for name, group in data.groupby(\"structure_id\"):\n", + " ax.scatter(group[\"year\"], group[\"shortage_total\"], label=name)\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", @@ -265,14 +258,16 @@ "# number of samples to generate\n", "n_samples = 1\n", "\n", - "stm.modify_res(output_dir=output_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " target_structure_id_list=['3603575','3604512'],\n", - " seed_value=seed_value,\n", - " n_jobs=n_jobs,\n", - " n_samples=n_samples,\n", - " save_sample=True)\n" + "stm.modify_res(\n", + " output_dir=output_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " target_structure_id_list=[\"3603575\", \"3604512\"],\n", + " seed_value=seed_value,\n", + " n_jobs=n_jobs,\n", + " n_samples=n_samples,\n", + " save_sample=True,\n", + ")" ] }, { @@ -293,7 +288,8 @@ "outputs": [], "source": [ "import numpy as np\n", - "sample_array = np.load(output_directory+'/res_1-samples_scenario-1.npy')\n", + "\n", + "sample_array = np.load(output_directory + \"/res_1-samples_scenario-1.npy\")\n", "sample_array" ] }, @@ -329,33 +325,31 @@ "\n", "# read RSP template\n", "with open(res_template_file) as template_obj:\n", - " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", - " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - " \n", + "\n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"RES\": f\"../../input_files/cm2015B_{scenario}.res\"}\n", - " \n", + "\n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - " \n", + "\n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir_res, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - " \n", + "\n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"cm2015B_{scenario}.rsp\")\n", - " \n", + "\n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - " \n", + "\n", " # construct simulated basin path\n", " simulated_basin_path = f\"cm2015B_{scenario}\"\n", "\n", @@ -364,16 +358,27 @@ " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - " \n", - " #Save output to parquet files \n", - " print('creating parquet for ' + scenario)\n", - " \n", - " output_directory = os.path.join(parquet_dir_res+\"/scenario/\"+ scenario)\n", - " \n", + "\n", + " # Save output to parquet files\n", + " print(\"creating parquet for \" + scenario)\n", + "\n", + " output_directory = os.path.join(parquet_dir_res + \"/scenario/\" + scenario)\n", + "\n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - " \n", - " stm.xdd.convert_xdd(output_path=output_directory,allow_overwrite=True,xdd_files=scenarios_dir_res + \"/\"+ scenario + \"/cm2015B_\"+scenario+\".xdd\",id_subset=['3601008'],parallel_jobs=2)" + "\n", + " stm.xdd.convert_xdd(\n", + " output_path=output_directory,\n", + " allow_overwrite=True,\n", + " xdd_files=scenarios_dir_res\n", + " + \"/\"\n", + " + scenario\n", + " + \"/cm2015B_\"\n", + " + scenario\n", + " + \".xdd\",\n", + " id_subset=[\"3601008\"],\n", + " parallel_jobs=2,\n", + " )" ] }, { @@ -393,21 +398,27 @@ }, "outputs": [], "source": [ - "baseline=pd.read_parquet(data_dir+'/'+'historic_shortages/cm2015B.parquet',engine='pyarrow')\n", - "SOW_1=pd.read_parquet(parquet_dir_res+'/scenario/S0_1/cm2015B_S0_1.parquet',engine='pyarrow')\n", - "\n", - "#Subtract shortages with respect to the baseline\n", - "subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total']],axis=1)\n", - "subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1'], axis=1)\n", - "subset_df['Baseline']=subset_df['Baseline'].astype(float)\n", - "subset_df['SOW_1']=subset_df['SOW_1'].astype(float)\n", - "subset_df['Year']=subset_df['Year'].astype(int)\n", - "subset_df['SOW_1_diff']=subset_df['SOW_1']-subset_df['Baseline']\n", - "\n", - "#Plot shortages\n", + "baseline = pd.read_parquet(\n", + " data_dir + \"/\" + \"historic_shortages/cm2015B.parquet\", engine=\"pyarrow\"\n", + ")\n", + "SOW_1 = pd.read_parquet(\n", + " parquet_dir_res + \"/scenario/S0_1/cm2015B_S0_1.parquet\", engine=\"pyarrow\"\n", + ")\n", + "\n", + "# Subtract shortages with respect to the baseline\n", + "subset_df = pd.concat(\n", + " [baseline[\"year\"], baseline[\"shortage_total\"], SOW_1[\"shortage_total\"]], axis=1\n", + ")\n", + "subset_df = subset_df.set_axis([\"Year\", \"Baseline\", \"SOW_1\"], axis=1)\n", + "subset_df[\"Baseline\"] = subset_df[\"Baseline\"].astype(float)\n", + "subset_df[\"SOW_1\"] = subset_df[\"SOW_1\"].astype(float)\n", + "subset_df[\"Year\"] = subset_df[\"Year\"].astype(int)\n", + "subset_df[\"SOW_1_diff\"] = subset_df[\"SOW_1\"] - subset_df[\"Baseline\"]\n", + "\n", + "# Plot shortages\n", "fig, ax = plt.subplots()\n", "\n", - "ax.scatter(subset_df['Year'], subset_df['SOW_1_diff'])\n", + "ax.scatter(subset_df[\"Year\"], subset_df[\"SOW_1_diff\"])\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", diff --git a/notebooks/N4_Streamflow_File_Modification.ipynb b/notebooks/N4_Streamflow_File_Modification.ipynb index b341b94..5aecfd7 100644 --- a/notebooks/N4_Streamflow_File_Modification.ipynb +++ b/notebooks/N4_Streamflow_File_Modification.ipynb @@ -67,12 +67,13 @@ "import logging\n", "import os\n", "import pickle\n", - "from string import Template\n", "import subprocess\n", + "from string import Template\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd \n", + "import pandas as pd\n", + "\n", "import statemodify as stm" ] }, @@ -106,12 +107,7 @@ "statemod_exe = os.path.join(root_dir, \"statemod\")\n", "\n", "# data directory and root name for the target basin\n", - "data_dir = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"cm2015_StateMod\",\n", - " \"StateMod\"\n", - ")\n", + "data_dir = os.path.join(home_dir, \"data\", \"cm2015_StateMod\", \"StateMod\")\n", "\n", "# directory to the target basin input files with root name for the basin\n", "basin_path = os.path.join(data_dir, \"cm2015B\")\n", @@ -119,12 +115,8 @@ "# scenarios output directory\n", "scenarios_dir = os.path.join(data_dir, \"scenarios\")\n", "\n", - "# path to iwr/xbm file \n", - "xbm_iwr_template_file = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"cm2015B_template_xbm_iwr.rsp\"\n", - ")" + "# path to iwr/xbm file\n", + "xbm_iwr_template_file = os.path.join(home_dir, \"data\", \"cm2015B_template_xbm_iwr.rsp\")" ] }, { @@ -144,7 +136,7 @@ }, "outputs": [], "source": [ - "#Make directory to store HMM parameters\n", + "# Make directory to store HMM parameters\n", "\n", "output_dir = os.path.join(data_dir, \"HMM_parameters\")\n", "\n", @@ -156,9 +148,9 @@ "# choice to save parameters to NumPy array files\n", "save_parameters = True\n", "\n", - "fit_array_dict = stm.hmm_multisite_fit(n_basins=n_basins,\n", - " save_parameters=save_parameters,\n", - " output_directory=output_dir)\n", + "fit_array_dict = stm.hmm_multisite_fit(\n", + " n_basins=n_basins, save_parameters=save_parameters, output_directory=output_dir\n", + ")\n", "\n", "# unpack output dictionary\n", "unconditional_dry = fit_array_dict[\"unconditional_dry\"]\n", @@ -168,7 +160,7 @@ "covariance_matrix_wet = fit_array_dict[\"covariance_matrix_wet\"]\n", "covariance_matrix_dry = fit_array_dict[\"covariance_matrix_dry\"]\n", "wet_state_means = fit_array_dict[\"wet_state_means\"]\n", - "dry_state_means = fit_array_dict[\"dry_state_means\"]\n" + "dry_state_means = fit_array_dict[\"dry_state_means\"]" ] }, { @@ -196,7 +188,7 @@ }, "outputs": [], "source": [ - "#Create a folder to store the runs\n", + "# Create a folder to store the runs\n", "\n", "output_dir = os.path.join(data_dir, \"HMM_Runs\")\n", "\n", @@ -204,15 +196,17 @@ " os.makedirs(output_dir)\n", "\n", "# using the outputs of the fit function above; this function write output sample files to the output directory\n", - "stm.hmm_multisite_sample(logAnnualQ_h,\n", - " transition_matrix,\n", - " unconditional_dry,\n", - " dry_state_means,\n", - " wet_state_means,\n", - " covariance_matrix_dry,\n", - " covariance_matrix_wet,\n", - " n_basins=n_basins,\n", - " output_directory=output_dir)" + "stm.hmm_multisite_sample(\n", + " logAnnualQ_h,\n", + " transition_matrix,\n", + " unconditional_dry,\n", + " dry_state_means,\n", + " wet_state_means,\n", + " covariance_matrix_dry,\n", + " covariance_matrix_wet,\n", + " n_basins=n_basins,\n", + " output_directory=output_dir,\n", + ")" ] }, { @@ -232,10 +226,13 @@ }, "outputs": [], "source": [ - "stm.plot_flow_duration_curves(flow_realizations_directory=output_dir,\n", - " save_figure=True,output_directory=output_dir,\n", - " figure_name= 'FDC',\n", - " dpi= 300)" + "stm.plot_flow_duration_curves(\n", + " flow_realizations_directory=output_dir,\n", + " save_figure=True,\n", + " output_directory=output_dir,\n", + " figure_name=\"FDC\",\n", + " dpi=300,\n", + ")" ] }, { @@ -265,16 +262,16 @@ }, "outputs": [], "source": [ - "#Make directory to store input files \n", + "# Make directory to store input files\n", "\n", "output_dir = os.path.join(data_dir, \"input_files\")\n", "\n", "if not os.path.exists(output_dir):\n", " os.makedirs(output_dir)\n", - " \n", + "\n", "\n", "flow_realizations_directory = os.path.join(data_dir, \"HMM_Runs\")\n", - " \n", + "\n", "scenario = \"1\"\n", "\n", "# basin name to process\n", @@ -290,15 +287,17 @@ "n_samples = 1\n", "\n", "# generate a batch of files using generated LHS\n", - "stm.modify_xbm_iwr(output_dir=output_dir,\n", - " flow_realizations_directory=flow_realizations_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " seed_value=seed_value,\n", - " n_jobs=n_jobs,\n", - " n_samples=n_samples,\n", - " save_sample=True,\n", - " randomly_select_flow_sample=True) " + "stm.modify_xbm_iwr(\n", + " output_dir=output_dir,\n", + " flow_realizations_directory=flow_realizations_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " seed_value=seed_value,\n", + " n_jobs=n_jobs,\n", + " n_samples=n_samples,\n", + " save_sample=True,\n", + " randomly_select_flow_sample=True,\n", + ")" ] }, { @@ -347,33 +346,34 @@ "\n", "# read RSP template\n", "with open(xbm_iwr_template_file) as template_obj:\n", - " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", - " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - " \n", + "\n", " # dictionary holding search keys and replacement values to update the template file\n", - " d = {\"XBM\": f\"../../input_files/cm2015B_{scenario}.xbm\",\"IWR\": f\"../../input_files/cm2015B_{scenario}.iwr\"}\n", - " \n", + " d = {\n", + " \"XBM\": f\"../../input_files/cm2015B_{scenario}.xbm\",\n", + " \"IWR\": f\"../../input_files/cm2015B_{scenario}.iwr\",\n", + " }\n", + "\n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - " \n", + "\n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - " \n", + "\n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"cm2015B_{scenario}.rsp\")\n", - " \n", + "\n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - " \n", + "\n", " # construct simulated basin path\n", " simulated_basin_path = f\"cm2015B_{scenario}\"\n", "\n", @@ -381,7 +381,7 @@ " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", - " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n" + " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])" ] }, { @@ -402,14 +402,16 @@ "outputs": [], "source": [ "# Example with Granby Lake\n", - "zip_file_path = os.path.join(home_dir, 'data', 'Granby_Dataset.zip')\n", - "final_directory = os.path.join(home_dir, 'data/')\n", + "zip_file_path = os.path.join(home_dir, \"data\", \"Granby_Dataset.zip\")\n", + "final_directory = os.path.join(home_dir, \"data/\")\n", "\n", - "!unzip $zip_file_path -d $final_directory \n", - "granby_hmm, granby_hist, granby_hist_mean, granby_hist_1p = stm.read_xre(os.path.join(home_dir,\"data/Upper_Colorado/\"), 'Granby')\n", + "!unzip $zip_file_path -d $final_directory\n", + "granby_hmm, granby_hist, granby_hist_mean, granby_hist_1p = stm.read_xre(\n", + " os.path.join(home_dir, \"data/Upper_Colorado/\"), \"Granby\"\n", + ")\n", "\n", "# Plot quantiles\n", - "stm.plot_res_quantiles(granby_hmm, granby_hist_mean, 'Lake Granby')" + "stm.plot_res_quantiles(granby_hmm, granby_hist_mean, \"Lake Granby\")" ] }, { @@ -438,7 +440,7 @@ "outputs": [], "source": [ "# using the output of the above `read_xre` function as inputs\n", - "stm.plot_reservoir_boxes(granby_hmm, granby_hist, 'Lake Granby')" + "stm.plot_reservoir_boxes(granby_hmm, granby_hist, \"Lake Granby\")" ] }, { diff --git a/notebooks/N5_Batch_Modification.ipynb b/notebooks/N5_Batch_Modification.ipynb index 9099c0a..0bec3e9 100644 --- a/notebooks/N5_Batch_Modification.ipynb +++ b/notebooks/N5_Batch_Modification.ipynb @@ -33,12 +33,13 @@ "import logging\n", "import os\n", "import pickle\n", - "from string import Template\n", "import subprocess\n", + "from string import Template\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd \n", + "import pandas as pd\n", + "\n", "import statemodify as stm" ] }, @@ -80,12 +81,7 @@ "statemod_exe = os.path.join(root_dir, \"statemod\")\n", "\n", "# data directory and root name for the target basin\n", - "data_dir = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"cm2015_StateMod\",\n", - " \"StateMod\"\n", - ")\n", + "data_dir = os.path.join(home_dir, \"data\", \"cm2015_StateMod\", \"StateMod\")\n", "\n", "# directory to the target basin input files with root name for the basin\n", "basin_path = os.path.join(data_dir, \"cm2015B\")\n", @@ -93,16 +89,12 @@ "# scenarios output directory\n", "scenarios_dir = os.path.join(data_dir, \"scenarios\")\n", "\n", - "#parquet output directory\n", - "parquet_dir=os.path.join(data_dir, \"parquet\")\n", + "# parquet output directory\n", + "parquet_dir = os.path.join(data_dir, \"parquet\")\n", "\n", "\n", "# path to template file\n", - "multi_template_file = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"cm2015B_template_multi.rsp\"\n", - ")" + "multi_template_file = os.path.join(home_dir, \"data\", \"cm2015B_template_multi.rsp\")" ] }, { @@ -169,13 +161,9 @@ "# problem dictionary\n", "problem_dict = {\n", " \"n_samples\": 1,\n", - " 'num_vars': 3,\n", - " 'names': ['modify_eva', 'modify_ddr', 'modify_ddm'],\n", - " 'bounds': [\n", - " [-0.5, 1.0],\n", - " None,\n", - " [0.5, 1.0]\n", - " ],\n", + " \"num_vars\": 3,\n", + " \"names\": [\"modify_eva\", \"modify_ddr\", \"modify_ddm\"],\n", + " \"bounds\": [[-0.5, 1.0], None, [0.5, 1.0]],\n", " # additional settings for each function\n", " \"modify_eva\": {\n", " \"seed_value\": seed_value,\n", @@ -183,7 +171,7 @@ " \"scenario\": scenario,\n", " \"basin_name\": basin_name,\n", " \"query_field\": \"id\",\n", - " \"ids\": [\"10008\", \"10009\"]\n", + " \"ids\": [\"10008\", \"10009\"],\n", " },\n", " \"modify_ddr\": {\n", " \"seed_value\": seed_value,\n", @@ -193,7 +181,7 @@ " \"query_field\": \"id\",\n", " \"ids\": [\"3600507.01\", \"3600507.02\"],\n", " \"admin\": [1, None],\n", - " \"on_off\": [1, 1]\n", + " \"on_off\": [1, 1],\n", " },\n", " \"modify_ddm\": {\n", " \"seed_value\": seed_value,\n", @@ -201,8 +189,8 @@ " \"scenario\": scenario,\n", " \"basin_name\": basin_name,\n", " \"query_field\": \"id\",\n", - " \"ids\": [\"3600507\", \"3600603\"]\n", - " }\n", + " \"ids\": [\"3600507\", \"3600603\"],\n", + " },\n", "}\n", "\n", "# run in batch\n", @@ -3028,33 +3016,35 @@ "\n", "# read RSP template\n", "with open(multi_template_file) as template_obj:\n", - " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", - " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - " \n", + "\n", " # dictionary holding search keys and replacement values to update the template file\n", - " d = {\"EVA\": f\"../../input_files/cm2015B_{scenario}.eva\",\"DDM\": f\"../../input_files/cm2015B_{scenario}.ddm\",\"DDR\": f\"../../input_files/cm2015B_{scenario}.ddr\"}\n", - " \n", + " d = {\n", + " \"EVA\": f\"../../input_files/cm2015B_{scenario}.eva\",\n", + " \"DDM\": f\"../../input_files/cm2015B_{scenario}.ddm\",\n", + " \"DDR\": f\"../../input_files/cm2015B_{scenario}.ddr\",\n", + " }\n", + "\n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - " \n", + "\n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - " \n", + "\n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"cm2015B_{scenario}.rsp\")\n", - " \n", + "\n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - " \n", + "\n", " # construct simulated basin path\n", " simulated_basin_path = f\"cm2015B_{scenario}\"\n", "\n", @@ -3063,16 +3053,22 @@ " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - " \n", - " #Save output to parquet files \n", - " print('creating parquet for ' + scenario)\n", - " \n", - " output_directory = os.path.join(parquet_dir+ \"/scenario/\"+ scenario)\n", - " \n", + "\n", + " # Save output to parquet files\n", + " print(\"creating parquet for \" + scenario)\n", + "\n", + " output_directory = os.path.join(parquet_dir + \"/scenario/\" + scenario)\n", + "\n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - " \n", - " stm.xdd.convert_xdd(output_path=output_directory,allow_overwrite=False,xdd_files=scenarios_dir + \"/\"+ scenario + \"/cm2015B_\"+scenario+\".xdd\",id_subset=['3601008'],parallel_jobs=2)\n" + "\n", + " stm.xdd.convert_xdd(\n", + " output_path=output_directory,\n", + " allow_overwrite=False,\n", + " xdd_files=scenarios_dir + \"/\" + scenario + \"/cm2015B_\" + scenario + \".xdd\",\n", + " id_subset=[\"3601008\"],\n", + " parallel_jobs=2,\n", + " )" ] }, { From ec54a411460ab2eb3ed8774503f504d7f93ea062 Mon Sep 17 00:00:00 2001 From: crvernon Date: Wed, 10 Apr 2024 11:42:33 -0400 Subject: [PATCH 06/12] add option for xdd dtype conversion --- statemodify/xdd.py | 76 +++++++++++++++++++++++++++++++++++++++------- 1 file changed, 65 insertions(+), 11 deletions(-) diff --git a/statemodify/xdd.py b/statemodify/xdd.py index 8973986..8c44419 100644 --- a/statemodify/xdd.py +++ b/statemodify/xdd.py @@ -21,23 +21,27 @@ def __init__( xdd_files: Union[str, Path, list[Union[str, Path]]] = "**/*.xdd", id_subset: Union[None, list[str]] = None, parallel_jobs: int = 4, + preserve_string_dtype: bool = True ): """Convert object for transforming StateMod output .xdd files into compressed, columnar .parquet files. - :param output_path: Path to a folder where outputs should be written; default "./output" - :type output_path: str + :param output_path: Path to a folder where outputs should be written; default "./output" + :type output_path: str - :param allow_overwrite: If False, abort if files already exist in the output_path; default False - :type allow_overwrite: bool + :param allow_overwrite: If False, abort if files already exist in the output_path; default False + :type allow_overwrite: bool - :param xdd_files: File(s) or glob(s) to the .xdd files to convert; default "**/*.xdd" - :type xdd_files: List[str] + :param xdd_files: File(s) or glob(s) to the .xdd files to convert; default "**/*.xdd" + :type xdd_files: List[str] - :param id_subset: List of structure IDs to convert, or None for all; default None - :type id_subset: List[str] + :param id_subset: List of structure IDs to convert, or None for all; default None + :type id_subset: List[str] - :param parallel_jobs: How many files to process in parallel; default 4 - :type parallel_jobs: int + :param parallel_jobs: How many files to process in parallel; default 4 + :type parallel_jobs: int + + :param preserve_string_dtype: Keep string parsed data instead of casting to actual type; default True + :type preserve_string_dtype: bool :example: @@ -96,6 +100,9 @@ def __init__( # run this many jobs in parallel self.parallel_jobs = parallel_jobs + # to preserve string type for spacing set to True otherwise False for natural types + self.preserve_string_dtype = preserve_string_dtype + # field names for each .xdd entry self.fields = [ "structure_id", @@ -135,6 +142,45 @@ def __init__( "control_right", ] + # actual field data types + self.field_dtypes = { + "structure_id": str, + "river_id": str, + "year": np.int64, + "month": str, + "demand_total": np.float64, + "demand_cu": np.float64, + "from_river_by_priority": np.float64, + "from_river_by_storage": np.float64, + "from_river_by_other": np.float64, + "from_river_by_loss": np.float64, + "from_well": np.float64, + "from_carrier_by_priority": np.float64, + "from_carrier_by_other": np.float64, + "from_carrier_by_loss": np.float64, + "carried_exchange_bypass": np.float64, + "from_soil": np.float64, + "supply_total": np.float64, + "shortage_total": np.float64, + "shortage_cu": np.float64, + "water_use_cu": np.float64, + "water_use_to_soil": np.float64, + "water_use_to_other": np.float64, + "water_use_loss": np.float64, + "station_in_out_upstream_inflow": np.float64, + "station_in_out_reach_gain": np.float64, + "station_in_out_return_flow": np.float64, + "station_in_out_well_deplete": np.float64, + "station_in_out_from_to_groundwater_storage": np.float64, + "station_balance_river_inflow": np.float64, + "station_balance_river_divert": np.float64, + "station_balance_river_by_well": np.float64, + "station_balance_river_outflow": np.float64, + "available_flow": np.float64, + "control_location": str, + "control_right": np.float64, + } + # field sizes for each line of interest in the .xdd file self.field_sizes = np.array( [ @@ -243,7 +289,11 @@ def _parse_file( df = pd.DataFrame(data=data, columns=["structure_name"] + fields) if id_subset is not None: df = df[df["structure_id"].isin(id_subset)] - df.to_parquet(f"{output_path}/{Path(file).stem}.parquet") + + if self.preserve_string_dtype is False: + df = df.astype(self.field_dtypes) + + df.to_parquet(f"{output_path}/{Path(file).stem}.parquet") def convert_xdd( @@ -253,6 +303,7 @@ def convert_xdd( xdd_files: Union[str, Path, list[Union[str, Path]]] = "**/*.xdd", id_subset: Union[None, list[str]] = None, parallel_jobs: int = 4, + preserve_string_dtype: bool = True ): """Convert StateMod output .xdd files to compressed, columnar .parquet files. @@ -273,6 +324,9 @@ def convert_xdd( :param parallel_jobs: How many files to process in parallel; default 4 :type parallel_jobs: int + :param preserve_string_dtype: Keep string parsed data instead of casting to actual type; default True + :type preserve_string_dtype: bool + :return: None :rtype: None From 569eb5156456eb8fbb203e3314cf3560491898ea Mon Sep 17 00:00:00 2001 From: crvernon Date: Wed, 10 Apr 2024 11:47:31 -0400 Subject: [PATCH 07/12] fix call to preserve type fn --- statemodify/xdd.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/statemodify/xdd.py b/statemodify/xdd.py index 8c44419..74d5592 100644 --- a/statemodify/xdd.py +++ b/statemodify/xdd.py @@ -255,6 +255,7 @@ def convert(self): months=self.months, output_path=self.output_path, id_subset=self.id_subset, + preserve_string_dtype=self.preserve_string_dtype, ) for file in tqdm(self.xdd_files) ) @@ -269,6 +270,7 @@ def _parse_file( months: list[str], output_path: str, id_subset: Union[None, list[str]], + preserve_string_dtype: bool, ): data = [] with open(file) as f: @@ -290,7 +292,7 @@ def _parse_file( if id_subset is not None: df = df[df["structure_id"].isin(id_subset)] - if self.preserve_string_dtype is False: + if preserve_string_dtype is False: df = df.astype(self.field_dtypes) df.to_parquet(f"{output_path}/{Path(file).stem}.parquet") From 6744022a354170c652c8971bc9ca59ca2ab7393c Mon Sep 17 00:00:00 2001 From: crvernon Date: Wed, 10 Apr 2024 12:43:47 -0400 Subject: [PATCH 08/12] propagate call to dtypes to fn --- statemodify/xdd.py | 1 + 1 file changed, 1 insertion(+) diff --git a/statemodify/xdd.py b/statemodify/xdd.py index 74d5592..1d08515 100644 --- a/statemodify/xdd.py +++ b/statemodify/xdd.py @@ -360,4 +360,5 @@ def convert_xdd( xdd_files=xdd_files, id_subset=id_subset, parallel_jobs=parallel_jobs, + preserve_string_dtype=preserve_string_dtype, ).convert() From 7562cba6700221e6003651eb2e76dbeb73de4168 Mon Sep 17 00:00:00 2001 From: crvernon Date: Wed, 10 Apr 2024 13:04:20 -0400 Subject: [PATCH 09/12] fix field dtypes internal call --- statemodify/xdd.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/statemodify/xdd.py b/statemodify/xdd.py index 1d08515..4355581 100644 --- a/statemodify/xdd.py +++ b/statemodify/xdd.py @@ -256,6 +256,7 @@ def convert(self): output_path=self.output_path, id_subset=self.id_subset, preserve_string_dtype=self.preserve_string_dtype, + field_dtypes=self.field_dtypes, ) for file in tqdm(self.xdd_files) ) @@ -271,6 +272,7 @@ def _parse_file( output_path: str, id_subset: Union[None, list[str]], preserve_string_dtype: bool, + field_dtypes: dict ): data = [] with open(file) as f: @@ -293,7 +295,7 @@ def _parse_file( df = df[df["structure_id"].isin(id_subset)] if preserve_string_dtype is False: - df = df.astype(self.field_dtypes) + df = df.astype(field_dtypes) df.to_parquet(f"{output_path}/{Path(file).stem}.parquet") From 3ed9f414fbd5f3554b87681c40a08d687a37c0e1 Mon Sep 17 00:00:00 2001 From: crvernon Date: Wed, 10 Apr 2024 13:25:36 -0400 Subject: [PATCH 10/12] update dtypes call in notebooks --- ...Demand_WaterRights_File_Modification.ipynb | 5081 +++++++++++++- .../N2_Evaporation_File_Modification.ipynb | 4689 ++++++++++++- .../N3_Reservoir_File_Modification.ipynb | 5870 ++++++++++++++++- .../N4_Streamflow_File_Modification.ipynb | 2955 ++++++++- notebooks/N5_Batch_Modification.ipynb | 2641 +------- 5 files changed, 18249 insertions(+), 2987 deletions(-) diff --git a/notebooks/N1_Demand_WaterRights_File_Modification.ipynb b/notebooks/N1_Demand_WaterRights_File_Modification.ipynb index 63dd110..ebed839 100644 --- a/notebooks/N1_Demand_WaterRights_File_Modification.ipynb +++ b/notebooks/N1_Demand_WaterRights_File_Modification.ipynb @@ -77,13 +77,12 @@ "import logging\n", "import os\n", "import pickle\n", - "import subprocess\n", "from string import Template\n", + "import subprocess\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd\n", - "\n", + "import pandas as pd \n", "import statemodify as stm" ] }, @@ -123,7 +122,11 @@ "\n", "# data directory and root name for the target basin\n", "data_dir = os.path.join(\n", - " home_dir, \"data\", \"sj2015_StateMod_modified\", \"sj2015_StateMod_modified\", \"StateMod\"\n", + " home_dir,\n", + " \"data\",\n", + " \"sj2015_StateMod_modified\",\n", + " \"sj2015_StateMod_modified\",\n", + " \"StateMod\"\n", ")\n", "\n", "# directory to the target basin input files with root name for the basin\n", @@ -138,23 +141,1462 @@ "parquet_dir_ddr = os.path.join(data_dir, \"parquet_ddr\")\n", "\n", "# path to ddm and ddr template file\n", - "ddm_template_file = os.path.join(home_dir, \"data\", \"sj2015B_template_ddm.rsp\")\n", + "ddm_template_file = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"sj2015B_template_ddm.rsp\"\n", + ")\n", "\n", - "ddr_template_file = os.path.join(home_dir, \"data\", \"sj2015B_template_ddr.rsp\")" + "ddr_template_file = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"sj2015B_template_ddr.rsp\"\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "4568a7bb-7e35-4c54-9e95-f24f44d7384c", "metadata": { "scrolled": true, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Startup log file for messages to this point: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/sj2015B.rsp \n", + " Closing startup log file: statem.log\n", + " Opening dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/sj2015B.log \n", + "________________________________________________________________________\n", + "\n", + " StateMod \n", + " State of Colorado - Water Supply Planning Model \n", + "\n", + " Version: 17.0.3 \n", + " Last revision date: 2021/09/12\n", + "\n", + "________________________________________________________________________\n", + " \n", + " Subroutine Execut\n", + " Subroutine Datinp\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Control File (*.ctl) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Network File (*.rin)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Reservoir Station File (*.res)\n", + " Subroutine GetRes\n", + "\n", + " GetRes; Reservoir Station File (*.res) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Diversion Station File (*.dds)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Station File (*.ris)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Instream Flow Station File (*.ifs) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Well Station File (*.wes) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Plan Station File (*.pln) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Gage File (*.rig) \n", + " Subroutine Riginp\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Instream Flow Right File (*.ifr) 20 1 98\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Reservoir Right File (*.rer) \n", + "\n", + "________________________________________________________________________\n", + " Riginp; Direct Diversion Right File (*.ddr) \n", + "\n", + "________________________________________________________________________\n", + " Riginp; Direct Diversion Right File (*.ddr) \n", + "\n", + "________________________________________________________________________\n", + " Oprinp; Operational Right File (*.opr) \n", + "\n", + "________________________________________________________________________\n", + " Mdainp; Instream flow Demand file - Annual (*.ifa) \n", + " Subroutine Execut\n", + "\n", + "+ Execut; Year 1908 Month OCT \n", + "+ Execut; Year 1908 Month NOV \n", + "+ Execut; Year 1908 Month DEC \n", + "+ Execut; Year 1909 Month JAN \n", + "+ Execut; Year 1909 Month FEB \n", + "+ Execut; Year 1909 Month MAR \n", + "+ Execut; Year 1909 Month APR \n", + "+ Execut; Year 1909 Month MAY \n", + "+ Execut; Year 1909 Month JUN \n", + "+ Execut; Year 1909 Month JUL \n", + "+ Execut; Year 1909 Month AUG \n", + "+ Execut; Year 1909 Month SEP \n", + "+ Execut; Year 1909 Month OCT \n", + "+ Execut; Year 1909 Month NOV \n", + "+ Execut; Year 1909 Month DEC \n", + "+ Execut; Year 1910 Month JAN \n", + "+ Execut; Year 1910 Month FEB \n", + "+ Execut; Year 1910 Month MAR \n", + "+ Execut; Year 1910 Month APR \n", + "+ Execut; Year 1910 Month MAY \n", + "+ Execut; Year 1910 Month JUN \n", + "+ Execut; Year 1910 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DEC \n", + "+ Execut; Year 1996 Month JAN \n", + "+ Execut; Year 1996 Month FEB \n", + "+ Execut; Year 1996 Month MAR \n", + "+ Execut; Year 1996 Month APR \n", + "+ Execut; Year 1996 Month MAY \n", + "+ Execut; Year 1996 Month JUN \n", + "+ Execut; Year 1996 Month JUL \n", + "+ Execut; Year 1996 Month AUG \n", + "+ Execut; Year 1996 Month SEP \n", + "+ Execut; Year 1996 Month OCT \n", + "+ Execut; Year 1996 Month NOV \n", + "+ Execut; Year 1996 Month DEC \n", + "+ Execut; Year 1997 Month JAN \n", + "+ Execut; Year 1997 Month FEB \n", + "+ Execut; Year 1997 Month MAR \n", + "+ Execut; Year 1997 Month APR \n", + "+ Execut; Year 1997 Month MAY \n", + "+ Execut; Year 1997 Month JUN \n", + "+ Execut; Year 1997 Month JUL \n", + "+ Execut; Year 1997 Month AUG \n", + "+ Execut; Year 1997 Month SEP \n", + "+ Execut; Year 1997 Month OCT \n", + "+ Execut; Year 1997 Month NOV \n", + "+ Execut; Year 1997 Month DEC \n", + "+ Execut; Year 1998 Month JAN \n", + "+ Execut; Year 1998 Month FEB \n", + 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Month JUL \n", + "+ Execut; Year 2002 Month AUG \n", + "+ Execut; Year 2002 Month SEP \n", + "+ Execut; Year 2002 Month OCT \n", + "+ Execut; Year 2002 Month NOV \n", + "+ Execut; Year 2002 Month DEC \n", + "+ Execut; Year 2003 Month JAN \n", + "+ Execut; Year 2003 Month FEB \n", + "+ Execut; Year 2003 Month MAR \n", + "+ Execut; Year 2003 Month APR \n", + "+ Execut; Year 2003 Month MAY \n", + "+ Execut; Year 2003 Month JUN \n", + "+ Execut; Year 2003 Month JUL \n", + "+ Execut; Year 2003 Month AUG \n", + "+ Execut; Year 2003 Month SEP \n", + "+ Execut; Year 2003 Month OCT \n", + "+ Execut; Year 2003 Month NOV \n", + "+ Execut; Year 2003 Month DEC \n", + "+ Execut; Year 2004 Month JAN \n", + "+ Execut; Year 2004 Month FEB \n", + "+ Execut; Year 2004 Month MAR \n", + "+ Execut; Year 2004 Month APR \n", + "+ Execut; Year 2004 Month MAY \n", + "+ Execut; Year 2004 Month JUN \n", + "+ Execut; Year 2004 Month JUL \n", + "+ Execut; Year 2004 Month AUG \n", + "+ Execut; Year 2004 Month SEP \n", + "+ Execut; Year 2004 Month OCT \n", + "+ Execut; Year 2004 Month NOV \n", + "+ Execut; Year 2004 Month DEC \n", + "+ Execut; Year 2005 Month JAN \n", + "+ Execut; Year 2005 Month FEB \n", + "+ Execut; Year 2005 Month MAR \n", + "+ Execut; Year 2005 Month APR \n", + "+ Execut; Year 2005 Month MAY \n", + "+ Execut; Year 2005 Month JUN \n", + "+ Execut; Year 2005 Month JUL \n", + "+ Execut; Year 2005 Month AUG \n", + "+ Execut; Year 2005 Month SEP \n", + "+ Execut; Year 2005 Month OCT \n", + "+ Execut; Year 2005 Month NOV \n", + "+ Execut; Year 2005 Month DEC \n", + "+ Execut; Year 2006 Month JAN \n", + "+ Execut; Year 2006 Month FEB \n", + "+ Execut; Year 2006 Month MAR \n", + "+ Execut; Year 2006 Month APR \n", + "+ Execut; Year 2006 Month MAY \n", + "+ Execut; Year 2006 Month JUN \n", + "+ Execut; Year 2006 Month JUL \n", + "+ Execut; Year 2006 Month AUG \n", + "+ Execut; Year 2006 Month SEP \n", + "+ Execut; Year 2006 Month OCT \n", + "+ Execut; Year 2006 Month NOV \n", + "+ Execut; Year 2006 Month DEC \n", + "+ Execut; Year 2007 Month JAN \n", + "+ Execut; Year 2007 Month FEB \n", + "+ Execut; Year 2007 Month MAR \n", + "+ Execut; Year 2007 Month APR \n", + "+ Execut; Year 2007 Month MAY \n", + "+ Execut; Year 2007 Month JUN \n", + "+ Execut; Year 2007 Month JUL \n", + "+ Execut; Year 2007 Month AUG \n", + "+ Execut; Year 2007 Month SEP \n", + "+ Execut; Year 2007 Month OCT \n", + "+ Execut; Year 2007 Month NOV \n", + "+ Execut; Year 2007 Month DEC \n", + "+ Execut; Year 2008 Month JAN \n", + "+ Execut; Year 2008 Month FEB \n", + "+ Execut; Year 2008 Month MAR \n", + "+ Execut; Year 2008 Month APR \n", + "+ Execut; Year 2008 Month MAY \n", + "+ Execut; Year 2008 Month JUN \n", + "+ Execut; Year 2008 Month JUL \n", + "+ Execut; Year 2008 Month AUG \n", + "+ Execut; Year 2008 Month SEP \n", + "+ Execut; Year 2008 Month OCT \n", + "+ Execut; Year 2008 Month NOV \n", + "+ Execut; Year 2008 Month DEC \n", + "+ Execut; Year 2009 Month JAN \n", + "+ Execut; Year 2009 Month FEB \n", + "+ Execut; Year 2009 Month MAR \n", + "+ Execut; Year 2009 Month APR \n", + "+ Execut; Year 2009 Month MAY \n", + "+ Execut; Year 2009 Month JUN \n", + "+ Execut; Year 2009 Month JUL \n", + "+ Execut; Year 2009 Month AUG \n", + "+ Execut; Year 2009 Month SEP \n", + "+ Execut; Year 2009 Month OCT \n", + "+ Execut; Year 2009 Month NOV \n", + "+ Execut; Year 2009 Month DEC \n", + "+ Execut; Year 2010 Month JAN \n", + "+ Execut; Year 2010 Month FEB \n", + "+ Execut; Year 2010 Month MAR \n", + "+ Execut; Year 2010 Month APR \n", + "+ Execut; Year 2010 Month MAY \n", + "+ Execut; Year 2010 Month JUN \n", + "+ Execut; Year 2010 Month JUL \n", + "+ Execut; Year 2010 Month AUG \n", + "+ Execut; Year 2010 Month SEP \n", + "+ Execut; Year 2010 Month OCT \n", + "+ Execut; Year 2010 Month NOV \n", + "+ Execut; Year 2010 Month DEC \n", + "+ Execut; Year 2011 Month JAN \n", + "+ Execut; Year 2011 Month FEB \n", + "+ Execut; Year 2011 Month MAR \n", + "+ Execut; Year 2011 Month APR \n", + "+ Execut; Year 2011 Month MAY \n", + "+ Execut; Year 2011 Month JUN \n", + "+ Execut; Year 2011 Month JUL \n", + "+ Execut; Year 2011 Month AUG \n", + "+ Execut; Year 2011 Month SEP \n", + "+ Execut; Year 2011 Month OCT \n", + "+ Execut; Year 2011 Month NOV \n", + "+ Execut; Year 2011 Month DEC \n", + "+ Execut; Year 2012 Month JAN \n", + "+ Execut; Year 2012 Month FEB \n", + "+ Execut; Year 2012 Month MAR \n", + "+ Execut; Year 2012 Month APR \n", + "+ Execut; Year 2012 Month MAY \n", + "+ Execut; Year 2012 Month JUN \n", + "+ Execut; Year 2012 Month JUL \n", + "+ Execut; Year 2012 Month AUG \n", + "+ Execut; Year 2012 Month SEP \n", + "+ Execut; Year 2012 Month OCT \n", + "+ Execut; Year 2012 Month NOV \n", + "+ Execut; Year 2012 Month DEC \n", + "+ Execut; Year 2013 Month JAN \n", + "+ Execut; Year 2013 Month FEB \n", + "+ Execut; Year 2013 Month MAR \n", + "+ Execut; Year 2013 Month APR \n", + "+ Execut; Year 2013 Month MAY \n", + "+ Execut; Year 2013 Month JUN \n", + "+ Execut; Year 2013 Month JUL \n", + "+ Execut; Year 2013 Month AUG \n", + "+ Execut; Year 2013 Month SEP \n", + "\n", + "________________________________________________________________________\n", + " Execut; On Year 1966 Month JUN Day 1\n", + " The maximum number of reoperations 221\n", + "\n", + "________________________________________________________________________\n", + " Execut; Writing reports\n", + "\n", + "________________________________________________________________________\n", + " Execut; \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutRes \n", + "+ Printing Reservoir Summary 1 of 33; or 3. % Complete\n", + "+ Printing Reservoir Summary 26 of 33; or 79. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutDivW \n", + "+ Printing Diversion & Stream Summary 1 of 558; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 26 of 558; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 51 of 558; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 76 of 558; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 101 of 558; or 18. % Complete\n", + "+ Printing Diversion & Stream Summary 126 of 558; or 23. % Complete\n", + "+ Printing Diversion & Stream Summary 151 of 558; or 27. % Complete\n", + "+ Printing Diversion & Stream Summary 176 of 558; or 32. % Complete\n", + "+ Printing Diversion & Stream Summary 201 of 558; or 36. % Complete\n", + "+ Printing Diversion & Stream Summary 226 of 558; or 41. % Complete\n", + "+ Printing Diversion & Stream Summary 251 of 558; or 45. % Complete\n", + "+ Printing Diversion & Stream Summary 276 of 558; or 49. % Complete\n", + "+ Printing Diversion & Stream Summary 301 of 558; or 54. % Complete\n", + "+ Printing Diversion & Stream Summary 326 of 558; or 58. % Complete\n", + "+ Printing Diversion & Stream Summary 351 of 558; or 63. % Complete\n", + "+ Printing Diversion & Stream Summary 376 of 558; or 67. % Complete\n", + "+ Printing Diversion & Stream Summary 401 of 558; or 72. % Complete\n", + "+ Printing Diversion & Stream Summary 426 of 558; or 76. % Complete\n", + "+ Printing Diversion & Stream Summary 451 of 558; or 81. % Complete\n", + "+ Printing Diversion & Stream Summary 476 of 558; or 85. % Complete\n", + "+ Printing Diversion & Stream Summary 501 of 558; or 90. % Complete\n", + "+ Printing Diversion & Stream Summary 526 of 558; or 94. % Complete\n", + "+ Printing Diversion & Stream Summary 551 of 558; or 99. % Complete\n", + "+ Printing Diversion & Stream Summary 558 of 558; or 100. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutOpr \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutXss \n", + "+ Printing Structure Summary (*.xss) 0 of 342; or 0. % Complete\n", + "+ Printing Structure Summary (*.xss) 25 of 342; or 8. % Complete\n", + "+ Printing Structure Summary (*.xss) 50 of 342; or 15. % Complete\n", + "+ Printing Structure Summary (*.xss) 75 of 342; or 22. % Complete\n", + "+ Printing Structure Summary (*.xss) 100 of 342; or 30. % Complete\n", + "+ Printing Structure Summary (*.xss) 125 of 342; or 37. % Complete\n", + "+ Printing Structure Summary (*.xss) 150 of 342; or 44. % Complete\n", + "+ Printing Structure Summary (*.xss) 175 of 342; or 51. % Complete\n", + "+ Printing Structure Summary (*.xss) 200 of 342; or 59. % Complete\n", + "+ Printing Structure Summary (*.xss) 225 of 342; or 66. % Complete\n", + "+ Printing Structure Summary (*.xss) 250 of 342; or 73. % Complete\n", + "+ Printing Structure Summary (*.xss) 275 of 342; or 81. % Complete\n", + "+ Printing Structure Summary (*.xss) 300 of 342; or 88. % Complete\n", + "+ Printing Structure Summary (*.xss) 325 of 342; or 95. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine Outifr\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutPln \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutWW \n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Run output files are:\n", + " \n", + " Diversion output: *.xdd\n", + " Reservoir output: *.xre\n", + " Operating Rule Info: *.xop\n", + " Instream Reach Info: *.xir\n", + " Structure Summary: *.xss\n", + " Call (Control) Summary: *.xca\n", + " Plan Output: *.xpl\n", + " WWSP Output: *.xww\n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Termination\n", + " Statem; See detailed messages in dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/sj2015B.log \n", + " Stop 0\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# run statemod\n", - "subprocess.call([statemod_exe, basin_path, \"-simulate\"])" + "subprocess.call([statemod_exe, basin_path, \"-simulate\"])\n" ] }, { @@ -167,16 +1609,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "097557cc-273b-4218-aa66-7341f408a616", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 29.32it/s]\n" + ] + } + ], "source": [ - "# Extract shortages using statemodify convert_xdd() function\n", + "#Extract shortages using statemodify convert_xdd() function \n", "\n", - "# create a directory to store the historical shortages\n", + "# create a directory to store the historical shortages \n", "output_dir = os.path.join(data_dir, \"historic_shortages\")\n", "\n", "# create a directory to store the new files in if it does not exist\n", @@ -192,22 +1642,451 @@ " # path, glob, or a list of paths/globs to the .xdd files you want to convert\n", " xdd_files=os.path.join(data_dir, \"*.xdd\"),\n", " # if the output .parquet files should only contain a subset of structure ids, list them here; None for all\n", - " id_subset=[\"2900501\", \"2900519\", \"2900555\"],\n", + " id_subset=['2900501','2900519','2900555'],\n", " # how many .xdd files to convert in parallel; optimally you will want 2-4 CPUs per parallel process\n", " parallel_jobs=4,\n", - ")" + " # convert to natural data types\n", + " preserve_string_dtype=False\n", + ")\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "01247aaa-c0c1-4f92-bfd5-d5d50873b2be", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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structure_namestructure_idriver_idyearmonthdemand_totaldemand_cufrom_river_by_priorityfrom_river_by_storagefrom_river_by_other...station_in_out_return_flowstation_in_out_well_depletestation_in_out_from_to_groundwater_storagestation_balance_river_inflowstation_balance_river_divertstation_balance_river_by_wellstation_balance_river_outflowavailable_flowcontrol_locationcontrol_right
15015ALLEN CREEK DITCH290050129005011908OCT13.07.013.00.00.0...0.00.00.03792.013.00.03779.02918.0NA-1.0
15016ALLEN CREEK DITCH290050129005011908NOV0.00.00.00.00.0...0.00.00.02343.00.00.02343.01510.0NA-1.0
15017ALLEN CREEK DITCH290050129005011908DEC0.00.00.00.00.0...0.00.00.01721.00.00.01721.0860.0NA-1.0
15018ALLEN CREEK DITCH290050129005011909JAN0.00.00.00.00.0...0.00.00.01512.00.00.01512.0525.0NA-1.0
15019ALLEN CREEK DITCH290050129005011909FEB0.00.00.00.00.0...0.00.00.01370.00.00.01370.0510.0NA-1.0
..................................................................
118750CARR DITCH290055529005552013JUN426.081.0426.00.00.0...711.00.00.029239.0426.00.028813.026410.0NA-1.0
118751CARR DITCH290055529005552013JUL314.088.0314.00.00.0...581.00.00.09580.0314.00.09266.07180.0NA-1.0
118752CARR DITCH290055529005552013AUG203.059.0203.00.00.0...524.00.00.020441.0203.00.020238.018989.0NA-1.0
118753CARR DITCH290055529005552013SEP144.039.0144.00.00.0...454.00.00.042214.0144.00.042070.041359.0NA-1.0
118754CARR DITCH290055529005552013TOT1341.0328.01341.00.00.0...3395.00.00.0228506.01341.00.0227165.0215605.0NA-1.0
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4095 rows × 36 columns

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" + ], + "text/plain": [ + " structure_name structure_id river_id year month demand_total \\\n", + "15015 ALLEN CREEK DITCH 2900501 2900501 1908 OCT 13.0 \n", + "15016 ALLEN CREEK DITCH 2900501 2900501 1908 NOV 0.0 \n", + "15017 ALLEN CREEK DITCH 2900501 2900501 1908 DEC 0.0 \n", + "15018 ALLEN CREEK DITCH 2900501 2900501 1909 JAN 0.0 \n", + "15019 ALLEN CREEK DITCH 2900501 2900501 1909 FEB 0.0 \n", + "... ... ... ... ... ... ... \n", + "118750 CARR DITCH 2900555 2900555 2013 JUN 426.0 \n", + "118751 CARR DITCH 2900555 2900555 2013 JUL 314.0 \n", + "118752 CARR DITCH 2900555 2900555 2013 AUG 203.0 \n", + "118753 CARR DITCH 2900555 2900555 2013 SEP 144.0 \n", + "118754 CARR DITCH 2900555 2900555 2013 TOT 1341.0 \n", + "\n", + " demand_cu from_river_by_priority from_river_by_storage \\\n", + "15015 7.0 13.0 0.0 \n", + "15016 0.0 0.0 0.0 \n", + "15017 0.0 0.0 0.0 \n", + "15018 0.0 0.0 0.0 \n", + "15019 0.0 0.0 0.0 \n", + "... ... ... ... \n", + "118750 81.0 426.0 0.0 \n", + "118751 88.0 314.0 0.0 \n", + "118752 59.0 203.0 0.0 \n", + "118753 39.0 144.0 0.0 \n", + "118754 328.0 1341.0 0.0 \n", + "\n", + " from_river_by_other ... station_in_out_return_flow \\\n", + "15015 0.0 ... 0.0 \n", + "15016 0.0 ... 0.0 \n", + "15017 0.0 ... 0.0 \n", + "15018 0.0 ... 0.0 \n", + "15019 0.0 ... 0.0 \n", + "... ... ... ... \n", + "118750 0.0 ... 711.0 \n", + "118751 0.0 ... 581.0 \n", + "118752 0.0 ... 524.0 \n", + "118753 0.0 ... 454.0 \n", + "118754 0.0 ... 3395.0 \n", + "\n", + " station_in_out_well_deplete \\\n", + "15015 0.0 \n", + "15016 0.0 \n", + "15017 0.0 \n", + "15018 0.0 \n", + "15019 0.0 \n", + "... ... \n", + "118750 0.0 \n", + "118751 0.0 \n", + "118752 0.0 \n", + "118753 0.0 \n", + "118754 0.0 \n", + "\n", + " station_in_out_from_to_groundwater_storage \\\n", + "15015 0.0 \n", + "15016 0.0 \n", + "15017 0.0 \n", + "15018 0.0 \n", + "15019 0.0 \n", + "... ... \n", + "118750 0.0 \n", + "118751 0.0 \n", + "118752 0.0 \n", + "118753 0.0 \n", + "118754 0.0 \n", + "\n", + " station_balance_river_inflow station_balance_river_divert \\\n", + "15015 3792.0 13.0 \n", + "15016 2343.0 0.0 \n", + "15017 1721.0 0.0 \n", + "15018 1512.0 0.0 \n", + "15019 1370.0 0.0 \n", + "... ... ... \n", + "118750 29239.0 426.0 \n", + "118751 9580.0 314.0 \n", + "118752 20441.0 203.0 \n", + "118753 42214.0 144.0 \n", + "118754 228506.0 1341.0 \n", + "\n", + " station_balance_river_by_well station_balance_river_outflow \\\n", + "15015 0.0 3779.0 \n", + "15016 0.0 2343.0 \n", + "15017 0.0 1721.0 \n", + "15018 0.0 1512.0 \n", + "15019 0.0 1370.0 \n", + "... ... ... \n", + "118750 0.0 28813.0 \n", + "118751 0.0 9266.0 \n", + "118752 0.0 20238.0 \n", + "118753 0.0 42070.0 \n", + "118754 0.0 227165.0 \n", + "\n", + " available_flow control_location control_right \n", + "15015 2918.0 NA -1.0 \n", + "15016 1510.0 NA -1.0 \n", + "15017 860.0 NA -1.0 \n", + "15018 525.0 NA -1.0 \n", + "15019 510.0 NA -1.0 \n", + "... ... ... ... \n", + "118750 26410.0 NA -1.0 \n", + "118751 7180.0 NA -1.0 \n", + "118752 18989.0 NA -1.0 \n", + "118753 41359.0 NA -1.0 \n", + "118754 215605.0 NA -1.0 \n", + "\n", + "[4095 rows x 36 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "data = pd.read_parquet(os.path.join(output_dir, \"sj2015B.parquet\"), engine=\"pyarrow\")\n", + "data=pd.read_parquet(os.path.join(output_dir,'sj2015B.parquet'),engine='pyarrow')\n", "data" ] }, @@ -231,20 +2110,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "85b20787-1445-4af2-801f-f94b52006ab4", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "data[\"shortage_total\"] = data[\"shortage_total\"].astype(float)\n", - "data[\"year\"] = data[\"year\"].astype(int)\n", - "\n", "fig, ax = plt.subplots()\n", "\n", - "for name, group in data.groupby(\"structure_id\"):\n", - " ax.scatter(group[\"year\"], group[\"shortage_total\"], label=name)\n", + "for name, group in data.groupby('structure_id'):\n", + " ax.scatter(\n", + " group['year'], group['shortage_total'], label=name)\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", @@ -281,7 +2179,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "e1c425b3-f416-4447-ac95-e8efb00ad5cc", "metadata": { "tags": [] @@ -289,7 +2187,10 @@ "outputs": [], "source": [ "# a dictionary to describe what users you want to modify and the bounds for the LHS\n", - "setup_dict = {\"ids\": [\"2900501\", \"2900519\", \"2900555\"], \"bounds\": [0.5, 1.5]}\n", + "setup_dict = {\n", + " \"ids\": [\"2900501\", \"2900519\",\"2900555\"],\n", + " \"bounds\": [0.5, 1.5]\n", + "}\n", "\n", "output_directory = output_dir = os.path.join(data_dir, \"input_files\")\n", "\n", @@ -330,8 +2231,8 @@ " data_specification_file=None,\n", " min_bound_value=-0.5,\n", " max_bound_value=1.5,\n", - " save_sample=True,\n", - ")" + " save_sample=True\n", + ")\n" ] }, { @@ -344,16 +2245,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "fb711821-eba6-444b-baf9-4ab5f88d9587", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.708511 ],\n", + " [1.36016225]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import numpy as np\n", - "\n", - "sample_array = np.load(output_directory + \"/ddm_2-samples_scenario-1.npy\")\n", + "sample_array = np.load(output_directory+'/ddm_2-samples_scenario-1.npy')\n", "sample_array" ] }, @@ -379,13 +2290,1449 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "4e303b1d-ac7f-4123-b76d-e1c11d407478", "metadata": { "scrolled": true, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running: S0_1\n", + " Startup log file for messages to this point: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddm/S0_1/sj2015B_S0_1.rsp \n", + " Closing startup log file: statem.log\n", + " Opening dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddm/S0_1/sj2015B_S0_1.log \n", + "________________________________________________________________________\n", + "\n", + " StateMod \n", + " State of Colorado - Water Supply Planning Model \n", + "\n", + " Version: 17.0.3 \n", + " Last revision date: 2021/09/12\n", + "\n", + "________________________________________________________________________\n", + " \n", + " Subroutine Execut\n", + " Subroutine Datinp\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Control File (*.ctl) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Network File (*.rin)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Reservoir Station File (*.res)\n", + " Subroutine GetRes\n", + "\n", + " GetRes; Reservoir Station File (*.res) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Diversion Station File (*.dds)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Station File (*.ris)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Instream Flow Station File (*.ifs) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Well Station File (*.wes) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Plan Station File (*.pln) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Gage File (*.rig) \n", + " Subroutine Riginp\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Instream Flow Right File (*.ifr) 20 1 98\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Reservoir Right File (*.rer) \n", + "\n", + "________________________________________________________________________\n", + " Riginp; Direct Diversion Right File (*.ddr) \n", + "\n", + "________________________________________________________________________\n", + " Riginp; Direct Diversion Right File (*.ddr) \n", + "\n", + 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2004 Month JUN \n", + "+ Execut; Year 2004 Month JUL \n", + "+ Execut; Year 2004 Month AUG \n", + "+ Execut; Year 2004 Month SEP \n", + "+ Execut; Year 2004 Month OCT \n", + "+ Execut; Year 2004 Month NOV \n", + "+ Execut; Year 2004 Month DEC \n", + "+ Execut; Year 2005 Month JAN \n", + "+ Execut; Year 2005 Month FEB \n", + "+ Execut; Year 2005 Month MAR \n", + "+ Execut; Year 2005 Month APR \n", + "+ Execut; Year 2005 Month MAY \n", + "+ Execut; Year 2005 Month JUN \n", + "+ Execut; Year 2005 Month JUL \n", + "+ Execut; Year 2005 Month AUG \n", + "+ Execut; Year 2005 Month SEP \n", + "+ Execut; Year 2005 Month OCT \n", + "+ Execut; Year 2005 Month NOV \n", + "+ Execut; Year 2005 Month DEC \n", + "+ Execut; Year 2006 Month JAN \n", + "+ Execut; Year 2006 Month FEB \n", + "+ Execut; Year 2006 Month MAR \n", + "+ Execut; Year 2006 Month APR \n", + "+ Execut; Year 2006 Month MAY \n", + "+ Execut; Year 2006 Month JUN \n", + "+ Execut; Year 2006 Month JUL \n", + "+ Execut; Year 2006 Month AUG \n", + "+ Execut; Year 2006 Month SEP \n", + "+ Execut; Year 2006 Month OCT \n", + "+ Execut; Year 2006 Month NOV \n", + "+ Execut; Year 2006 Month DEC \n", + "+ Execut; Year 2007 Month JAN \n", + "+ Execut; Year 2007 Month FEB \n", + "+ Execut; Year 2007 Month MAR \n", + "+ Execut; Year 2007 Month APR \n", + "+ Execut; Year 2007 Month MAY \n", + "+ Execut; Year 2007 Month JUN \n", + "+ Execut; Year 2007 Month JUL \n", + "+ Execut; Year 2007 Month AUG \n", + "+ Execut; Year 2007 Month SEP \n", + "+ Execut; Year 2007 Month OCT \n", + "+ Execut; Year 2007 Month NOV \n", + "+ Execut; Year 2007 Month DEC \n", + "+ Execut; Year 2008 Month JAN \n", + "+ Execut; Year 2008 Month FEB \n", + "+ Execut; Year 2008 Month MAR \n", + "+ Execut; Year 2008 Month APR \n", + "+ Execut; Year 2008 Month MAY \n", + "+ Execut; Year 2008 Month JUN \n", + "+ Execut; Year 2008 Month JUL \n", + "+ Execut; Year 2008 Month AUG \n", + "+ Execut; Year 2008 Month SEP \n", + "+ Execut; Year 2008 Month OCT \n", + "+ Execut; Year 2008 Month NOV \n", + "+ Execut; Year 2008 Month DEC \n", + "+ Execut; Year 2009 Month JAN \n", + "+ Execut; Year 2009 Month FEB \n", + "+ Execut; Year 2009 Month MAR \n", + "+ Execut; Year 2009 Month APR \n", + "+ Execut; Year 2009 Month MAY \n", + "+ Execut; Year 2009 Month JUN \n", + "+ Execut; Year 2009 Month JUL \n", + "+ Execut; Year 2009 Month AUG \n", + "+ Execut; Year 2009 Month SEP \n", + "+ Execut; Year 2009 Month OCT \n", + "+ Execut; Year 2009 Month NOV \n", + "+ Execut; Year 2009 Month DEC \n", + "+ Execut; Year 2010 Month JAN \n", + "+ Execut; Year 2010 Month FEB \n", + "+ Execut; Year 2010 Month MAR \n", + "+ Execut; Year 2010 Month APR \n", + "+ Execut; Year 2010 Month MAY \n", + "+ Execut; Year 2010 Month JUN \n", + "+ Execut; Year 2010 Month JUL \n", + "+ Execut; Year 2010 Month AUG \n", + "+ Execut; Year 2010 Month SEP \n", + "+ Execut; Year 2010 Month OCT \n", + "+ Execut; Year 2010 Month NOV \n", + "+ Execut; Year 2010 Month DEC \n", + "+ Execut; Year 2011 Month JAN \n", + "+ Execut; Year 2011 Month FEB \n", + "+ Execut; Year 2011 Month MAR \n", + "+ Execut; Year 2011 Month APR \n", + "+ Execut; Year 2011 Month MAY \n", + "+ Execut; Year 2011 Month JUN \n", + "+ Execut; Year 2011 Month JUL \n", + "+ Execut; Year 2011 Month AUG \n", + "+ Execut; Year 2011 Month SEP \n", + "+ Execut; Year 2011 Month OCT \n", + "+ Execut; Year 2011 Month NOV \n", + "+ Execut; Year 2011 Month DEC \n", + "+ Execut; Year 2012 Month JAN \n", + "+ Execut; Year 2012 Month FEB \n", + "+ Execut; Year 2012 Month MAR \n", + "+ Execut; Year 2012 Month APR \n", + "+ Execut; Year 2012 Month MAY \n", + "+ Execut; Year 2012 Month JUN \n", + "+ Execut; Year 2012 Month JUL \n", + "+ Execut; Year 2012 Month AUG \n", + "+ Execut; Year 2012 Month SEP \n", + "+ Execut; Year 2012 Month OCT \n", + "+ Execut; Year 2012 Month NOV \n", + "+ Execut; Year 2012 Month DEC \n", + "+ Execut; Year 2013 Month JAN \n", + "+ Execut; Year 2013 Month FEB \n", + "+ Execut; Year 2013 Month MAR \n", + "+ Execut; Year 2013 Month APR \n", + "+ Execut; Year 2013 Month MAY \n", + "+ Execut; Year 2013 Month JUN \n", + "+ Execut; Year 2013 Month JUL \n", + "+ Execut; Year 2013 Month AUG \n", + "+ Execut; Year 2013 Month SEP \n", + "\n", + "________________________________________________________________________\n", + " Execut; On Year 1966 Month JUN Day 1\n", + " The maximum number of reoperations 221\n", + "\n", + "________________________________________________________________________\n", + " Execut; Writing reports\n", + "\n", + "________________________________________________________________________\n", + " Execut; \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutRes \n", + "+ Printing Reservoir Summary 1 of 33; or 3. % Complete\n", + "+ Printing Reservoir Summary 26 of 33; or 79. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutDivW \n", + "+ Printing Diversion & Stream Summary 1 of 558; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 26 of 558; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 51 of 558; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 76 of 558; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 101 of 558; or 18. % Complete\n", + "+ Printing Diversion & Stream Summary 126 of 558; or 23. % Complete\n", + "+ Printing Diversion & Stream Summary 151 of 558; or 27. % Complete\n", + "+ Printing Diversion & Stream Summary 176 of 558; or 32. % Complete\n", + "+ Printing Diversion & Stream Summary 201 of 558; or 36. % Complete\n", + "+ Printing Diversion & Stream Summary 226 of 558; or 41. % Complete\n", + "+ Printing Diversion & Stream Summary 251 of 558; or 45. % Complete\n", + "+ Printing Diversion & Stream Summary 276 of 558; or 49. % Complete\n", + "+ Printing Diversion & Stream Summary 301 of 558; or 54. % Complete\n", + "+ Printing Diversion & Stream Summary 326 of 558; or 58. % Complete\n", + "+ Printing Diversion & Stream Summary 351 of 558; or 63. % Complete\n", + "+ Printing Diversion & Stream Summary 376 of 558; or 67. % Complete\n", + "+ Printing Diversion & Stream Summary 401 of 558; or 72. % Complete\n", + "+ Printing Diversion & Stream Summary 426 of 558; or 76. % Complete\n", + "+ Printing Diversion & Stream Summary 451 of 558; or 81. % Complete\n", + "+ Printing Diversion & Stream Summary 476 of 558; or 85. % Complete\n", + "+ Printing Diversion & Stream Summary 501 of 558; or 90. % Complete\n", + "+ Printing Diversion & Stream Summary 526 of 558; or 94. % Complete\n", + "+ Printing Diversion & Stream Summary 551 of 558; or 99. % Complete\n", + "+ Printing Diversion & Stream Summary 558 of 558; or 100. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutOpr \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutXss \n", + "+ Printing Structure Summary (*.xss) 0 of 342; or 0. % Complete\n", + "+ Printing Structure Summary (*.xss) 25 of 342; or 8. % Complete\n", + "+ Printing Structure Summary (*.xss) 50 of 342; or 15. % Complete\n", + "+ Printing Structure Summary (*.xss) 75 of 342; or 22. % Complete\n", + "+ Printing Structure Summary (*.xss) 100 of 342; or 30. % Complete\n", + "+ Printing Structure Summary (*.xss) 125 of 342; or 37. % Complete\n", + "+ Printing Structure Summary (*.xss) 150 of 342; or 44. % Complete\n", + "+ Printing Structure Summary (*.xss) 175 of 342; or 51. % Complete\n", + "+ Printing Structure Summary (*.xss) 200 of 342; or 59. % Complete\n", + "+ Printing Structure Summary (*.xss) 225 of 342; or 66. % Complete\n", + "+ Printing Structure Summary (*.xss) 250 of 342; or 73. % Complete\n", + "+ Printing Structure Summary (*.xss) 275 of 342; or 81. % Complete\n", + "+ Printing Structure Summary (*.xss) 300 of 342; or 88. % Complete\n", + "+ Printing Structure Summary (*.xss) 325 of 342; or 95. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine Outifr\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutPln \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutWW \n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Run output files are:\n", + " \n", + " Diversion output: *.xdd\n", + " Reservoir output: *.xre\n", + " Operating Rule Info: *.xop\n", + " Instream Reach Info: *.xir\n", + " Structure Summary: *.xss\n", + " Call (Control) Summary: *.xca\n", + " Plan Output: *.xpl\n", + " WWSP Output: *.xww\n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Termination\n", + " Statem; See detailed messages in dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddm/S0_1/sj2015B_S0_1.log \n", + " Stop 0\n", + "creating parquet for S0_1\n" + ] + }, + { + "ename": "OSError", + "evalue": "parquet files already exist in /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/parquet_ddm/scenario/S0_1 but allow_overwrite is False", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 51\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(output_directory):\n\u001b[1;32m 49\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(output_directory)\n\u001b[0;32m---> 51\u001b[0m \u001b[43mstm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxdd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_xdd\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_directory\u001b[49m\u001b[43m,\u001b[49m\u001b[43mallow_overwrite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mxdd_files\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscenarios_dir_ddm\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mscenario\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/sj2015B_\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mscenario\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m.xdd\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mid_subset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m2900501\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m2900519\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m2900555\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mparallel_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/usr/src/statemodify/statemodify/statemodify/xdd.py:244\u001b[0m, in \u001b[0;36mconvert_xdd\u001b[0;34m(output_path, allow_overwrite, xdd_files, id_subset, parallel_jobs)\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconvert_xdd\u001b[39m(\n\u001b[1;32m 193\u001b[0m \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m 194\u001b[0m output_path: Union[\u001b[38;5;28mstr\u001b[39m, Path] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./output\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 198\u001b[0m parallel_jobs: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m,\n\u001b[1;32m 199\u001b[0m ):\n\u001b[1;32m 200\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Convert StateMod output .xdd files to compressed, columnar .parquet files which easily interoperate\u001b[39;00m\n\u001b[1;32m 201\u001b[0m \u001b[38;5;124;03m with pandas dataframes.\u001b[39;00m\n\u001b[1;32m 202\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 241\u001b[0m \n\u001b[1;32m 242\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 244\u001b[0m \u001b[43mXddConverter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 245\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 246\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_overwrite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_overwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 247\u001b[0m \u001b[43m \u001b[49m\u001b[43mxdd_files\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxdd_files\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[43m \u001b[49m\u001b[43mid_subset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mid_subset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 249\u001b[0m \u001b[43m \u001b[49m\u001b[43mparallel_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparallel_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 250\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mconvert()\n", + "File \u001b[0;32m/usr/src/statemodify/statemodify/statemodify/xdd.py:73\u001b[0m, in \u001b[0;36mXddConverter.__init__\u001b[0;34m(self, output_path, allow_overwrite, xdd_files, id_subset, parallel_jobs)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_overwrite:\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_path\u001b[38;5;241m.\u001b[39mglob(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*.parquet\u001b[39m\u001b[38;5;124m\"\u001b[39m))) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m---> 73\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIOError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparquet files already exist in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m but allow_overwrite is False\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_path\u001b[38;5;241m.\u001b[39mglob(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*.parquet\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", + "\u001b[0;31mOSError\u001b[0m: parquet files already exist in /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/parquet_ddm/scenario/S0_1 but allow_overwrite is False" + ] + } + ], "source": [ "# set realization and sample\n", "realization = 1\n", @@ -393,62 +3740,58 @@ "\n", "# read RSP template\n", "with open(ddm_template_file) as template_obj:\n", + " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", + " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - "\n", + " \n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"DDM\": f\"../../input_files/sj2015B_{scenario}.ddm\"}\n", - "\n", + " \n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - "\n", + " \n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir_ddm, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - "\n", + " \n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}.rsp\")\n", - "\n", + " \n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - "\n", + " \n", " # construct simulated basin path\n", - " simulated_basin_path = os.path.join(\n", - " simulated_scenario_dir, f\"sj2015B_{scenario}\"\n", - " )\n", + " simulated_basin_path = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}\")\n", "\n", " # run StateMod\n", " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - "\n", - " # Save output to parquet files\n", - " print(\"creating parquet for \" + scenario)\n", - "\n", - " output_directory = os.path.join(parquet_dir_ddm + \"/scenario/\" + scenario)\n", - "\n", + " \n", + " #Save output to parquet files \n", + " print('creating parquet for ' + scenario)\n", + " \n", + " output_directory = os.path.join(parquet_dir_ddm+\"/scenario/\"+ scenario)\n", + " \n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - "\n", + " \n", " stm.xdd.convert_xdd(\n", " output_path=output_directory,\n", " allow_overwrite=False,\n", - " xdd_files=scenarios_dir_ddm\n", - " + \"/\"\n", - " + scenario\n", - " + \"/sj2015B_\"\n", - " + scenario\n", - " + \".xdd\",\n", - " id_subset=[\"2900501\", \"2900519\", \"2900555\"],\n", + " xdd_files=scenarios_dir_ddm + \"/\"+ scenario + \"/sj2015B_\"+scenario+\".xdd\",\n", + " id_subset=['2900501','2900519','2900555'],\n", " parallel_jobs=4,\n", - " )" + " preserve_string_dtype=False\n", + " )\n" ] }, { @@ -473,15 +3816,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "14c8d406-b4eb-4d7a-8b65-7f01047ba688", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.708511 ],\n", + " [1.36016225]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "output_directory = os.path.join(data_dir, \"input_files\")\n", - "sample_array = np.load(output_directory + \"/ddm_2-samples_scenario-1.npy\")\n", + "sample_array = np.load(output_directory+'/ddm_2-samples_scenario-1.npy')\n", "sample_array" ] }, @@ -495,48 +3850,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "bb7de22c-bce2-4b5a-8766-729b5e24cd34", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Read in raw parquet files\n", - "baseline = pd.read_parquet(\n", - " data_dir + \"/historic_shortages/sj2015B.parquet\", engine=\"pyarrow\"\n", - ")\n", - "SOW_1 = pd.read_parquet(\n", - " parquet_dir_ddm + \"/scenario/S0_1/sj2015B_S0_1.parquet\", engine=\"pyarrow\"\n", - ")\n", - "SOW_2 = pd.read_parquet(\n", - " parquet_dir_ddm + \"/scenario/S1_1/sj2015B_S1_1.parquet\", engine=\"pyarrow\"\n", - ")\n", + "baseline=pd.read_parquet(data_dir+'/historic_shortages/sj2015B.parquet',engine='pyarrow')\n", + "SOW_1=pd.read_parquet(parquet_dir_ddm+'/scenario/S0_1/sj2015B_S0_1.parquet',engine='pyarrow')\n", + "SOW_2=pd.read_parquet(parquet_dir_ddm+'/scenario/S1_1/sj2015B_S1_1.parquet',engine='pyarrow')\n", "\n", "# Subtract shortages with respect to the baseline\n", - "subset_df = pd.concat(\n", - " [\n", - " baseline[\"year\"],\n", - " baseline[\"shortage_total\"],\n", - " SOW_1[\"shortage_total\"],\n", - " SOW_2[\"shortage_total\"],\n", - " ],\n", - " axis=1,\n", - ")\n", - "subset_df = subset_df.set_axis([\"Year\", \"Baseline\", \"SOW_1\", \"SOW_2\"], axis=1)\n", - "subset_df[\"Baseline\"] = subset_df[\"Baseline\"].astype(float)\n", - "subset_df[\"SOW_1\"] = subset_df[\"SOW_1\"].astype(float)\n", - "subset_df[\"SOW_2\"] = subset_df[\"SOW_2\"].astype(float)\n", - "subset_df[\"Year\"] = subset_df[\"Year\"].astype(int)\n", - "subset_df[\"SOW_1_diff\"] = subset_df[\"SOW_1\"] - subset_df[\"Baseline\"]\n", - "subset_df[\"SOW_2_diff\"] = subset_df[\"SOW_2\"] - subset_df[\"Baseline\"]\n", - "\n", + "subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total'],SOW_2['shortage_total']],axis=1)\n", + "subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1','SOW_2'], axis=1)\n", + "subset_df['SOW_1_diff'] = subset_df['SOW_1']-subset_df['Baseline']\n", + "subset_df['SOW_2_diff'] = subset_df['SOW_2']-subset_df['Baseline']\n", "\n", "# Plot shortages\n", "fig, ax = plt.subplots()\n", "\n", - "ax.scatter(subset_df[\"Year\"], subset_df[\"SOW_1_diff\"], label=\"Decreased Demand\")\n", - "ax.scatter(subset_df[\"Year\"], subset_df[\"SOW_2_diff\"], label=\"Increased Demand\")\n", + "ax.scatter(subset_df['Year'], subset_df['SOW_1_diff'],label='Decreased Demand')\n", + "ax.scatter(subset_df['Year'], subset_df['SOW_2_diff'],label='Increased Demand')\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", @@ -570,7 +3927,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "3a05136d-2234-45b3-ad26-211664fef29a", "metadata": { "tags": [] @@ -581,9 +3938,11 @@ "setup_dict = {\n", " # ids can either be 'struct' or 'id' values\n", " \"ids\": [\"2900501\"],\n", + "\n", " # turn id on or off completely or for a given period\n", " # if 0 = off, 1 = on, YYYY = on for years >= YYYY, -YYYY = off for years > YYYY; see file header\n", " \"on_off\": [1],\n", + "\n", " # apply rank of administrative order where 0 is lowest (senior) and n is highest (junior); None is no change\n", " \"admin\": [1],\n", "}\n", @@ -626,8 +3985,8 @@ " data_specification_file=None,\n", " min_bound_value=-0.5,\n", " max_bound_value=1.5,\n", - " save_sample=True,\n", - ")" + " save_sample=True\n", + ")\n" ] }, { @@ -640,13 +3999,1449 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "0cb6d099-8aa4-4d52-b5ee-94ab8a254d4a", "metadata": { "scrolled": true, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running: S0_1\n", + " Startup log file for messages to this point: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddr/S0_1/sj2015B_S0_1.rsp \n", + " Closing startup log file: statem.log\n", + " Opening dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddr/S0_1/sj2015B_S0_1.log \n", + "________________________________________________________________________\n", + "\n", + " StateMod \n", + " State of Colorado - Water Supply Planning Model \n", + "\n", + " Version: 17.0.3 \n", + " Last revision date: 2021/09/12\n", + "\n", + "________________________________________________________________________\n", + " \n", + " Subroutine Execut\n", + " Subroutine Datinp\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Control File (*.ctl) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Network File (*.rin)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Reservoir Station File (*.res)\n", + " Subroutine GetRes\n", + "\n", + " GetRes; Reservoir Station File (*.res) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Diversion Station File (*.dds)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Station File (*.ris)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Instream Flow Station File (*.ifs) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Well Station File (*.wes) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Plan Station File (*.pln) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Gage File (*.rig) \n", + " Subroutine Riginp\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Instream Flow Right File (*.ifr) 20 1 98\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Reservoir Right File (*.rer) \n", + "\n", + "________________________________________________________________________\n", + " Riginp; Direct Diversion Right File (*.ddr) \n", + "\n", + "________________________________________________________________________\n", + " Riginp; Direct Diversion Right File (*.ddr) \n", + "\n", + "________________________________________________________________________\n", + " Oprinp; Operational Right File (*.opr) \n", + "\n", + "________________________________________________________________________\n", + " Mdainp; Instream flow Demand file - Annual (*.ifa) \n", + " Subroutine Execut\n", + "\n", + "+ Execut; Year 1908 Month OCT \n", + "+ Execut; Year 1908 Month NOV \n", + "+ Execut; Year 1908 Month DEC \n", + "+ Execut; Year 1909 Month JAN \n", + "+ Execut; Year 1909 Month FEB \n", + "+ Execut; Year 1909 Month MAR \n", + "+ Execut; Year 1909 Month APR \n", + "+ Execut; Year 1909 Month MAY \n", + "+ Execut; Year 1909 Month JUN \n", + "+ Execut; Year 1909 Month JUL \n", + "+ Execut; Year 1909 Month AUG \n", + "+ Execut; Year 1909 Month SEP \n", + "+ Execut; Year 1909 Month OCT \n", + "+ Execut; Year 1909 Month NOV \n", + "+ Execut; Year 1909 Month DEC \n", + "+ Execut; Year 1910 Month JAN \n", + "+ Execut; Year 1910 Month FEB \n", + "+ Execut; Year 1910 Month MAR \n", + "+ Execut; Year 1910 Month APR \n", + "+ Execut; Year 1910 Month MAY \n", + "+ Execut; Year 1910 Month JUN \n", + "+ Execut; Year 1910 Month JUL \n", + "+ Execut; Year 1910 Month AUG \n", + "+ Execut; Year 1910 Month SEP \n", + "+ Execut; Year 1910 Month OCT \n", + "+ Execut; Year 1910 Month NOV \n", + "+ Execut; Year 1910 Month DEC \n", + "+ Execut; Year 1911 Month JAN \n", + "+ Execut; Year 1911 Month FEB \n", + "+ Execut; Year 1911 Month MAR \n", + "+ Execut; Year 1911 Month APR \n", + "+ Execut; Year 1911 Month MAY \n", + "+ Execut; Year 1911 Month JUN \n", + "+ Execut; Year 1911 Month JUL \n", + "+ Execut; Year 1911 Month AUG \n", + "+ Execut; Year 1911 Month SEP \n", + "+ Execut; Year 1911 Month OCT \n", + 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Month MAR \n", + "+ Execut; Year 2008 Month APR \n", + "+ Execut; Year 2008 Month MAY \n", + "+ Execut; Year 2008 Month JUN \n", + "+ Execut; Year 2008 Month JUL \n", + "+ Execut; Year 2008 Month AUG \n", + "+ Execut; Year 2008 Month SEP \n", + "+ Execut; Year 2008 Month OCT \n", + "+ Execut; Year 2008 Month NOV \n", + "+ Execut; Year 2008 Month DEC \n", + "+ Execut; Year 2009 Month JAN \n", + "+ Execut; Year 2009 Month FEB \n", + "+ Execut; Year 2009 Month MAR \n", + "+ Execut; Year 2009 Month APR \n", + "+ Execut; Year 2009 Month MAY \n", + "+ Execut; Year 2009 Month JUN \n", + "+ Execut; Year 2009 Month JUL \n", + "+ Execut; Year 2009 Month AUG \n", + "+ Execut; Year 2009 Month SEP \n", + "+ Execut; Year 2009 Month OCT \n", + "+ Execut; Year 2009 Month NOV \n", + "+ Execut; Year 2009 Month DEC \n", + "+ Execut; Year 2010 Month JAN \n", + "+ Execut; Year 2010 Month FEB \n", + "+ Execut; Year 2010 Month MAR \n", + "+ Execut; Year 2010 Month APR \n", + "+ Execut; Year 2010 Month MAY \n", + "+ Execut; Year 2010 Month JUN \n", + "+ Execut; Year 2010 Month JUL \n", + "+ Execut; Year 2010 Month AUG \n", + "+ Execut; Year 2010 Month SEP \n", + "+ Execut; Year 2010 Month OCT \n", + "+ Execut; Year 2010 Month NOV \n", + "+ Execut; Year 2010 Month DEC \n", + "+ Execut; Year 2011 Month JAN \n", + "+ Execut; Year 2011 Month FEB \n", + "+ Execut; Year 2011 Month MAR \n", + "+ Execut; Year 2011 Month APR \n", + "+ Execut; Year 2011 Month MAY \n", + "+ Execut; Year 2011 Month JUN \n", + "+ Execut; Year 2011 Month JUL \n", + "+ Execut; Year 2011 Month AUG \n", + "+ Execut; Year 2011 Month SEP \n", + "+ Execut; Year 2011 Month OCT \n", + "+ Execut; Year 2011 Month NOV \n", + "+ Execut; Year 2011 Month DEC \n", + "+ Execut; Year 2012 Month JAN \n", + "+ Execut; Year 2012 Month FEB \n", + "+ Execut; Year 2012 Month MAR \n", + "+ Execut; Year 2012 Month APR \n", + "+ Execut; Year 2012 Month MAY \n", + "+ Execut; Year 2012 Month JUN \n", + "+ Execut; Year 2012 Month JUL \n", + "+ Execut; Year 2012 Month AUG \n", + "+ Execut; Year 2012 Month SEP \n", + "+ Execut; Year 2012 Month OCT \n", + "+ Execut; Year 2012 Month NOV \n", + "+ Execut; Year 2012 Month DEC \n", + "+ Execut; Year 2013 Month JAN \n", + "+ Execut; Year 2013 Month FEB \n", + "+ Execut; Year 2013 Month MAR \n", + "+ Execut; Year 2013 Month APR \n", + "+ Execut; Year 2013 Month MAY \n", + "+ Execut; Year 2013 Month JUN \n", + "+ Execut; Year 2013 Month JUL \n", + "+ Execut; Year 2013 Month AUG \n", + "+ Execut; Year 2013 Month SEP \n", + "\n", + "________________________________________________________________________\n", + " Execut; On Year 1966 Month JUN Day 1\n", + " The maximum number of reoperations 221\n", + "\n", + "________________________________________________________________________\n", + " Execut; Writing reports\n", + "\n", + "________________________________________________________________________\n", + " Execut; \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutRes \n", + "+ Printing Reservoir Summary 1 of 33; or 3. % Complete\n", + "+ Printing Reservoir Summary 26 of 33; or 79. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutDivW \n", + "+ Printing Diversion & Stream Summary 1 of 558; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 26 of 558; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 51 of 558; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 76 of 558; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 101 of 558; or 18. % Complete\n", + "+ Printing Diversion & Stream Summary 126 of 558; or 23. % Complete\n", + "+ Printing Diversion & Stream Summary 151 of 558; or 27. % Complete\n", + "+ Printing Diversion & Stream Summary 176 of 558; or 32. % Complete\n", + "+ Printing Diversion & Stream Summary 201 of 558; or 36. % Complete\n", + "+ Printing Diversion & Stream Summary 226 of 558; or 41. % Complete\n", + "+ Printing Diversion & Stream Summary 251 of 558; or 45. % Complete\n", + "+ Printing Diversion & Stream Summary 276 of 558; or 49. % Complete\n", + "+ Printing Diversion & Stream Summary 301 of 558; or 54. % Complete\n", + "+ Printing Diversion & Stream Summary 326 of 558; or 58. % Complete\n", + "+ Printing Diversion & Stream Summary 351 of 558; or 63. % Complete\n", + "+ Printing Diversion & Stream Summary 376 of 558; or 67. % Complete\n", + "+ Printing Diversion & Stream Summary 401 of 558; or 72. % Complete\n", + "+ Printing Diversion & Stream Summary 426 of 558; or 76. % Complete\n", + "+ Printing Diversion & Stream Summary 451 of 558; or 81. % Complete\n", + "+ Printing Diversion & Stream Summary 476 of 558; or 85. % Complete\n", + "+ Printing Diversion & Stream Summary 501 of 558; or 90. % Complete\n", + "+ Printing Diversion & Stream Summary 526 of 558; or 94. % Complete\n", + "+ Printing Diversion & Stream Summary 551 of 558; or 99. % Complete\n", + "+ Printing Diversion & Stream Summary 558 of 558; or 100. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutOpr \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutXss \n", + "+ Printing Structure Summary (*.xss) 0 of 342; or 0. % Complete\n", + "+ Printing Structure Summary (*.xss) 25 of 342; or 8. % Complete\n", + "+ Printing Structure Summary (*.xss) 50 of 342; or 15. % Complete\n", + "+ Printing Structure Summary (*.xss) 75 of 342; or 22. % Complete\n", + "+ Printing Structure Summary (*.xss) 100 of 342; or 30. % Complete\n", + "+ Printing Structure Summary (*.xss) 125 of 342; or 37. % Complete\n", + "+ Printing Structure Summary (*.xss) 150 of 342; or 44. % Complete\n", + "+ Printing Structure Summary (*.xss) 175 of 342; or 51. % Complete\n", + "+ Printing Structure Summary (*.xss) 200 of 342; or 59. % Complete\n", + "+ Printing Structure Summary (*.xss) 225 of 342; or 66. % Complete\n", + "+ Printing Structure Summary (*.xss) 250 of 342; or 73. % Complete\n", + "+ Printing Structure Summary (*.xss) 275 of 342; or 81. % Complete\n", + "+ Printing Structure Summary (*.xss) 300 of 342; or 88. % Complete\n", + "+ Printing Structure Summary (*.xss) 325 of 342; or 95. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine Outifr\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutPln \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutWW \n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Run output files are:\n", + " \n", + " Diversion output: *.xdd\n", + " Reservoir output: *.xre\n", + " Operating Rule Info: *.xop\n", + " Instream Reach Info: *.xir\n", + " Structure Summary: *.xss\n", + " Call (Control) Summary: *.xca\n", + " Plan Output: *.xpl\n", + " WWSP Output: *.xww\n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Termination\n", + " Statem; See detailed messages in dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddr/S0_1/sj2015B_S0_1.log \n", + " Stop 0\n", + "creating parquet for S0_1\n" + ] + }, + { + "ename": "OSError", + "evalue": "parquet files already exist in /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/parquet_ddr/scenario/S0_1 but allow_overwrite is False", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[13], line 51\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(output_directory):\n\u001b[1;32m 49\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(output_directory)\n\u001b[0;32m---> 51\u001b[0m \u001b[43mstm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxdd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_xdd\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_directory\u001b[49m\u001b[43m,\u001b[49m\u001b[43mallow_overwrite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mxdd_files\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscenarios_dir_ddr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mscenario\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/sj2015B_\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mscenario\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m.xdd\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mid_subset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m2900501\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mparallel_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/usr/src/statemodify/statemodify/statemodify/xdd.py:244\u001b[0m, in \u001b[0;36mconvert_xdd\u001b[0;34m(output_path, allow_overwrite, xdd_files, id_subset, parallel_jobs)\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconvert_xdd\u001b[39m(\n\u001b[1;32m 193\u001b[0m \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m 194\u001b[0m output_path: Union[\u001b[38;5;28mstr\u001b[39m, Path] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./output\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 198\u001b[0m parallel_jobs: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m,\n\u001b[1;32m 199\u001b[0m ):\n\u001b[1;32m 200\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Convert StateMod output .xdd files to compressed, columnar .parquet files which easily interoperate\u001b[39;00m\n\u001b[1;32m 201\u001b[0m \u001b[38;5;124;03m with pandas dataframes.\u001b[39;00m\n\u001b[1;32m 202\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 241\u001b[0m \n\u001b[1;32m 242\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 244\u001b[0m \u001b[43mXddConverter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 245\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 246\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_overwrite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_overwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 247\u001b[0m \u001b[43m \u001b[49m\u001b[43mxdd_files\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxdd_files\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[43m \u001b[49m\u001b[43mid_subset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mid_subset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 249\u001b[0m \u001b[43m \u001b[49m\u001b[43mparallel_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparallel_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 250\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mconvert()\n", + "File \u001b[0;32m/usr/src/statemodify/statemodify/statemodify/xdd.py:73\u001b[0m, in \u001b[0;36mXddConverter.__init__\u001b[0;34m(self, output_path, allow_overwrite, xdd_files, id_subset, parallel_jobs)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_overwrite:\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_path\u001b[38;5;241m.\u001b[39mglob(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*.parquet\u001b[39m\u001b[38;5;124m\"\u001b[39m))) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m---> 73\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIOError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparquet files already exist in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m but allow_overwrite is False\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_path\u001b[38;5;241m.\u001b[39mglob(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*.parquet\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", + "\u001b[0;31mOSError\u001b[0m: parquet files already exist in /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/parquet_ddr/scenario/S0_1 but allow_overwrite is False" + ] + } + ], "source": [ "# set realization and sample\n", "realization = 1\n", @@ -654,62 +5449,58 @@ "\n", "# read RSP template\n", "with open(ddr_template_file) as template_obj:\n", + " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", + " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - "\n", + " \n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"DDR\": f\"../../input_files/sj2015B_{scenario}.ddr\"}\n", - "\n", + " \n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - "\n", + " \n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir_ddr, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - "\n", + " \n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}.rsp\")\n", - "\n", + " \n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - "\n", + " \n", " # construct simulated basin path\n", - " simulated_basin_path = os.path.join(\n", - " simulated_scenario_dir, f\"sj2015B_{scenario}\"\n", - " )\n", + " simulated_basin_path = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}\")\n", "\n", " # run StateMod\n", " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - "\n", - " # Save output to parquet files\n", - " print(\"creating parquet for \" + scenario)\n", - "\n", - " output_directory = os.path.join(parquet_dir_ddr + \"/scenario/\" + scenario)\n", - "\n", + " \n", + " #Save output to parquet files \n", + " print('creating parquet for ' + scenario)\n", + " \n", + " output_directory = os.path.join(parquet_dir_ddr+\"/scenario/\"+ scenario)\n", + " \n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - "\n", + " \n", " stm.xdd.convert_xdd(\n", " output_path=output_directory,\n", " allow_overwrite=False,\n", - " xdd_files=scenarios_dir_ddr\n", - " + \"/\"\n", - " + scenario\n", - " + \"/sj2015B_\"\n", - " + scenario\n", - " + \".xdd\",\n", - " id_subset=[\"2900501\"],\n", + " xdd_files=scenarios_dir_ddr + \"/\"+ scenario + \"/sj2015B_\"+scenario+\".xdd\",\n", + " id_subset=['2900501'],\n", " parallel_jobs=2,\n", - " )" + " preserve_string_dtype=False\n", + " )\n" ] }, { @@ -722,35 +5513,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "c00fc3c1-cfda-465f-9220-8cc884cb05fd", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Change in Shortages from the Baseline')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Read in raw parquet files\n", - "baseline = pd.read_parquet(\n", - " data_dir + \"/historic_shortages/sj2015B.parquet\", engine=\"pyarrow\"\n", - ")\n", - "SOW_1 = pd.read_parquet(\n", - " parquet_dir_ddr + \"/scenario/S0_1/sj2015B_S0_1.parquet\", engine=\"pyarrow\"\n", - ")\n", + "baseline=pd.read_parquet(data_dir+'/historic_shortages/sj2015B.parquet',engine='pyarrow')\n", + "SOW_1=pd.read_parquet(parquet_dir_ddr+ '/scenario/S0_1/sj2015B_S0_1.parquet',engine='pyarrow')\n", "\n", "# Subtract shortages with respect to the baseline\n", - "subset_df = pd.concat(\n", - " [baseline[\"year\"], baseline[\"shortage_total\"], SOW_1[\"shortage_total\"]], axis=1\n", - ")\n", - "subset_df = subset_df.set_axis([\"Year\", \"Baseline\", \"SOW_1\"], axis=1)\n", - "subset_df[\"Baseline\"] = subset_df[\"Baseline\"].astype(float)\n", - "subset_df[\"SOW_1\"] = subset_df[\"SOW_1\"].astype(float)\n", - "subset_df[\"Year\"] = subset_df[\"Year\"].astype(int)\n", - "subset_df[\"diff\"] = subset_df[\"SOW_1\"] - subset_df[\"Baseline\"]\n", + "subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total']],axis=1)\n", + "subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1'], axis=1)\n", + "subset_df['diff']=subset_df['SOW_1']-subset_df['Baseline']\n", "\n", "# Plot shortages\n", "fig, ax = plt.subplots()\n", "\n", - "ax.scatter(subset_df[\"Year\"], subset_df[\"diff\"])\n", + "ax.scatter(subset_df['Year'], subset_df['diff'])\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", diff --git a/notebooks/N2_Evaporation_File_Modification.ipynb b/notebooks/N2_Evaporation_File_Modification.ipynb index 924ee6a..52d2e91 100644 --- a/notebooks/N2_Evaporation_File_Modification.ipynb +++ b/notebooks/N2_Evaporation_File_Modification.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "13aa645c-aeb0-4fba-b3ae-39e3b4c331ad", "metadata": { "tags": [] @@ -47,13 +47,12 @@ "import logging\n", "import os\n", "import pickle\n", - "import subprocess\n", "from string import Template\n", + "import subprocess\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd\n", - "\n", + "import pandas as pd \n", "import statemodify as stm" ] }, @@ -76,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "4f42b539", "metadata": { "tags": [] @@ -97,7 +96,11 @@ "\n", "# data directory and root name for the target basin\n", "data_dir = os.path.join(\n", - " home_dir, \"data\", \"gm2015_StateMod_modified\", \"gm2015_StateMod_modified\", \"StateMod\"\n", + " home_dir,\n", + " \"data\",\n", + " \"gm2015_StateMod_modified\",\n", + " \"gm2015_StateMod_modified\",\n", + " \"StateMod\"\n", ")\n", "\n", "# directory to the target basin input files with root name for the basin\n", @@ -107,21 +110,1467 @@ "scenarios_dir = os.path.join(data_dir, \"scenarios\")\n", "\n", "# path to eva template file\n", - "eva_template_file = os.path.join(home_dir, \"data\", \"gm2015B_template_eva.rsp\")" + "eva_template_file = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"gm2015B_template_eva.rsp\"\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "e4d8fefd-298f-4474-a17f-c66e4e7f3ebd", "metadata": { "scrolled": true, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Startup log file for messages to this point: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015B.rsp \n", + " Closing startup log file: statem.log\n", + " Opening dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015B.log \n", + "________________________________________________________________________\n", + "\n", + " StateMod \n", + " State of Colorado - Water Supply Planning Model \n", + "\n", + " Version: 17.0.3 \n", + " Last revision date: 2021/09/12\n", + "\n", + "________________________________________________________________________\n", + " \n", + " Subroutine Execut\n", + " Subroutine Datinp\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Control File (*.ctl) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Network File (*.rin)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Reservoir Station File (*.res)\n", + " Subroutine GetRes\n", + "\n", + " GetRes; Reservoir Station File (*.res) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Diversion Station File (*.dds)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Station File (*.ris)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Instream Flow Station File (*.ifs) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Well Station File (*.wes) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Plan Station File (*.pln) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Gage File (*.rig) \n", + " Subroutine Riginp\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Instream Flow Right File (*.ifr) 20 1 98\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Reservoir Right File (*.rer) \n", + "\n", + "________________________________________________________________________\n", + " Riginp; Direct Diversion Right File (*.ddr) \n", + "\n", + "________________________________________________________________________\n", + " Oprinp; Operational Right File (*.opr) \n", + "\n", + "________________________________________________________________________\n", + " Mdainp; Instream flow Demand file - Annual (*.ifa) \n", + " Subroutine Execut\n", + "\n", + "+ Execut; Year 1908 Month OCT \n", + "+ Execut; Year 1908 Month NOV \n", + "+ Execut; 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Year 1974 Month MAR \n", + "+ Execut; Year 1974 Month APR \n", + "+ Execut; Year 1974 Month MAY \n", + "+ Execut; Year 1974 Month JUN \n", + "+ Execut; Year 1974 Month JUL \n", + "+ Execut; Year 1974 Month AUG \n", + "+ Execut; Year 1974 Month SEP \n", + "+ Execut; Year 1974 Month OCT \n", + "+ Execut; Year 1974 Month NOV \n", + "+ Execut; Year 1974 Month DEC \n", + "+ Execut; Year 1975 Month JAN \n", + "+ Execut; Year 1975 Month FEB \n", + "+ Execut; Year 1975 Month MAR \n", + "+ Execut; Year 1975 Month APR \n", + "+ Execut; Year 1975 Month MAY \n", + "+ Execut; Year 1975 Month JUN \n", + "+ Execut; Year 1975 Month JUL \n", + "+ Execut; Year 1975 Month AUG \n", + "+ Execut; Year 1975 Month SEP \n", + "+ Execut; Year 1975 Month OCT \n", + "+ Execut; Year 1975 Month NOV \n", + "+ Execut; Year 1975 Month DEC \n", + "+ Execut; Year 1976 Month JAN \n", + "+ Execut; Year 1976 Month FEB \n", + "+ Execut; Year 1976 Month MAR \n", + "+ Execut; Year 1976 Month APR \n", + "+ Execut; Year 1976 Month MAY \n", + "+ Execut; Year 1976 Month JUN \n", + "+ Execut; Year 1976 Month JUL \n", + "+ Execut; Year 1976 Month AUG \n", + "+ Execut; Year 1976 Month SEP \n", + "+ Execut; Year 1976 Month OCT \n", + "+ Execut; Year 1976 Month NOV \n", + "+ Execut; Year 1976 Month DEC \n", + "+ Execut; Year 1977 Month JAN \n", + "+ Execut; Year 1977 Month FEB \n", + "+ Execut; Year 1977 Month MAR \n", + "+ Execut; Year 1977 Month APR \n", + "+ Execut; Year 1977 Month MAY \n", + "+ Execut; Year 1977 Month JUN \n", + "+ Execut; Year 1977 Month JUL \n", + "+ Execut; Year 1977 Month AUG \n", + "+ Execut; Year 1977 Month SEP \n", + "+ Execut; Year 1977 Month OCT \n", + "+ Execut; Year 1977 Month NOV \n", + "+ Execut; Year 1977 Month DEC \n", + "+ Execut; Year 1978 Month JAN \n", + "+ Execut; Year 1978 Month FEB \n", + "+ Execut; Year 1978 Month MAR \n", + "+ Execut; Year 1978 Month APR \n", + "+ Execut; Year 1978 Month MAY \n", + "+ Execut; Year 1978 Month JUN \n", + "+ Execut; Year 1978 Month JUL \n", + "+ Execut; Year 1978 Month AUG \n", + "+ Execut; Year 1978 Month SEP \n", + "+ Execut; Year 1978 Month OCT \n", + "+ Execut; Year 1978 Month NOV \n", + "+ Execut; Year 1978 Month DEC \n", + "+ Execut; Year 1979 Month JAN \n", + "+ Execut; Year 1979 Month FEB \n", + "+ Execut; Year 1979 Month MAR \n", + "+ Execut; Year 1979 Month APR \n", + "+ Execut; Year 1979 Month MAY \n", + "+ Execut; Year 1979 Month JUN \n", + "+ Execut; Year 1979 Month JUL \n", + "+ Execut; Year 1979 Month AUG \n", + "+ Execut; Year 1979 Month SEP \n", + "+ Execut; Year 1979 Month OCT \n", + "+ Execut; Year 1979 Month NOV \n", + "+ Execut; Year 1979 Month DEC \n", + "+ Execut; Year 1980 Month JAN \n", + "+ Execut; Year 1980 Month FEB \n", + "+ Execut; Year 1980 Month MAR \n", + "+ Execut; Year 1980 Month APR \n", + "+ Execut; Year 1980 Month MAY \n", + "+ Execut; Year 1980 Month JUN \n", + "+ Execut; Year 1980 Month JUL \n", + "+ Execut; Year 1980 Month AUG \n", + "+ Execut; Year 1980 Month SEP \n", + "+ Execut; Year 1980 Month OCT \n", + "+ Execut; Year 1980 Month NOV \n", + "+ Execut; Year 1980 Month DEC \n", + "+ Execut; Year 1981 Month JAN \n", + "+ Execut; Year 1981 Month FEB \n", + "+ Execut; Year 1981 Month MAR \n", + "+ Execut; Year 1981 Month APR \n", + "+ Execut; Year 1981 Month MAY \n", + "+ Execut; Year 1981 Month JUN \n", + "+ Execut; Year 1981 Month JUL \n", + "+ Execut; Year 1981 Month AUG \n", + "+ Execut; Year 1981 Month SEP \n", + "+ Execut; Year 1981 Month OCT \n", + "+ Execut; Year 1981 Month NOV \n", + "+ Execut; Year 1981 Month DEC \n", + "+ Execut; Year 1982 Month JAN \n", + "+ Execut; Year 1982 Month FEB \n", + "+ Execut; Year 1982 Month MAR \n", + "+ Execut; Year 1982 Month APR \n", + "+ Execut; Year 1982 Month MAY \n", + "+ Execut; Year 1982 Month JUN \n", + "+ Execut; Year 1982 Month JUL \n", + "+ Execut; Year 1982 Month AUG \n", + "+ Execut; Year 1982 Month SEP \n", + "+ Execut; Year 1982 Month OCT \n", + "+ Execut; Year 1982 Month NOV \n", + "+ Execut; Year 1982 Month DEC \n", + "+ Execut; Year 1983 Month JAN \n", + "+ Execut; Year 1983 Month FEB \n", + "+ Execut; Year 1983 Month MAR \n", + "+ Execut; Year 1983 Month APR \n", + "+ Execut; Year 1983 Month MAY \n", + "+ Execut; Year 1983 Month JUN \n", + "+ Execut; Year 1983 Month JUL \n", + "+ Execut; Year 1983 Month AUG \n", + "+ Execut; Year 1983 Month SEP \n", + "+ Execut; Year 1983 Month OCT \n", + "+ Execut; Year 1983 Month NOV \n", + "+ Execut; Year 1983 Month DEC \n", + "+ Execut; Year 1984 Month JAN \n", + "+ Execut; Year 1984 Month FEB \n", + "+ Execut; Year 1984 Month MAR \n", + "+ Execut; Year 1984 Month APR \n", + "+ Execut; Year 1984 Month MAY \n", + "+ Execut; Year 1984 Month JUN \n", + "+ Execut; Year 1984 Month JUL \n", + "+ Execut; Year 1984 Month AUG \n", + "+ Execut; Year 1984 Month SEP \n", + "+ Execut; Year 1984 Month OCT \n", + "+ Execut; Year 1984 Month NOV \n", + "+ Execut; Year 1984 Month DEC \n", + "+ Execut; Year 1985 Month JAN \n", + "+ Execut; Year 1985 Month FEB \n", + "+ Execut; Year 1985 Month MAR \n", + "+ Execut; Year 1985 Month APR \n", + "+ Execut; Year 1985 Month MAY \n", + "+ Execut; Year 1985 Month JUN \n", + "+ Execut; Year 1985 Month JUL \n", + "+ Execut; Year 1985 Month AUG \n", + "+ Execut; Year 1985 Month SEP \n", + "+ Execut; Year 1985 Month OCT \n", + "+ Execut; Year 1985 Month NOV \n", + "+ Execut; Year 1985 Month DEC \n", + "+ Execut; Year 1986 Month JAN \n", + "+ Execut; Year 1986 Month FEB \n", + "+ Execut; Year 1986 Month MAR \n", + "+ Execut; Year 1986 Month APR \n", + "+ Execut; Year 1986 Month MAY \n", + "+ Execut; Year 1986 Month JUN \n", + "+ Execut; Year 1986 Month JUL \n", + "+ Execut; Year 1986 Month AUG \n", + "+ Execut; Year 1986 Month SEP \n", + "+ Execut; Year 1986 Month OCT \n", + "+ Execut; Year 1986 Month NOV \n", + "+ Execut; Year 1986 Month DEC \n", + "+ Execut; Year 1987 Month JAN \n", + "+ Execut; Year 1987 Month FEB \n", + "+ Execut; Year 1987 Month MAR \n", + "+ Execut; Year 1987 Month APR \n", + "+ Execut; Year 1987 Month MAY \n", + "+ Execut; Year 1987 Month JUN \n", + "+ Execut; Year 1987 Month JUL \n", + "+ Execut; Year 1987 Month AUG \n", + "+ Execut; Year 1987 Month SEP \n", + "+ Execut; Year 1987 Month OCT \n", + "+ Execut; Year 1987 Month NOV \n", + "+ Execut; Year 1987 Month DEC \n", + "+ Execut; Year 1988 Month JAN \n", + "+ Execut; Year 1988 Month FEB \n", + "+ Execut; Year 1988 Month MAR \n", + "+ Execut; Year 1988 Month APR \n", + "+ Execut; Year 1988 Month MAY \n", + "+ Execut; Year 1988 Month JUN \n", + "+ Execut; Year 1988 Month JUL \n", + "+ Execut; Year 1988 Month AUG \n", + "+ Execut; Year 1988 Month SEP \n", + "+ Execut; Year 1988 Month OCT \n", + "+ Execut; Year 1988 Month NOV \n", + "+ Execut; Year 1988 Month DEC \n", + "+ Execut; Year 1989 Month JAN \n", + "+ Execut; Year 1989 Month FEB \n", + "+ Execut; Year 1989 Month MAR \n", + "+ Execut; Year 1989 Month APR \n", + "+ Execut; Year 1989 Month MAY \n", + "+ Execut; Year 1989 Month JUN \n", + "+ Execut; Year 1989 Month JUL \n", + "+ Execut; Year 1989 Month AUG \n", + "+ Execut; Year 1989 Month SEP \n", + "+ Execut; Year 1989 Month OCT \n", + "+ Execut; Year 1989 Month NOV \n", + "+ Execut; Year 1989 Month DEC \n", + "+ Execut; Year 1990 Month JAN \n", + "+ Execut; Year 1990 Month FEB \n", + "+ Execut; Year 1990 Month MAR \n", + "+ Execut; Year 1990 Month APR \n", + "+ Execut; Year 1990 Month MAY \n", + "+ Execut; Year 1990 Month JUN \n", + "+ Execut; Year 1990 Month JUL \n", + "+ Execut; Year 1990 Month AUG \n", + "+ Execut; Year 1990 Month SEP \n", + "+ Execut; Year 1990 Month OCT \n", + "+ Execut; Year 1990 Month NOV \n", + "+ Execut; Year 1990 Month DEC \n", + "+ Execut; Year 1991 Month JAN \n", + "+ Execut; Year 1991 Month FEB \n", + "+ Execut; Year 1991 Month MAR \n", + "+ Execut; Year 1991 Month APR \n", + "+ Execut; Year 1991 Month MAY \n", + "+ Execut; Year 1991 Month JUN \n", + "+ Execut; Year 1991 Month JUL \n", + "+ Execut; Year 1991 Month AUG \n", + "+ Execut; Year 1991 Month SEP \n", + "+ Execut; Year 1991 Month OCT \n", + "+ Execut; Year 1991 Month NOV \n", + "+ Execut; Year 1991 Month DEC \n", + "+ Execut; Year 1992 Month JAN \n", + "+ Execut; Year 1992 Month FEB \n", + "+ Execut; Year 1992 Month MAR \n", + "+ Execut; Year 1992 Month APR \n", + "+ Execut; Year 1992 Month MAY \n", + "+ Execut; Year 1992 Month JUN \n", + "+ Execut; Year 1992 Month JUL \n", + "+ Execut; Year 1992 Month AUG \n", + "+ Execut; Year 1992 Month SEP \n", + "+ Execut; Year 1992 Month OCT \n", + "+ Execut; Year 1992 Month NOV \n", + "+ Execut; Year 1992 Month DEC \n", + "+ Execut; Year 1993 Month JAN \n", + "+ Execut; Year 1993 Month FEB \n", + "+ Execut; Year 1993 Month MAR \n", + "+ Execut; Year 1993 Month APR \n", + "+ Execut; Year 1993 Month MAY \n", + "+ Execut; Year 1993 Month JUN \n", + "+ Execut; Year 1993 Month JUL \n", + "+ Execut; Year 1993 Month AUG \n", + "+ Execut; Year 1993 Month SEP \n", + "+ Execut; Year 1993 Month OCT \n", + "+ Execut; Year 1993 Month NOV \n", + "+ Execut; Year 1993 Month DEC \n", + "+ Execut; Year 1994 Month JAN \n", + "+ Execut; Year 1994 Month FEB \n", + "+ Execut; Year 1994 Month MAR \n", + "+ Execut; Year 1994 Month APR \n", + "+ Execut; Year 1994 Month MAY \n", + "+ Execut; Year 1994 Month JUN \n", + "+ Execut; Year 1994 Month JUL \n", + "+ Execut; Year 1994 Month AUG \n", + "+ Execut; Year 1994 Month SEP \n", + "+ Execut; Year 1994 Month OCT \n", + "+ Execut; Year 1994 Month NOV \n", + "+ Execut; Year 1994 Month DEC \n", + "+ Execut; Year 1995 Month JAN \n", + "+ Execut; Year 1995 Month FEB \n", + "+ Execut; Year 1995 Month MAR \n", + "+ Execut; Year 1995 Month APR \n", + "+ Execut; Year 1995 Month MAY \n", + "+ Execut; Year 1995 Month JUN \n", + "+ Execut; Year 1995 Month JUL \n", + "+ Execut; Year 1995 Month AUG \n", + "+ Execut; Year 1995 Month SEP \n", + "+ Execut; Year 1995 Month OCT \n", + "+ Execut; Year 1995 Month NOV \n", + "+ Execut; Year 1995 Month DEC \n", + "+ Execut; Year 1996 Month JAN \n", + "+ Execut; Year 1996 Month FEB \n", + "+ Execut; Year 1996 Month MAR \n", + "+ Execut; Year 1996 Month APR \n", + "+ Execut; Year 1996 Month MAY \n", + "+ Execut; Year 1996 Month JUN \n", + "+ Execut; Year 1996 Month JUL \n", + "+ Execut; Year 1996 Month AUG \n", + "+ Execut; Year 1996 Month SEP \n", + "+ Execut; Year 1996 Month OCT \n", + "+ Execut; Year 1996 Month NOV \n", + "+ Execut; Year 1996 Month DEC \n", + "+ Execut; Year 1997 Month JAN \n", + "+ Execut; Year 1997 Month FEB \n", + "+ Execut; Year 1997 Month MAR \n", + "+ Execut; Year 1997 Month APR \n", + "+ Execut; Year 1997 Month MAY \n", + "+ Execut; Year 1997 Month JUN \n", + "+ Execut; Year 1997 Month JUL \n", + "+ Execut; Year 1997 Month AUG \n", + "+ Execut; Year 1997 Month SEP \n", + "+ Execut; Year 1997 Month OCT \n", + "+ Execut; Year 1997 Month NOV \n", + "+ Execut; Year 1997 Month DEC \n", + "+ Execut; Year 1998 Month JAN \n", + "+ Execut; Year 1998 Month FEB \n", + "+ Execut; Year 1998 Month MAR \n", + "+ Execut; Year 1998 Month APR \n", + "+ Execut; Year 1998 Month MAY \n", + "+ Execut; Year 1998 Month JUN \n", + "+ Execut; Year 1998 Month JUL \n", + "+ Execut; Year 1998 Month AUG \n", + "+ Execut; Year 1998 Month SEP \n", + "+ Execut; Year 1998 Month OCT \n", + "+ Execut; Year 1998 Month NOV \n", + "+ Execut; Year 1998 Month DEC \n", + "+ Execut; Year 1999 Month JAN \n", + "+ Execut; Year 1999 Month FEB \n", + "+ Execut; Year 1999 Month MAR \n", + "+ Execut; Year 1999 Month APR \n", + "+ Execut; Year 1999 Month MAY \n", + "+ Execut; Year 1999 Month JUN \n", + "+ Execut; Year 1999 Month JUL \n", + "+ Execut; Year 1999 Month AUG \n", + "+ Execut; Year 1999 Month SEP \n", + "+ Execut; Year 1999 Month OCT \n", + "+ Execut; Year 1999 Month NOV \n", + "+ Execut; Year 1999 Month DEC \n", + "+ Execut; Year 2000 Month JAN \n", + "+ Execut; Year 2000 Month FEB \n", + "+ Execut; Year 2000 Month MAR \n", + "+ Execut; Year 2000 Month APR \n", + "+ Execut; Year 2000 Month MAY \n", + "+ Execut; Year 2000 Month JUN \n", + "+ Execut; Year 2000 Month JUL \n", + "+ Execut; Year 2000 Month AUG \n", + "+ Execut; Year 2000 Month SEP \n", + "+ Execut; Year 2000 Month OCT \n", + "+ Execut; Year 2000 Month NOV \n", + "+ Execut; Year 2000 Month DEC \n", + "+ Execut; Year 2001 Month JAN \n", + "+ Execut; Year 2001 Month FEB \n", + "+ Execut; Year 2001 Month MAR \n", + "+ Execut; Year 2001 Month APR \n", + "+ Execut; Year 2001 Month MAY \n", + "+ Execut; Year 2001 Month JUN \n", + "+ Execut; Year 2001 Month JUL \n", + "+ Execut; Year 2001 Month AUG \n", + "+ Execut; Year 2001 Month SEP \n", + "+ Execut; Year 2001 Month OCT \n", + "+ Execut; Year 2001 Month NOV \n", + "+ Execut; Year 2001 Month DEC \n", + "+ Execut; Year 2002 Month JAN \n", + "+ Execut; Year 2002 Month FEB \n", + "+ Execut; Year 2002 Month MAR \n", + "+ Execut; Year 2002 Month APR \n", + "+ Execut; Year 2002 Month MAY \n", + "+ Execut; Year 2002 Month JUN \n", + "+ Execut; Year 2002 Month JUL \n", + "+ Execut; Year 2002 Month AUG \n", + "+ Execut; Year 2002 Month SEP \n", + "+ Execut; Year 2002 Month OCT \n", + "+ Execut; Year 2002 Month NOV \n", + "+ Execut; Year 2002 Month DEC \n", + "+ Execut; Year 2003 Month JAN \n", + "+ Execut; Year 2003 Month FEB \n", + "+ Execut; Year 2003 Month MAR \n", + "+ Execut; Year 2003 Month APR \n", + "+ Execut; Year 2003 Month MAY \n", + "+ Execut; Year 2003 Month JUN \n", + "+ Execut; Year 2003 Month JUL \n", + "+ Execut; Year 2003 Month AUG \n", + "+ Execut; Year 2003 Month SEP \n", + "+ Execut; Year 2003 Month OCT \n", + "+ Execut; Year 2003 Month NOV \n", + "+ Execut; Year 2003 Month DEC \n", + "+ Execut; Year 2004 Month JAN \n", + "+ Execut; Year 2004 Month FEB \n", + "+ Execut; Year 2004 Month MAR \n", + "+ Execut; Year 2004 Month APR \n", + "+ Execut; Year 2004 Month MAY \n", + "+ Execut; Year 2004 Month JUN \n", + "+ Execut; Year 2004 Month JUL \n", + "+ Execut; Year 2004 Month AUG \n", + "+ Execut; Year 2004 Month SEP \n", + "+ Execut; Year 2004 Month OCT \n", + "+ Execut; Year 2004 Month NOV \n", + "+ Execut; Year 2004 Month DEC \n", + "+ Execut; Year 2005 Month JAN \n", + "+ Execut; Year 2005 Month FEB \n", + "+ Execut; Year 2005 Month MAR \n", + "+ Execut; Year 2005 Month APR \n", + "+ Execut; Year 2005 Month MAY \n", + "+ Execut; Year 2005 Month JUN \n", + "+ Execut; Year 2005 Month JUL \n", + "+ Execut; Year 2005 Month AUG \n", + "+ Execut; Year 2005 Month SEP \n", + "+ Execut; Year 2005 Month OCT \n", + "+ Execut; Year 2005 Month NOV \n", + "+ Execut; Year 2005 Month DEC \n", + "+ Execut; Year 2006 Month JAN \n", + "+ Execut; Year 2006 Month FEB \n", + "+ Execut; Year 2006 Month MAR \n", + "+ Execut; Year 2006 Month APR \n", + "+ Execut; Year 2006 Month MAY \n", + "+ Execut; Year 2006 Month JUN \n", + "+ Execut; Year 2006 Month JUL \n", + "+ Execut; Year 2006 Month AUG \n", + "+ Execut; Year 2006 Month SEP \n", + "+ Execut; Year 2006 Month OCT \n", + "+ Execut; Year 2006 Month NOV \n", + "+ Execut; Year 2006 Month DEC \n", + "+ Execut; Year 2007 Month JAN \n", + "+ Execut; Year 2007 Month FEB \n", + "+ Execut; Year 2007 Month MAR \n", + "+ Execut; Year 2007 Month APR \n", + "+ Execut; Year 2007 Month MAY \n", + "+ Execut; Year 2007 Month JUN \n", + "+ Execut; Year 2007 Month JUL \n", + "+ Execut; Year 2007 Month AUG \n", + "+ Execut; Year 2007 Month SEP \n", + "+ Execut; Year 2007 Month OCT \n", + "+ Execut; Year 2007 Month NOV \n", + "+ Execut; Year 2007 Month DEC \n", + "+ Execut; Year 2008 Month JAN \n", + "+ Execut; Year 2008 Month FEB \n", + "+ Execut; Year 2008 Month MAR \n", + "+ Execut; Year 2008 Month APR \n", + "+ Execut; Year 2008 Month MAY \n", + "+ Execut; Year 2008 Month JUN \n", + "+ Execut; Year 2008 Month JUL \n", + "+ Execut; Year 2008 Month AUG \n", + "+ Execut; Year 2008 Month SEP \n", + "+ Execut; Year 2008 Month OCT \n", + "+ Execut; Year 2008 Month NOV \n", + "+ Execut; Year 2008 Month DEC \n", + "+ Execut; Year 2009 Month JAN \n", + "+ Execut; Year 2009 Month FEB \n", + "+ Execut; Year 2009 Month MAR \n", + "+ Execut; Year 2009 Month APR \n", + "+ Execut; Year 2009 Month MAY \n", + "+ Execut; Year 2009 Month JUN \n", + "+ Execut; Year 2009 Month JUL \n", + "+ Execut; Year 2009 Month AUG \n", + "+ Execut; Year 2009 Month SEP \n", + "+ Execut; Year 2009 Month OCT \n", + "+ Execut; Year 2009 Month NOV \n", + "+ Execut; Year 2009 Month DEC \n", + "+ Execut; Year 2010 Month JAN \n", + "+ Execut; Year 2010 Month FEB \n", + "+ Execut; Year 2010 Month MAR \n", + "+ Execut; Year 2010 Month APR \n", + "+ Execut; Year 2010 Month MAY \n", + "+ Execut; Year 2010 Month JUN \n", + "+ Execut; Year 2010 Month JUL \n", + "+ Execut; Year 2010 Month AUG \n", + "+ Execut; Year 2010 Month SEP \n", + "+ Execut; Year 2010 Month OCT \n", + "+ Execut; Year 2010 Month NOV \n", + "+ Execut; Year 2010 Month DEC \n", + "+ Execut; Year 2011 Month JAN \n", + "+ Execut; Year 2011 Month FEB \n", + "+ Execut; Year 2011 Month MAR \n", + "+ Execut; Year 2011 Month APR \n", + "+ Execut; Year 2011 Month MAY \n", + "+ Execut; Year 2011 Month JUN \n", + "+ Execut; Year 2011 Month JUL \n", + "+ Execut; Year 2011 Month AUG \n", + "+ Execut; Year 2011 Month SEP \n", + "+ Execut; Year 2011 Month OCT \n", + "+ Execut; Year 2011 Month NOV \n", + "+ Execut; Year 2011 Month DEC \n", + "+ Execut; Year 2012 Month JAN \n", + "+ Execut; Year 2012 Month FEB \n", + "+ Execut; Year 2012 Month MAR \n", + "+ Execut; Year 2012 Month APR \n", + "+ Execut; Year 2012 Month MAY \n", + "+ Execut; Year 2012 Month JUN \n", + "+ Execut; Year 2012 Month JUL \n", + "+ Execut; Year 2012 Month AUG \n", + "+ Execut; Year 2012 Month SEP \n", + "+ Execut; Year 2012 Month OCT \n", + "+ Execut; Year 2012 Month NOV \n", + "+ Execut; Year 2012 Month DEC \n", + "+ Execut; Year 2013 Month JAN \n", + "+ Execut; Year 2013 Month FEB \n", + "+ Execut; Year 2013 Month MAR \n", + "+ Execut; Year 2013 Month APR \n", + "+ Execut; Year 2013 Month MAY \n", + "+ Execut; Year 2013 Month JUN \n", + "+ Execut; Year 2013 Month JUL \n", + "+ Execut; Year 2013 Month AUG \n", + "+ Execut; Year 2013 Month SEP \n", + "\n", + "________________________________________________________________________\n", + " Execut; On Year 1973 Month JUN Day 1\n", + " The maximum number of reoperations 169\n", + "\n", + "________________________________________________________________________\n", + " Execut; Writing reports\n", + "\n", + "________________________________________________________________________\n", + " Execut; \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutRes \n", + "+ Printing Reservoir Summary 1 of 41; or 2. % Complete\n", + "+ Printing Reservoir Summary 26 of 41; or 63. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutDivW \n", + "+ Printing Diversion & Stream Summary 1 of 725; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 26 of 725; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 51 of 725; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 76 of 725; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 101 of 725; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 126 of 725; or 17. % Complete\n", + "+ Printing Diversion & Stream Summary 151 of 725; or 21. % Complete\n", + "+ Printing Diversion & Stream Summary 176 of 725; or 24. % Complete\n", + "+ Printing Diversion & Stream Summary 201 of 725; or 28. % Complete\n", + "+ Printing Diversion & Stream Summary 226 of 725; or 31. % Complete\n", + "+ Printing Diversion & Stream Summary 251 of 725; or 35. % Complete\n", + "+ Printing Diversion & Stream Summary 276 of 725; or 38. % Complete\n", + "+ Printing Diversion & Stream Summary 301 of 725; or 42. % Complete\n", + "+ Printing Diversion & Stream Summary 326 of 725; or 45. % Complete\n", + "+ Printing Diversion & Stream Summary 351 of 725; or 48. % Complete\n", + "+ Printing Diversion & Stream Summary 376 of 725; or 52. % Complete\n", + "+ Printing Diversion & Stream Summary 401 of 725; or 55. % Complete\n", + "+ Printing Diversion & Stream Summary 426 of 725; or 59. % Complete\n", + "+ Printing Diversion & Stream Summary 451 of 725; or 62. % Complete\n", + "+ Printing Diversion & Stream Summary 476 of 725; or 66. % Complete\n", + "+ Printing Diversion & Stream Summary 501 of 725; or 69. % Complete\n", + "+ Printing Diversion & Stream Summary 526 of 725; or 73. % Complete\n", + "+ Printing Diversion & Stream Summary 551 of 725; or 76. % Complete\n", + "+ Printing Diversion & Stream Summary 576 of 725; or 79. % Complete\n", + "+ Printing Diversion & Stream Summary 601 of 725; or 83. % Complete\n", + "+ Printing Diversion & Stream Summary 626 of 725; or 86. % Complete\n", + "+ Printing Diversion & Stream Summary 651 of 725; or 90. % Complete\n", + "+ Printing Diversion & Stream Summary 676 of 725; or 93. % Complete\n", + "+ Printing Diversion & Stream Summary 701 of 725; or 97. % Complete\n", + "+ Printing Diversion & Stream Summary 725 of 725; or 100. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutOpr \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutXss \n", + "+ Printing Structure Summary (*.xss) 0 of 544; or 0. % Complete\n", + "+ Printing Structure Summary (*.xss) 25 of 544; or 5. % Complete\n", + "+ Printing Structure Summary (*.xss) 50 of 544; or 9. % Complete\n", + "+ Printing Structure Summary (*.xss) 75 of 544; or 14. % Complete\n", + "+ Printing Structure Summary (*.xss) 100 of 544; or 19. % Complete\n", + "+ Printing Structure Summary (*.xss) 125 of 544; or 23. % Complete\n", + "+ Printing Structure Summary (*.xss) 150 of 544; or 28. % Complete\n", + "+ Printing Structure Summary (*.xss) 175 of 544; or 32. % Complete\n", + "+ Printing Structure Summary (*.xss) 200 of 544; or 37. % Complete\n", + "+ Printing Structure Summary (*.xss) 225 of 544; or 42. % Complete\n", + "+ Printing Structure Summary (*.xss) 250 of 544; or 46. % Complete\n", + "+ Printing Structure Summary (*.xss) 275 of 544; or 51. % Complete\n", + "+ Printing Structure Summary (*.xss) 300 of 544; or 55. % Complete\n", + "+ Printing Structure Summary (*.xss) 325 of 544; or 60. % Complete\n", + "+ Printing Structure Summary (*.xss) 350 of 544; or 65. % Complete\n", + "+ Printing Structure Summary (*.xss) 375 of 544; or 69. % Complete\n", + "+ Printing Structure Summary (*.xss) 400 of 544; or 74. % Complete\n", + "+ Printing Structure Summary (*.xss) 425 of 544; or 78. % Complete\n", + "+ Printing Structure Summary (*.xss) 450 of 544; or 83. % Complete\n", + "+ Printing Structure Summary (*.xss) 475 of 544; or 88. % Complete\n", + "+ Printing Structure Summary (*.xss) 500 of 544; or 92. % Complete\n", + "+ Printing Structure Summary (*.xss) 525 of 544; or 97. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine Outifr\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutPln \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutWW \n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Run output files are:\n", + " \n", + " Diversion output: *.xdd\n", + " Reservoir output: *.xre\n", + " Operating Rule Info: *.xop\n", + " Instream Reach Info: *.xir\n", + " Structure Summary: *.xss\n", + " Call (Control) Summary: *.xca\n", + " Plan Output: *.xpl\n", + " WWSP Output: *.xww\n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Termination\n", + " Statem; See detailed messages in dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015B.log \n", + " Stop 0\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# run statemod\n", - "subprocess.call([statemod_exe, basin_path, \"-simulate\"])" + "subprocess.call([statemod_exe, basin_path, \"-simulate\"])\n" ] }, { @@ -134,14 +1583,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "f381ae11-0f42-499d-be43-271ce7033402", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# create a directory to store the historical reservoir levels at Blue Mesa\n", + "# create a directory to store the historical reservoir levels at Blue Mesa \n", "output_dir = os.path.join(data_dir, \"historic_reservoir_levels\")\n", "\n", "if not os.path.exists(output_dir):\n", @@ -151,20 +1600,19 @@ "xre_file = os.path.join(data_dir, \"gm2015B.xre\")\n", "\n", "# structure ID for reservoir of interest\n", - "structure_ID = \"6203532\"\n", + "structure_ID = '6203532' \n", "\n", "# name of the reservoir\n", - "structure_name = \"Blue_Mesa\"\n", + "structure_name = 'Blue_Mesa' \n", "\n", "# extract the target info into a Pandas data frame\n", - "df = stm.extract_xre_data(\n", - " structure_name=structure_name,\n", - " structure_id=structure_ID,\n", - " input_file=xre_file,\n", - " basin_name=None,\n", - " output_directory=output_dir,\n", - " write_csv=True,\n", - " write_parquet=None,\n", + "df = stm.extract_xre_data(structure_name=structure_name,\n", + " structure_id=structure_ID,\n", + " input_file=xre_file,\n", + " basin_name=None,\n", + " output_directory=output_dir,\n", + " write_csv=True,\n", + " write_parquet=None\n", ")" ] }, @@ -178,22 +1626,133 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "4fb009c5-1fb6-4796-a216-2bff0db94ce6", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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YearInit. Storage
01908441516.500000
11909409705.807692
21910378741.903846
31911374242.865385
41912402187.230769
.........
1012009384270.711538
1022010380057.192308
1032011346074.019231
1042012290796.692308
1052013202086.125000
\n", + "

106 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " Year Init. Storage\n", + "0 1908 441516.500000\n", + "1 1909 409705.807692\n", + "2 1910 378741.903846\n", + "3 1911 374242.865385\n", + "4 1912 402187.230769\n", + ".. ... ...\n", + "101 2009 384270.711538\n", + "102 2010 380057.192308\n", + "103 2011 346074.019231\n", + "104 2012 290796.692308\n", + "105 2013 202086.125000\n", + "\n", + "[106 rows x 2 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "output_xre_file = os.path.join(output_dir, \"Blue_Mesa_xre_data.csv\")\n", "\n", "# read output data into a data frame\n", - "df = pd.read_csv(output_xre_file, usecols=[\"Year\", \"Init. Storage\"], index_col=False)\n", + "df = pd.read_csv(\n", + " output_xre_file, \n", + " usecols=['Year','Init. Storage'],\n", + " index_col=False) \n", "\n", "# calculate the annual average\n", - "df = df.groupby(\"Year\").mean().reset_index()\n", + "df = df.groupby('Year').mean().reset_index()\n", "\n", - "df" + "df\n" ] }, { @@ -206,20 +1765,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "ef081dea-e9b4-48b7-a87b-306622cd9799", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Reservoir Storage (AF)')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "\n", - "plt.plot(df[\"Year\"], df[\"Init. Storage\"])\n", + "plt.plot(df['Year'], df['Init. Storage'])\n", "\n", "plt.title(\"Blue Mesa Storage\")\n", "plt.xlabel(\"Year\")\n", - "plt.ylabel(\"Reservoir Storage (AF)\")" + "plt.ylabel(\"Reservoir Storage (AF)\")\n" ] }, { @@ -240,15 +1820,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "3da00fb4-2273-4051-a116-f75e01cab719", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# a dictionary to describe what you want to modify and the bounds for the Latin hypercube sample.\n", - "setup_dict = {\"ids\": [\"10011\"], \"bounds\": [-0.5, 1.0]}\n", + "# a dictionary to describe what you want to modify and the bounds for the Latin hypercube sample. \n", + "setup_dict = {\n", + " \"ids\": ['10011'],\n", + " \"bounds\": [-0.5, 1.0]\n", + "}\n", "\n", "# create a directory to store the new files in if it does not exist\n", "output_directory = os.path.join(data_dir, \"input_files\")\n", @@ -277,24 +1860,22 @@ "basin_name = \"Gunnison\"\n", "\n", "# generate a batch of files using generated LHS\n", - "stm.modify_eva(\n", - " modify_dict=setup_dict,\n", - " query_field=query_field,\n", - " output_dir=output_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " sampling_method=\"LHS\",\n", - " n_samples=n_samples,\n", - " skip_rows=skip_rows,\n", - " n_jobs=n_jobs,\n", - " seed_value=seed_value,\n", - " template_file=None,\n", - " factor_method=\"add\",\n", - " data_specification_file=None,\n", - " min_bound_value=-0.5,\n", - " max_bound_value=1.0,\n", - " save_sample=True,\n", - ")" + "stm.modify_eva(modify_dict=setup_dict,\n", + " query_field=query_field,\n", + " output_dir=output_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " sampling_method=\"LHS\",\n", + " n_samples=n_samples,\n", + " skip_rows=skip_rows,\n", + " n_jobs=n_jobs,\n", + " seed_value=seed_value,\n", + " template_file=None,\n", + " factor_method=\"add\",\n", + " data_specification_file=None,\n", + " min_bound_value=-0.5,\n", + " max_bound_value=1.0,\n", + " save_sample=True)\n" ] }, { @@ -307,20 +1888,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "757f8158-ee37-4990-8eb6-a9b02dd6db2b", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.1872335 ],\n", + " [ 0.79024337]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# path to the numpy file containing the samples\n", "eva_samples_file = os.path.join(output_directory, \"eva_2-samples_scenario-1.npy\")\n", "\n", - "# load samples\n", + "# load samples \n", "sample_array = np.load(eva_samples_file)\n", "\n", - "sample_array" + "sample_array\n" ] }, { @@ -341,13 +1934,2872 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "fc440b6f-e8ad-4863-a6ac-29c84ba724a0", "metadata": { "scrolled": true, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running: S0_1\n", + " Startup log file for messages to this point: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S0_1/gm2015B_S0_1.rsp \n", + " Closing startup log file: statem.log\n", + " Opening dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S0_1/gm2015B_S0_1.log \n", + "________________________________________________________________________\n", + "\n", + " StateMod \n", + " State of Colorado - Water Supply Planning Model \n", + "\n", + " Version: 17.0.3 \n", + " Last revision date: 2021/09/12\n", + "\n", + "________________________________________________________________________\n", + " \n", + " Subroutine Execut\n", + " Subroutine Datinp\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Control File (*.ctl) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Network File (*.rin)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Reservoir Station File (*.res)\n", + " Subroutine GetRes\n", + "\n", + " GetRes; Reservoir Station File (*.res) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Diversion Station File (*.dds)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Station File (*.ris)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Instream Flow Station File (*.ifs) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Well Station File (*.wes) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Plan Station File (*.pln) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Gage File (*.rig) \n", + " Subroutine Riginp\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Instream Flow Right File (*.ifr) 20 1 98\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Reservoir Right File (*.rer) \n", + "\n", + "________________________________________________________________________\n", + " Riginp; Direct Diversion Right File (*.ddr) \n", + "\n", + "________________________________________________________________________\n", + " Oprinp; Operational Right File (*.opr) \n", + "\n", + "________________________________________________________________________\n", + " Mdainp; Instream flow Demand file - Annual (*.ifa) \n", + " Subroutine Execut\n", + "\n", + "+ Execut; Year 1908 Month OCT \n", + "+ Execut; Year 1908 Month NOV \n", + "+ Execut; Year 1908 Month DEC \n", + "+ Execut; Year 1909 Month JAN \n", + "+ Execut; Year 1909 Month FEB \n", + "+ 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\n", + "+ Execut; Year 1999 Month JAN \n", + "+ Execut; Year 1999 Month FEB \n", + "+ Execut; Year 1999 Month MAR \n", + "+ Execut; Year 1999 Month APR \n", + "+ Execut; Year 1999 Month MAY \n", + "+ Execut; Year 1999 Month JUN \n", + "+ Execut; Year 1999 Month JUL \n", + "+ Execut; Year 1999 Month AUG \n", + "+ Execut; Year 1999 Month SEP \n", + "+ Execut; Year 1999 Month OCT \n", + "+ Execut; Year 1999 Month NOV \n", + "+ Execut; Year 1999 Month DEC \n", + "+ Execut; Year 2000 Month JAN \n", + "+ Execut; Year 2000 Month FEB \n", + "+ Execut; Year 2000 Month MAR \n", + "+ Execut; Year 2000 Month APR \n", + "+ Execut; Year 2000 Month MAY \n", + "+ Execut; Year 2000 Month JUN \n", + "+ Execut; Year 2000 Month JUL \n", + "+ Execut; Year 2000 Month AUG \n", + "+ Execut; Year 2000 Month SEP \n", + "+ Execut; Year 2000 Month OCT \n", + "+ Execut; Year 2000 Month NOV \n", + "+ Execut; Year 2000 Month DEC \n", + "+ Execut; Year 2001 Month JAN \n", + "+ Execut; Year 2001 Month FEB \n", + "+ Execut; Year 2001 Month MAR \n", + "+ Execut; Year 2001 Month APR \n", + "+ Execut; Year 2001 Month MAY \n", + "+ Execut; Year 2001 Month JUN \n", + "+ Execut; Year 2001 Month JUL \n", + "+ Execut; Year 2001 Month AUG \n", + "+ Execut; Year 2001 Month SEP \n", + "+ Execut; Year 2001 Month OCT \n", + "+ Execut; Year 2001 Month NOV \n", + "+ Execut; Year 2001 Month DEC \n", + "+ Execut; Year 2002 Month JAN \n", + "+ Execut; Year 2002 Month FEB \n", + "+ Execut; Year 2002 Month MAR \n", + "+ Execut; Year 2002 Month APR \n", + "+ Execut; Year 2002 Month MAY \n", + "+ Execut; Year 2002 Month JUN \n", + "+ Execut; Year 2002 Month JUL \n", + "+ Execut; Year 2002 Month AUG \n", + "+ Execut; Year 2002 Month SEP \n", + "+ Execut; Year 2002 Month OCT \n", + "+ Execut; Year 2002 Month NOV \n", + "+ Execut; Year 2002 Month DEC \n", + "+ Execut; Year 2003 Month JAN \n", + "+ Execut; Year 2003 Month FEB \n", + "+ Execut; Year 2003 Month MAR \n", + "+ Execut; Year 2003 Month APR \n", + "+ Execut; Year 2003 Month MAY \n", + "+ Execut; Year 2003 Month JUN \n", + "+ Execut; Year 2003 Month JUL \n", + "+ Execut; Year 2003 Month AUG \n", + "+ Execut; Year 2003 Month SEP \n", + "+ Execut; Year 2003 Month OCT \n", + "+ Execut; Year 2003 Month NOV \n", + "+ Execut; Year 2003 Month DEC \n", + "+ Execut; Year 2004 Month JAN \n", + "+ Execut; Year 2004 Month FEB \n", + "+ Execut; Year 2004 Month MAR \n", + "+ Execut; Year 2004 Month APR \n", + "+ Execut; Year 2004 Month MAY \n", + "+ Execut; Year 2004 Month JUN \n", + "+ Execut; Year 2004 Month JUL \n", + "+ Execut; Year 2004 Month AUG \n", + "+ Execut; Year 2004 Month SEP \n", + "+ Execut; Year 2004 Month OCT \n", + "+ Execut; Year 2004 Month NOV \n", + "+ Execut; Year 2004 Month DEC \n", + "+ Execut; Year 2005 Month JAN \n", + "+ Execut; Year 2005 Month FEB \n", + "+ Execut; Year 2005 Month MAR \n", + "+ Execut; Year 2005 Month APR \n", + "+ Execut; Year 2005 Month MAY \n", + "+ Execut; Year 2005 Month JUN \n", + "+ Execut; Year 2005 Month JUL \n", + "+ Execut; Year 2005 Month AUG \n", + "+ Execut; Year 2005 Month SEP \n", + "+ Execut; Year 2005 Month OCT \n", + "+ Execut; Year 2005 Month NOV \n", + "+ Execut; Year 2005 Month DEC \n", + "+ Execut; Year 2006 Month JAN \n", + "+ Execut; Year 2006 Month FEB \n", + "+ Execut; Year 2006 Month MAR \n", + "+ Execut; Year 2006 Month APR \n", + "+ Execut; Year 2006 Month MAY \n", + "+ Execut; Year 2006 Month JUN \n", + "+ Execut; Year 2006 Month JUL \n", + "+ Execut; Year 2006 Month AUG \n", + "+ Execut; Year 2006 Month SEP \n", + "+ Execut; Year 2006 Month OCT \n", + "+ Execut; Year 2006 Month NOV \n", + "+ Execut; Year 2006 Month DEC \n", + "+ Execut; Year 2007 Month JAN \n", + "+ Execut; Year 2007 Month FEB \n", + "+ Execut; Year 2007 Month MAR \n", + "+ Execut; Year 2007 Month APR \n", + "+ Execut; Year 2007 Month MAY \n", + "+ Execut; Year 2007 Month JUN \n", + "+ Execut; Year 2007 Month JUL \n", + "+ Execut; Year 2007 Month AUG \n", + "+ Execut; Year 2007 Month SEP \n", + "+ Execut; Year 2007 Month OCT \n", + "+ Execut; Year 2007 Month NOV \n", + "+ Execut; Year 2007 Month DEC \n", + "+ Execut; Year 2008 Month JAN \n", + "+ Execut; Year 2008 Month FEB \n", + "+ Execut; Year 2008 Month MAR \n", + "+ Execut; Year 2008 Month APR \n", + "+ Execut; Year 2008 Month MAY \n", + "+ Execut; Year 2008 Month JUN \n", + "+ Execut; Year 2008 Month JUL \n", + "+ Execut; Year 2008 Month AUG \n", + "+ Execut; Year 2008 Month SEP \n", + "+ Execut; Year 2008 Month OCT \n", + "+ Execut; Year 2008 Month NOV \n", + "+ Execut; Year 2008 Month DEC \n", + "+ Execut; Year 2009 Month JAN \n", + "+ Execut; Year 2009 Month FEB \n", + "+ Execut; Year 2009 Month MAR \n", + "+ Execut; Year 2009 Month APR \n", + "+ Execut; Year 2009 Month MAY \n", + "+ Execut; Year 2009 Month JUN \n", + "+ Execut; Year 2009 Month JUL \n", + "+ Execut; Year 2009 Month AUG \n", + "+ Execut; Year 2009 Month SEP \n", + "+ Execut; Year 2009 Month OCT \n", + "+ Execut; Year 2009 Month NOV \n", + "+ Execut; Year 2009 Month DEC \n", + "+ Execut; Year 2010 Month JAN \n", + "+ Execut; Year 2010 Month FEB \n", + "+ Execut; Year 2010 Month MAR \n", + "+ Execut; Year 2010 Month APR \n", + "+ Execut; Year 2010 Month MAY \n", + "+ Execut; Year 2010 Month JUN \n", + "+ Execut; Year 2010 Month JUL \n", + "+ Execut; Year 2010 Month AUG \n", + "+ Execut; Year 2010 Month SEP \n", + "+ Execut; Year 2010 Month OCT \n", + "+ Execut; Year 2010 Month NOV \n", + "+ Execut; Year 2010 Month DEC \n", + "+ Execut; Year 2011 Month JAN \n", + "+ Execut; Year 2011 Month FEB \n", + "+ Execut; Year 2011 Month MAR \n", + "+ Execut; Year 2011 Month APR \n", + "+ Execut; Year 2011 Month MAY \n", + "+ Execut; Year 2011 Month JUN \n", + "+ Execut; Year 2011 Month JUL \n", + "+ Execut; Year 2011 Month AUG \n", + "+ Execut; Year 2011 Month SEP \n", + "+ Execut; Year 2011 Month OCT \n", + "+ Execut; Year 2011 Month NOV \n", + "+ Execut; Year 2011 Month DEC \n", + "+ Execut; Year 2012 Month JAN \n", + "+ Execut; Year 2012 Month FEB \n", + "+ Execut; Year 2012 Month MAR \n", + "+ Execut; Year 2012 Month APR \n", + "+ Execut; Year 2012 Month MAY \n", + "+ Execut; Year 2012 Month JUN \n", + "+ Execut; Year 2012 Month JUL \n", + "+ Execut; Year 2012 Month AUG \n", + "+ Execut; Year 2012 Month SEP \n", + "+ Execut; Year 2012 Month OCT \n", + "+ Execut; Year 2012 Month NOV \n", + "+ Execut; Year 2012 Month DEC \n", + "+ Execut; Year 2013 Month JAN \n", + "+ Execut; Year 2013 Month FEB \n", + "+ Execut; Year 2013 Month MAR \n", + "+ Execut; Year 2013 Month APR \n", + "+ Execut; Year 2013 Month MAY \n", + "+ Execut; Year 2013 Month JUN \n", + "+ Execut; Year 2013 Month JUL \n", + "+ Execut; Year 2013 Month AUG \n", + "+ Execut; Year 2013 Month SEP \n", + "\n", + "________________________________________________________________________\n", + " Execut; On Year 1973 Month JUN Day 1\n", + " The maximum number of reoperations 169\n", + "\n", + "________________________________________________________________________\n", + " Execut; Writing reports\n", + "\n", + "________________________________________________________________________\n", + " Execut; \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutRes \n", + "+ Printing Reservoir Summary 1 of 41; or 2. % Complete\n", + "+ Printing Reservoir Summary 26 of 41; or 63. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutDivW \n", + "+ Printing Diversion & Stream Summary 1 of 725; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 26 of 725; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 51 of 725; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 76 of 725; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 101 of 725; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 126 of 725; or 17. % Complete\n", + "+ Printing Diversion & Stream Summary 151 of 725; or 21. % Complete\n", + "+ Printing Diversion & Stream Summary 176 of 725; or 24. % Complete\n", + "+ Printing Diversion & Stream Summary 201 of 725; or 28. % Complete\n", + "+ Printing Diversion & Stream Summary 226 of 725; or 31. % Complete\n", + "+ Printing Diversion & Stream Summary 251 of 725; or 35. % Complete\n", + "+ Printing Diversion & Stream Summary 276 of 725; or 38. % Complete\n", + "+ Printing Diversion & Stream Summary 301 of 725; or 42. % Complete\n", + "+ Printing Diversion & Stream Summary 326 of 725; or 45. % Complete\n", + "+ Printing Diversion & Stream Summary 351 of 725; or 48. % Complete\n", + "+ Printing Diversion & Stream Summary 376 of 725; or 52. % Complete\n", + "+ Printing Diversion & Stream Summary 401 of 725; or 55. % Complete\n", + "+ Printing Diversion & Stream Summary 426 of 725; or 59. % Complete\n", + "+ Printing Diversion & Stream Summary 451 of 725; or 62. % Complete\n", + "+ Printing Diversion & Stream Summary 476 of 725; or 66. % Complete\n", + "+ Printing Diversion & Stream Summary 501 of 725; or 69. % Complete\n", + "+ Printing Diversion & Stream Summary 526 of 725; or 73. % Complete\n", + "+ Printing Diversion & Stream Summary 551 of 725; or 76. % Complete\n", + "+ Printing Diversion & Stream Summary 576 of 725; or 79. % Complete\n", + "+ Printing Diversion & Stream Summary 601 of 725; or 83. % Complete\n", + "+ Printing Diversion & Stream Summary 626 of 725; or 86. % Complete\n", + "+ Printing Diversion & Stream Summary 651 of 725; or 90. % Complete\n", + "+ Printing Diversion & Stream Summary 676 of 725; or 93. % Complete\n", + "+ Printing Diversion & Stream Summary 701 of 725; or 97. % Complete\n", + "+ Printing Diversion & Stream Summary 725 of 725; or 100. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutOpr \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutXss \n", + "+ Printing Structure Summary (*.xss) 0 of 544; or 0. % Complete\n", + "+ Printing Structure Summary (*.xss) 25 of 544; or 5. % Complete\n", + "+ Printing Structure Summary (*.xss) 50 of 544; or 9. % Complete\n", + "+ Printing Structure Summary (*.xss) 75 of 544; or 14. % Complete\n", + "+ Printing Structure Summary (*.xss) 100 of 544; or 19. % Complete\n", + "+ Printing Structure Summary (*.xss) 125 of 544; or 23. % Complete\n", + "+ Printing Structure Summary (*.xss) 150 of 544; or 28. % Complete\n", + "+ Printing Structure Summary (*.xss) 175 of 544; or 32. % Complete\n", + "+ Printing Structure Summary (*.xss) 200 of 544; or 37. % Complete\n", + "+ Printing Structure Summary (*.xss) 225 of 544; or 42. % Complete\n", + "+ Printing Structure Summary (*.xss) 250 of 544; or 46. % Complete\n", + "+ Printing Structure Summary (*.xss) 275 of 544; or 51. % Complete\n", + "+ Printing Structure Summary (*.xss) 300 of 544; or 55. % Complete\n", + "+ Printing Structure Summary (*.xss) 325 of 544; or 60. % Complete\n", + "+ Printing Structure Summary (*.xss) 350 of 544; or 65. % Complete\n", + "+ Printing Structure Summary (*.xss) 375 of 544; or 69. % Complete\n", + "+ Printing Structure Summary (*.xss) 400 of 544; or 74. % Complete\n", + "+ Printing Structure Summary (*.xss) 425 of 544; or 78. % Complete\n", + "+ Printing Structure Summary (*.xss) 450 of 544; or 83. % Complete\n", + "+ Printing Structure Summary (*.xss) 475 of 544; or 88. % Complete\n", + "+ Printing Structure Summary (*.xss) 500 of 544; or 92. % Complete\n", + "+ Printing Structure Summary (*.xss) 525 of 544; or 97. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine Outifr\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutPln \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutWW \n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Run output files are:\n", + " \n", + " Diversion output: *.xdd\n", + " Reservoir output: *.xre\n", + " Operating Rule Info: *.xop\n", + " Instream Reach Info: *.xir\n", + " Structure Summary: *.xss\n", + " Call (Control) Summary: *.xca\n", + " Plan Output: *.xpl\n", + " WWSP Output: *.xww\n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Termination\n", + " Statem; See detailed messages in dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S0_1/gm2015B_S0_1.log \n", + " Stop 0\n", + "Running: S1_1\n", + " Startup log file for messages to this point: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S1_1/gm2015B_S1_1.rsp \n", + " Closing startup log file: statem.log\n", + " Opening dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S1_1/gm2015B_S1_1.log \n", + "________________________________________________________________________\n", + "\n", + " StateMod \n", + " State of Colorado - Water Supply Planning Model \n", + "\n", + " Version: 17.0.3 \n", + " Last revision date: 2021/09/12\n", + "\n", + "________________________________________________________________________\n", + " \n", + " Subroutine Execut\n", + " Subroutine Datinp\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Control File (*.ctl) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Network File (*.rin)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Reservoir Station File (*.res)\n", + " Subroutine GetRes\n", + "\n", + " GetRes; Reservoir Station File (*.res) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Diversion Station File (*.dds)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Station File (*.ris)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Instream Flow Station File (*.ifs) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Well Station File (*.wes) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Plan Station File (*.pln) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Gage File (*.rig) \n", + " Subroutine Riginp\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Instream Flow Right File (*.ifr) 20 1 98\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Reservoir Right File (*.rer) \n", + "\n", + "________________________________________________________________________\n", + " Riginp; Direct Diversion Right File (*.ddr) \n", + "\n", + "________________________________________________________________________\n", + " Oprinp; Operational Right File (*.opr) \n", + "\n", + "________________________________________________________________________\n", + " Mdainp; Instream flow Demand file - Annual (*.ifa) \n", + " Subroutine Execut\n", + "\n", + "+ Execut; Year 1908 Month OCT \n", + "+ Execut; Year 1908 Month NOV \n", + "+ Execut; Year 1908 Month DEC \n", + "+ Execut; Year 1909 Month JAN \n", + "+ Execut; Year 1909 Month FEB \n", + "+ Execut; Year 1909 Month MAR \n", + "+ Execut; Year 1909 Month APR \n", + "+ Execut; Year 1909 Month MAY \n", + "+ Execut; Year 1909 Month JUN \n", + "+ Execut; Year 1909 Month JUL \n", + "+ Execut; Year 1909 Month AUG \n", + "+ Execut; Year 1909 Month SEP \n", + "+ Execut; Year 1909 Month OCT \n", + "+ Execut; Year 1909 Month NOV \n", + "+ Execut; Year 1909 Month DEC \n", + "+ Execut; Year 1910 Month JAN \n", + "+ Execut; Year 1910 Month FEB \n", + "+ Execut; Year 1910 Month MAR \n", + "+ Execut; Year 1910 Month APR \n", + "+ Execut; Year 1910 Month MAY \n", + "+ Execut; Year 1910 Month JUN \n", + "+ Execut; Year 1910 Month JUL \n", + "+ Execut; Year 1910 Month AUG \n", + "+ Execut; Year 1910 Month SEP \n", + "+ Execut; Year 1910 Month OCT \n", + "+ Execut; Year 1910 Month NOV \n", + "+ Execut; Year 1910 Month DEC \n", + "+ Execut; Year 1911 Month JAN \n", + "+ Execut; Year 1911 Month FEB \n", + "+ Execut; Year 1911 Month MAR \n", + "+ Execut; Year 1911 Month APR \n", + "+ Execut; Year 1911 Month MAY \n", + "+ Execut; Year 1911 Month JUN \n", + "+ Execut; Year 1911 Month JUL \n", + "+ Execut; Year 1911 Month AUG \n", + "+ Execut; Year 1911 Month SEP \n", + "+ Execut; Year 1911 Month OCT \n", + "+ Execut; Year 1911 Month NOV \n", + "+ Execut; Year 1911 Month DEC \n", + "+ Execut; Year 1912 Month JAN \n", + "+ Execut; Year 1912 Month FEB \n", + "+ Execut; Year 1912 Month MAR \n", + "+ Execut; Year 1912 Month APR \n", + "+ Execut; Year 1912 Month MAY \n", + "+ Execut; Year 1912 Month JUN \n", + "+ Execut; Year 1912 Month JUL \n", + "+ Execut; Year 1912 Month AUG \n", + "+ Execut; Year 1912 Month SEP \n", + "+ Execut; Year 1912 Month OCT \n", + "+ Execut; Year 1912 Month NOV \n", + "+ Execut; Year 1912 Month DEC \n", + "+ Execut; Year 1913 Month JAN \n", + "+ Execut; Year 1913 Month FEB \n", + "+ Execut; Year 1913 Month MAR \n", + "+ Execut; Year 1913 Month APR \n", + "+ Execut; Year 1913 Month MAY \n", + "+ Execut; Year 1913 Month JUN \n", + "+ Execut; Year 1913 Month JUL \n", + "+ Execut; Year 1913 Month AUG \n", + "+ Execut; Year 1913 Month SEP \n", + "+ Execut; Year 1913 Month OCT \n", + "+ Execut; Year 1913 Month NOV \n", + "+ Execut; Year 1913 Month DEC \n", + "+ Execut; Year 1914 Month JAN \n", + "+ Execut; Year 1914 Month FEB \n", + "+ Execut; Year 1914 Month MAR \n", + "+ Execut; Year 1914 Month APR \n", + "+ Execut; Year 1914 Month MAY \n", + "+ Execut; Year 1914 Month JUN \n", + "+ Execut; Year 1914 Month JUL \n", + "+ Execut; Year 1914 Month AUG \n", + "+ Execut; Year 1914 Month SEP \n", + "+ Execut; Year 1914 Month OCT \n", + "+ Execut; Year 1914 Month NOV \n", + "+ Execut; Year 1914 Month DEC \n", + "+ Execut; Year 1915 Month JAN \n", + "+ Execut; Year 1915 Month FEB \n", + "+ Execut; Year 1915 Month MAR \n", + "+ Execut; Year 1915 Month APR \n", + "+ Execut; Year 1915 Month MAY \n", + "+ Execut; Year 1915 Month JUN \n", + "+ Execut; Year 1915 Month JUL \n", + "+ Execut; Year 1915 Month AUG \n", + "+ Execut; Year 1915 Month SEP \n", + "+ Execut; Year 1915 Month OCT \n", + "+ Execut; Year 1915 Month NOV \n", + "+ Execut; Year 1915 Month DEC \n", + "+ Execut; Year 1916 Month JAN \n", + "+ Execut; Year 1916 Month FEB \n", + "+ Execut; Year 1916 Month MAR \n", + "+ Execut; Year 1916 Month APR \n", + "+ Execut; Year 1916 Month MAY \n", + "+ Execut; Year 1916 Month JUN \n", + "+ Execut; Year 1916 Month JUL \n", + "+ Execut; Year 1916 Month AUG \n", + "+ Execut; Year 1916 Month SEP \n", + "+ Execut; Year 1916 Month OCT \n", + "+ Execut; Year 1916 Month NOV \n", + "+ Execut; Year 1916 Month DEC \n", + "+ Execut; Year 1917 Month JAN \n", + "+ Execut; Year 1917 Month FEB \n", + "+ Execut; Year 1917 Month MAR \n", + "+ Execut; Year 1917 Month APR \n", + "+ Execut; Year 1917 Month MAY \n", + "+ Execut; Year 1917 Month JUN \n", + "+ Execut; Year 1917 Month JUL \n", + "+ Execut; Year 1917 Month AUG \n", + "+ Execut; Year 1917 Month SEP \n", + "+ Execut; Year 1917 Month OCT \n", + "+ Execut; Year 1917 Month NOV \n", + "+ Execut; Year 1917 Month DEC \n", + "+ Execut; Year 1918 Month JAN \n", + "+ Execut; Year 1918 Month FEB \n", + "+ Execut; Year 1918 Month MAR \n", + "+ Execut; Year 1918 Month APR \n", + "+ Execut; Year 1918 Month MAY \n", + "+ Execut; Year 1918 Month JUN \n", + "+ Execut; Year 1918 Month JUL \n", + "+ Execut; Year 1918 Month AUG \n", + "+ Execut; Year 1918 Month SEP \n", + "+ Execut; Year 1918 Month OCT \n", + "+ Execut; Year 1918 Month NOV \n", + "+ Execut; Year 1918 Month DEC \n", + "+ Execut; Year 1919 Month JAN \n", + "+ Execut; Year 1919 Month FEB \n", + "+ Execut; Year 1919 Month MAR \n", + "+ Execut; Year 1919 Month APR \n", + "+ Execut; Year 1919 Month MAY \n", + "+ Execut; Year 1919 Month JUN \n", + "+ Execut; Year 1919 Month JUL \n", + "+ Execut; Year 1919 Month AUG \n", + "+ Execut; Year 1919 Month SEP \n", + "+ Execut; Year 1919 Month OCT \n", + "+ Execut; Year 1919 Month NOV \n", + "+ Execut; Year 1919 Month DEC \n", + "+ Execut; Year 1920 Month JAN \n", + "+ Execut; Year 1920 Month FEB \n", + "+ Execut; Year 1920 Month MAR \n", + "+ Execut; Year 1920 Month APR \n", + "+ Execut; Year 1920 Month MAY \n", + "+ Execut; Year 1920 Month JUN \n", + "+ Execut; Year 1920 Month JUL \n", + "+ Execut; Year 1920 Month AUG \n", + "+ Execut; Year 1920 Month SEP \n", + "+ Execut; Year 1920 Month OCT \n", + "+ Execut; Year 1920 Month NOV \n", + "+ Execut; Year 1920 Month DEC \n", + "+ Execut; Year 1921 Month JAN \n", + "+ Execut; Year 1921 Month FEB \n", + "+ Execut; Year 1921 Month MAR \n", + "+ Execut; Year 1921 Month APR \n", + "+ Execut; Year 1921 Month MAY \n", + "+ Execut; Year 1921 Month JUN \n", + "+ Execut; Year 1921 Month JUL \n", + "+ Execut; Year 1921 Month AUG \n", + "+ Execut; Year 1921 Month SEP \n", + "+ Execut; Year 1921 Month OCT \n", + "+ Execut; Year 1921 Month NOV \n", + "+ Execut; Year 1921 Month DEC \n", + "+ Execut; Year 1922 Month JAN \n", + "+ Execut; Year 1922 Month FEB \n", + "+ Execut; Year 1922 Month MAR \n", + "+ Execut; Year 1922 Month APR \n", + "+ Execut; Year 1922 Month MAY \n", + "+ Execut; Year 1922 Month JUN \n", + "+ Execut; Year 1922 Month JUL \n", + "+ Execut; Year 1922 Month AUG \n", + "+ Execut; Year 1922 Month 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Month APR \n", + "+ Execut; Year 1975 Month MAY \n", + "+ Execut; Year 1975 Month JUN \n", + "+ Execut; Year 1975 Month JUL \n", + "+ Execut; Year 1975 Month AUG \n", + "+ Execut; Year 1975 Month SEP \n", + "+ Execut; Year 1975 Month OCT \n", + "+ Execut; Year 1975 Month NOV \n", + "+ Execut; Year 1975 Month DEC \n", + "+ Execut; Year 1976 Month JAN \n", + "+ Execut; Year 1976 Month FEB \n", + "+ Execut; Year 1976 Month MAR \n", + "+ Execut; Year 1976 Month APR \n", + "+ Execut; Year 1976 Month MAY \n", + "+ Execut; Year 1976 Month JUN \n", + "+ Execut; Year 1976 Month JUL \n", + "+ Execut; Year 1976 Month AUG \n", + "+ Execut; Year 1976 Month SEP \n", + "+ Execut; Year 1976 Month OCT \n", + "+ Execut; Year 1976 Month NOV \n", + "+ Execut; Year 1976 Month DEC \n", + "+ Execut; Year 1977 Month JAN \n", + "+ Execut; Year 1977 Month FEB \n", + "+ Execut; Year 1977 Month MAR \n", + "+ Execut; Year 1977 Month APR \n", + "+ Execut; Year 1977 Month MAY \n", + "+ Execut; Year 1977 Month JUN \n", + "+ Execut; Year 1977 Month JUL \n", + "+ Execut; Year 1977 Month AUG \n", + "+ Execut; Year 1977 Month SEP \n", + "+ Execut; Year 1977 Month OCT \n", + "+ Execut; Year 1977 Month NOV \n", + "+ Execut; Year 1977 Month DEC \n", + "+ Execut; Year 1978 Month JAN \n", + "+ Execut; Year 1978 Month FEB \n", + "+ Execut; Year 1978 Month MAR \n", + "+ Execut; Year 1978 Month APR \n", + "+ Execut; Year 1978 Month MAY \n", + "+ Execut; Year 1978 Month JUN \n", + "+ Execut; Year 1978 Month JUL \n", + "+ Execut; Year 1978 Month AUG \n", + "+ Execut; Year 1978 Month SEP \n", + "+ Execut; Year 1978 Month OCT \n", + "+ Execut; Year 1978 Month NOV \n", + "+ Execut; Year 1978 Month DEC \n", + "+ Execut; Year 1979 Month JAN \n", + "+ Execut; Year 1979 Month FEB \n", + "+ Execut; Year 1979 Month MAR \n", + "+ Execut; Year 1979 Month APR \n", + "+ Execut; Year 1979 Month MAY \n", + "+ Execut; Year 1979 Month JUN \n", + "+ Execut; Year 1979 Month JUL \n", + "+ Execut; Year 1979 Month AUG \n", + "+ Execut; Year 1979 Month SEP \n", + "+ Execut; Year 1979 Month OCT \n", + "+ Execut; Year 1979 Month NOV \n", + "+ Execut; Year 1979 Month DEC \n", + "+ Execut; Year 1980 Month JAN \n", + "+ Execut; Year 1980 Month FEB \n", + "+ Execut; Year 1980 Month MAR \n", + "+ Execut; Year 1980 Month APR \n", + "+ Execut; Year 1980 Month MAY \n", + "+ Execut; Year 1980 Month JUN \n", + "+ Execut; Year 1980 Month JUL \n", + "+ Execut; Year 1980 Month AUG \n", + "+ Execut; Year 1980 Month SEP \n", + "+ Execut; Year 1980 Month OCT \n", + "+ Execut; Year 1980 Month NOV \n", + "+ Execut; Year 1980 Month DEC \n", + "+ Execut; Year 1981 Month JAN \n", + "+ Execut; Year 1981 Month FEB \n", + "+ Execut; Year 1981 Month MAR \n", + "+ Execut; Year 1981 Month APR \n", + "+ Execut; Year 1981 Month MAY \n", + "+ Execut; Year 1981 Month JUN \n", + "+ Execut; Year 1981 Month JUL \n", + "+ Execut; Year 1981 Month AUG \n", + "+ Execut; Year 1981 Month SEP \n", + "+ Execut; Year 1981 Month OCT \n", + "+ Execut; Year 1981 Month NOV \n", + "+ Execut; Year 1981 Month DEC \n", + "+ Execut; Year 1982 Month JAN \n", + "+ Execut; Year 1982 Month FEB \n", + "+ Execut; Year 1982 Month MAR \n", + "+ Execut; Year 1982 Month APR \n", + "+ Execut; Year 1982 Month MAY \n", + "+ Execut; Year 1982 Month JUN \n", + "+ Execut; Year 1982 Month JUL \n", + "+ Execut; Year 1982 Month AUG \n", + "+ Execut; Year 1982 Month SEP \n", + "+ Execut; Year 1982 Month OCT \n", + "+ Execut; Year 1982 Month NOV \n", + "+ Execut; Year 1982 Month DEC \n", + "+ Execut; Year 1983 Month JAN \n", + "+ Execut; Year 1983 Month FEB \n", + "+ Execut; Year 1983 Month MAR \n", + "+ Execut; Year 1983 Month APR \n", + "+ Execut; Year 1983 Month MAY \n", + "+ Execut; Year 1983 Month JUN \n", + "+ Execut; Year 1983 Month JUL \n", + "+ Execut; Year 1983 Month AUG \n", + "+ Execut; Year 1983 Month SEP \n", + "+ Execut; Year 1983 Month OCT \n", + "+ Execut; Year 1983 Month NOV \n", + "+ Execut; Year 1983 Month DEC \n", + "+ Execut; Year 1984 Month JAN \n", + "+ Execut; Year 1984 Month FEB \n", + "+ Execut; Year 1984 Month MAR \n", + "+ Execut; Year 1984 Month APR \n", + "+ Execut; Year 1984 Month MAY \n", + "+ Execut; Year 1984 Month JUN \n", + "+ Execut; Year 1984 Month JUL \n", + "+ Execut; Year 1984 Month AUG \n", + "+ Execut; Year 1984 Month SEP \n", + "+ Execut; Year 1984 Month OCT \n", + "+ Execut; Year 1984 Month NOV \n", + "+ Execut; Year 1984 Month DEC \n", + "+ Execut; Year 1985 Month JAN \n", + "+ Execut; Year 1985 Month FEB \n", + "+ Execut; Year 1985 Month MAR \n", + "+ Execut; Year 1985 Month APR \n", + "+ Execut; Year 1985 Month MAY \n", + "+ Execut; Year 1985 Month JUN \n", + "+ Execut; Year 1985 Month JUL \n", + "+ Execut; Year 1985 Month AUG \n", + "+ Execut; Year 1985 Month SEP \n", + "+ Execut; Year 1985 Month OCT \n", + "+ Execut; Year 1985 Month NOV \n", + "+ Execut; Year 1985 Month DEC \n", + "+ Execut; Year 1986 Month JAN \n", + "+ Execut; Year 1986 Month FEB \n", + "+ Execut; Year 1986 Month MAR \n", + "+ Execut; Year 1986 Month APR \n", + "+ Execut; Year 1986 Month MAY \n", + "+ Execut; Year 1986 Month JUN \n", + "+ Execut; Year 1986 Month JUL \n", + "+ Execut; Year 1986 Month AUG \n", + "+ Execut; Year 1986 Month SEP \n", + "+ Execut; Year 1986 Month OCT \n", + "+ Execut; Year 1986 Month NOV \n", + "+ Execut; Year 1986 Month DEC \n", + "+ Execut; Year 1987 Month JAN \n", + "+ Execut; Year 1987 Month FEB \n", + "+ Execut; Year 1987 Month MAR \n", + "+ Execut; Year 1987 Month APR \n", + "+ Execut; Year 1987 Month MAY \n", + "+ Execut; Year 1987 Month JUN \n", + "+ Execut; Year 1987 Month JUL \n", + "+ Execut; Year 1987 Month AUG \n", + "+ Execut; Year 1987 Month SEP \n", + "+ Execut; Year 1987 Month OCT \n", + "+ Execut; Year 1987 Month NOV \n", + "+ Execut; Year 1987 Month DEC \n", + "+ Execut; Year 1988 Month JAN \n", + "+ Execut; Year 1988 Month FEB \n", + "+ Execut; Year 1988 Month MAR \n", + "+ Execut; Year 1988 Month APR \n", + "+ Execut; Year 1988 Month MAY \n", + "+ Execut; Year 1988 Month JUN \n", + "+ Execut; Year 1988 Month JUL \n", + "+ Execut; Year 1988 Month AUG \n", + "+ Execut; Year 1988 Month SEP \n", + "+ Execut; Year 1988 Month OCT \n", + "+ Execut; Year 1988 Month NOV \n", + "+ Execut; Year 1988 Month DEC \n", + "+ Execut; Year 1989 Month JAN \n", + "+ Execut; Year 1989 Month FEB \n", + "+ Execut; Year 1989 Month MAR \n", + "+ Execut; Year 1989 Month APR \n", + "+ Execut; Year 1989 Month MAY \n", + "+ Execut; Year 1989 Month JUN \n", + "+ Execut; Year 1989 Month JUL \n", + "+ Execut; Year 1989 Month AUG \n", + "+ Execut; Year 1989 Month SEP \n", + "+ Execut; Year 1989 Month OCT \n", + "+ Execut; Year 1989 Month NOV \n", + "+ Execut; Year 1989 Month DEC \n", + "+ Execut; Year 1990 Month JAN \n", + "+ Execut; Year 1990 Month FEB \n", + "+ Execut; Year 1990 Month MAR \n", + "+ Execut; Year 1990 Month APR \n", + "+ Execut; Year 1990 Month MAY \n", + "+ Execut; Year 1990 Month JUN \n", + "+ Execut; Year 1990 Month JUL \n", + "+ Execut; Year 1990 Month AUG \n", + "+ Execut; Year 1990 Month SEP \n", + "+ Execut; Year 1990 Month OCT \n", + "+ Execut; Year 1990 Month NOV \n", + "+ Execut; Year 1990 Month DEC \n", + "+ Execut; Year 1991 Month JAN \n", + "+ Execut; Year 1991 Month FEB \n", + "+ Execut; Year 1991 Month MAR \n", + "+ Execut; Year 1991 Month APR \n", + "+ Execut; Year 1991 Month MAY \n", + "+ Execut; Year 1991 Month JUN \n", + "+ Execut; Year 1991 Month JUL \n", + "+ Execut; Year 1991 Month AUG \n", + "+ Execut; Year 1991 Month SEP \n", + "+ Execut; Year 1991 Month OCT \n", + "+ Execut; Year 1991 Month NOV \n", + "+ Execut; Year 1991 Month DEC \n", + "+ Execut; Year 1992 Month JAN \n", + "+ Execut; Year 1992 Month FEB \n", + "+ Execut; Year 1992 Month MAR \n", + "+ Execut; Year 1992 Month APR \n", + "+ Execut; Year 1992 Month MAY \n", + "+ Execut; Year 1992 Month JUN \n", + "+ Execut; Year 1992 Month JUL \n", + "+ Execut; Year 1992 Month AUG \n", + "+ Execut; Year 1992 Month SEP \n", + "+ Execut; Year 1992 Month OCT \n", + "+ Execut; Year 1992 Month NOV \n", + "+ Execut; Year 1992 Month DEC \n", + "+ Execut; Year 1993 Month JAN \n", + "+ Execut; Year 1993 Month FEB \n", + "+ Execut; Year 1993 Month MAR \n", + "+ Execut; Year 1993 Month APR \n", + "+ Execut; Year 1993 Month MAY \n", + "+ Execut; Year 1993 Month JUN \n", + "+ Execut; Year 1993 Month JUL \n", + "+ Execut; Year 1993 Month AUG \n", + "+ Execut; Year 1993 Month SEP \n", + "+ Execut; Year 1993 Month OCT \n", + "+ Execut; Year 1993 Month NOV \n", + "+ Execut; Year 1993 Month DEC \n", + "+ Execut; Year 1994 Month JAN \n", + "+ Execut; Year 1994 Month FEB \n", + "+ Execut; Year 1994 Month MAR \n", + "+ Execut; Year 1994 Month APR \n", + "+ Execut; Year 1994 Month MAY \n", + "+ Execut; Year 1994 Month JUN \n", + "+ Execut; Year 1994 Month JUL \n", + "+ Execut; Year 1994 Month AUG \n", + "+ Execut; Year 1994 Month SEP \n", + "+ Execut; Year 1994 Month OCT \n", + "+ Execut; Year 1994 Month NOV \n", + "+ Execut; Year 1994 Month DEC \n", + "+ Execut; Year 1995 Month JAN \n", + "+ Execut; Year 1995 Month FEB \n", + "+ Execut; Year 1995 Month MAR \n", + "+ Execut; Year 1995 Month APR \n", + "+ Execut; Year 1995 Month MAY \n", + "+ Execut; Year 1995 Month JUN \n", + "+ Execut; Year 1995 Month JUL \n", + "+ Execut; Year 1995 Month AUG \n", + "+ Execut; Year 1995 Month SEP \n", + "+ Execut; Year 1995 Month OCT \n", + "+ Execut; Year 1995 Month NOV \n", + "+ Execut; Year 1995 Month DEC \n", + "+ Execut; Year 1996 Month JAN \n", + "+ Execut; Year 1996 Month FEB \n", + "+ Execut; Year 1996 Month MAR \n", + "+ Execut; Year 1996 Month APR \n", + "+ Execut; Year 1996 Month MAY \n", + "+ Execut; Year 1996 Month JUN \n", + "+ Execut; Year 1996 Month JUL \n", + "+ Execut; Year 1996 Month AUG \n", + "+ Execut; Year 1996 Month SEP \n", + "+ Execut; Year 1996 Month OCT \n", + "+ Execut; Year 1996 Month NOV \n", + "+ Execut; Year 1996 Month DEC \n", + "+ Execut; Year 1997 Month JAN \n", + "+ Execut; Year 1997 Month FEB \n", + "+ Execut; Year 1997 Month MAR \n", + "+ Execut; Year 1997 Month APR \n", + "+ Execut; Year 1997 Month MAY \n", + "+ Execut; Year 1997 Month JUN \n", + "+ Execut; Year 1997 Month JUL \n", + "+ Execut; Year 1997 Month AUG \n", + "+ Execut; Year 1997 Month SEP \n", + "+ Execut; Year 1997 Month OCT \n", + "+ Execut; Year 1997 Month NOV \n", + "+ Execut; Year 1997 Month DEC \n", + "+ Execut; Year 1998 Month JAN \n", + "+ Execut; Year 1998 Month FEB \n", + "+ Execut; Year 1998 Month MAR \n", + "+ Execut; Year 1998 Month APR \n", + "+ Execut; Year 1998 Month MAY \n", + "+ Execut; Year 1998 Month JUN \n", + "+ Execut; Year 1998 Month JUL \n", + "+ Execut; Year 1998 Month AUG \n", + "+ Execut; Year 1998 Month SEP \n", + "+ Execut; Year 1998 Month OCT \n", + "+ Execut; Year 1998 Month NOV \n", + "+ Execut; Year 1998 Month DEC \n", + "+ Execut; Year 1999 Month JAN \n", + "+ Execut; Year 1999 Month FEB \n", + "+ Execut; Year 1999 Month MAR \n", + "+ Execut; Year 1999 Month APR \n", + "+ Execut; Year 1999 Month MAY \n", + "+ Execut; Year 1999 Month JUN \n", + "+ Execut; Year 1999 Month JUL \n", + "+ Execut; Year 1999 Month AUG \n", + "+ Execut; Year 1999 Month SEP \n", + "+ Execut; Year 1999 Month OCT \n", + "+ Execut; Year 1999 Month NOV \n", + "+ Execut; Year 1999 Month DEC \n", + "+ Execut; Year 2000 Month JAN \n", + "+ Execut; Year 2000 Month FEB \n", + "+ Execut; Year 2000 Month MAR \n", + "+ Execut; Year 2000 Month APR \n", + "+ Execut; Year 2000 Month MAY \n", + "+ Execut; Year 2000 Month JUN \n", + "+ Execut; Year 2000 Month JUL \n", + "+ Execut; Year 2000 Month AUG \n", + "+ Execut; Year 2000 Month SEP \n", + "+ Execut; Year 2000 Month OCT \n", + "+ Execut; Year 2000 Month NOV \n", + "+ Execut; Year 2000 Month DEC \n", + "+ Execut; Year 2001 Month JAN \n", + "+ Execut; Year 2001 Month FEB \n", + "+ Execut; Year 2001 Month MAR \n", + "+ Execut; Year 2001 Month APR \n", + "+ Execut; Year 2001 Month MAY \n", + "+ Execut; Year 2001 Month JUN \n", + "+ Execut; Year 2001 Month JUL \n", + "+ Execut; Year 2001 Month AUG \n", + "+ Execut; Year 2001 Month SEP \n", + "+ Execut; Year 2001 Month OCT \n", + "+ Execut; Year 2001 Month NOV \n", + "+ Execut; Year 2001 Month DEC \n", + "+ Execut; Year 2002 Month JAN \n", + "+ Execut; Year 2002 Month FEB \n", + "+ Execut; Year 2002 Month MAR \n", + "+ Execut; Year 2002 Month APR \n", + "+ Execut; Year 2002 Month MAY \n", + "+ Execut; Year 2002 Month JUN \n", + "+ Execut; Year 2002 Month JUL \n", + "+ Execut; Year 2002 Month AUG \n", + "+ Execut; Year 2002 Month SEP \n", + "+ Execut; Year 2002 Month OCT \n", + "+ Execut; Year 2002 Month NOV \n", + "+ Execut; Year 2002 Month DEC \n", + "+ Execut; Year 2003 Month JAN \n", + "+ Execut; Year 2003 Month FEB \n", + "+ Execut; Year 2003 Month MAR \n", + "+ Execut; Year 2003 Month APR \n", + "+ Execut; Year 2003 Month MAY \n", + "+ Execut; Year 2003 Month JUN \n", + "+ Execut; Year 2003 Month JUL \n", + "+ Execut; Year 2003 Month AUG \n", + "+ Execut; Year 2003 Month SEP \n", + "+ Execut; Year 2003 Month OCT \n", + "+ Execut; Year 2003 Month NOV \n", + "+ Execut; Year 2003 Month DEC \n", + "+ Execut; Year 2004 Month JAN \n", + "+ Execut; Year 2004 Month FEB \n", + "+ Execut; Year 2004 Month MAR \n", + "+ Execut; Year 2004 Month APR \n", + "+ Execut; Year 2004 Month MAY \n", + "+ Execut; Year 2004 Month JUN \n", + "+ Execut; Year 2004 Month JUL \n", + "+ Execut; Year 2004 Month AUG \n", + "+ Execut; Year 2004 Month SEP \n", + "+ Execut; Year 2004 Month OCT \n", + "+ Execut; Year 2004 Month NOV \n", + "+ Execut; Year 2004 Month DEC \n", + "+ Execut; Year 2005 Month JAN \n", + "+ Execut; Year 2005 Month FEB \n", + "+ Execut; Year 2005 Month MAR \n", + "+ Execut; Year 2005 Month APR \n", + "+ Execut; Year 2005 Month MAY \n", + "+ Execut; Year 2005 Month JUN \n", + "+ Execut; Year 2005 Month JUL \n", + "+ Execut; Year 2005 Month AUG \n", + "+ Execut; Year 2005 Month SEP \n", + "+ Execut; Year 2005 Month OCT \n", + "+ Execut; Year 2005 Month NOV \n", + "+ Execut; Year 2005 Month DEC \n", + "+ Execut; Year 2006 Month JAN \n", + "+ Execut; Year 2006 Month FEB \n", + "+ Execut; Year 2006 Month MAR \n", + "+ Execut; Year 2006 Month APR \n", + "+ Execut; Year 2006 Month MAY \n", + "+ Execut; Year 2006 Month JUN \n", + "+ Execut; Year 2006 Month JUL \n", + "+ Execut; Year 2006 Month AUG \n", + "+ Execut; Year 2006 Month SEP \n", + "+ Execut; Year 2006 Month OCT \n", + "+ Execut; Year 2006 Month NOV \n", + "+ Execut; Year 2006 Month DEC \n", + "+ Execut; Year 2007 Month JAN \n", + "+ Execut; Year 2007 Month FEB \n", + "+ Execut; Year 2007 Month MAR \n", + "+ Execut; Year 2007 Month APR \n", + "+ Execut; Year 2007 Month MAY \n", + "+ Execut; Year 2007 Month JUN \n", + "+ Execut; Year 2007 Month JUL \n", + "+ Execut; Year 2007 Month AUG \n", + "+ Execut; Year 2007 Month SEP \n", + "+ Execut; Year 2007 Month OCT \n", + "+ Execut; Year 2007 Month NOV \n", + "+ Execut; Year 2007 Month DEC \n", + "+ Execut; Year 2008 Month JAN \n", + "+ Execut; Year 2008 Month FEB \n", + "+ Execut; Year 2008 Month MAR \n", + "+ Execut; Year 2008 Month APR \n", + "+ Execut; Year 2008 Month MAY \n", + "+ Execut; Year 2008 Month JUN \n", + "+ Execut; Year 2008 Month JUL \n", + "+ Execut; Year 2008 Month AUG \n", + "+ Execut; Year 2008 Month SEP \n", + "+ Execut; Year 2008 Month OCT \n", + "+ Execut; Year 2008 Month NOV \n", + "+ Execut; Year 2008 Month DEC \n", + "+ Execut; Year 2009 Month JAN \n", + "+ Execut; Year 2009 Month FEB \n", + "+ Execut; Year 2009 Month MAR \n", + "+ Execut; Year 2009 Month APR \n", + "+ Execut; Year 2009 Month MAY \n", + "+ Execut; Year 2009 Month JUN \n", + "+ Execut; Year 2009 Month JUL \n", + "+ Execut; Year 2009 Month AUG \n", + "+ Execut; Year 2009 Month SEP \n", + "+ Execut; Year 2009 Month OCT \n", + "+ Execut; Year 2009 Month NOV \n", + "+ Execut; Year 2009 Month DEC \n", + "+ Execut; Year 2010 Month JAN \n", + "+ Execut; Year 2010 Month FEB \n", + "+ Execut; Year 2010 Month MAR \n", + "+ Execut; Year 2010 Month APR \n", + "+ Execut; Year 2010 Month MAY \n", + "+ Execut; Year 2010 Month JUN \n", + "+ Execut; Year 2010 Month JUL \n", + "+ Execut; Year 2010 Month AUG \n", + "+ Execut; Year 2010 Month SEP \n", + "+ Execut; Year 2010 Month OCT \n", + "+ Execut; Year 2010 Month NOV \n", + "+ Execut; Year 2010 Month DEC \n", + "+ Execut; Year 2011 Month JAN \n", + "+ Execut; Year 2011 Month FEB \n", + "+ Execut; Year 2011 Month MAR \n", + "+ Execut; Year 2011 Month APR \n", + "+ Execut; Year 2011 Month MAY \n", + "+ Execut; Year 2011 Month JUN \n", + "+ Execut; Year 2011 Month JUL \n", + "+ Execut; Year 2011 Month AUG \n", + "+ Execut; Year 2011 Month SEP \n", + "+ Execut; Year 2011 Month OCT \n", + "+ Execut; Year 2011 Month NOV \n", + "+ Execut; Year 2011 Month DEC \n", + "+ Execut; Year 2012 Month JAN \n", + "+ Execut; Year 2012 Month FEB \n", + "+ Execut; Year 2012 Month MAR \n", + "+ Execut; Year 2012 Month APR \n", + "+ Execut; Year 2012 Month MAY \n", + "+ Execut; Year 2012 Month JUN \n", + "+ Execut; Year 2012 Month JUL \n", + "+ Execut; Year 2012 Month AUG \n", + "+ Execut; Year 2012 Month SEP \n", + "+ Execut; Year 2012 Month OCT \n", + "+ Execut; Year 2012 Month NOV \n", + "+ Execut; Year 2012 Month DEC \n", + "+ Execut; Year 2013 Month JAN \n", + "+ Execut; Year 2013 Month FEB \n", + "+ Execut; Year 2013 Month MAR \n", + "+ Execut; Year 2013 Month APR \n", + "+ Execut; Year 2013 Month MAY \n", + "+ Execut; Year 2013 Month JUN \n", + "+ Execut; Year 2013 Month JUL \n", + "+ Execut; Year 2013 Month AUG \n", + "+ Execut; Year 2013 Month SEP \n", + "\n", + "________________________________________________________________________\n", + " Execut; On Year 1973 Month JUN Day 1\n", + " The maximum number of reoperations 169\n", + "\n", + "________________________________________________________________________\n", + " Execut; Writing reports\n", + "\n", + "________________________________________________________________________\n", + " Execut; \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutRes \n", + "+ Printing Reservoir Summary 1 of 41; or 2. % Complete\n", + "+ Printing Reservoir Summary 26 of 41; or 63. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutDivW \n", + "+ Printing Diversion & Stream Summary 1 of 725; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 26 of 725; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 51 of 725; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 76 of 725; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 101 of 725; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 126 of 725; or 17. % Complete\n", + "+ Printing Diversion & Stream Summary 151 of 725; or 21. % Complete\n", + "+ Printing Diversion & Stream Summary 176 of 725; or 24. % Complete\n", + "+ Printing Diversion & Stream Summary 201 of 725; or 28. % Complete\n", + "+ Printing Diversion & Stream Summary 226 of 725; or 31. % Complete\n", + "+ Printing Diversion & Stream Summary 251 of 725; or 35. % Complete\n", + "+ Printing Diversion & Stream Summary 276 of 725; or 38. % Complete\n", + "+ Printing Diversion & Stream Summary 301 of 725; or 42. % Complete\n", + "+ Printing Diversion & Stream Summary 326 of 725; or 45. % Complete\n", + "+ Printing Diversion & Stream Summary 351 of 725; or 48. % Complete\n", + "+ Printing Diversion & Stream Summary 376 of 725; or 52. % Complete\n", + "+ Printing Diversion & Stream Summary 401 of 725; or 55. % Complete\n", + "+ Printing Diversion & Stream Summary 426 of 725; or 59. % Complete\n", + "+ Printing Diversion & Stream Summary 451 of 725; or 62. % Complete\n", + "+ Printing Diversion & Stream Summary 476 of 725; or 66. % Complete\n", + "+ Printing Diversion & Stream Summary 501 of 725; or 69. % Complete\n", + "+ Printing Diversion & Stream Summary 526 of 725; or 73. % Complete\n", + "+ Printing Diversion & Stream Summary 551 of 725; or 76. % Complete\n", + "+ Printing Diversion & Stream Summary 576 of 725; or 79. % Complete\n", + "+ Printing Diversion & Stream Summary 601 of 725; or 83. % Complete\n", + "+ Printing Diversion & Stream Summary 626 of 725; or 86. % Complete\n", + "+ Printing Diversion & Stream Summary 651 of 725; or 90. % Complete\n", + "+ Printing Diversion & Stream Summary 676 of 725; or 93. % Complete\n", + "+ Printing Diversion & Stream Summary 701 of 725; or 97. % Complete\n", + "+ Printing Diversion & Stream Summary 725 of 725; or 100. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutOpr \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutXss \n", + "+ Printing Structure Summary (*.xss) 0 of 544; or 0. % Complete\n", + "+ Printing Structure Summary (*.xss) 25 of 544; or 5. % Complete\n", + "+ Printing Structure Summary (*.xss) 50 of 544; or 9. % Complete\n", + "+ Printing Structure Summary (*.xss) 75 of 544; or 14. % Complete\n", + "+ Printing Structure Summary (*.xss) 100 of 544; or 19. % Complete\n", + "+ Printing Structure Summary (*.xss) 125 of 544; or 23. % Complete\n", + "+ Printing Structure Summary (*.xss) 150 of 544; or 28. % Complete\n", + "+ Printing Structure Summary (*.xss) 175 of 544; or 32. % Complete\n", + "+ Printing Structure Summary (*.xss) 200 of 544; or 37. % Complete\n", + "+ Printing Structure Summary (*.xss) 225 of 544; or 42. % Complete\n", + "+ Printing Structure Summary (*.xss) 250 of 544; or 46. % Complete\n", + "+ Printing Structure Summary (*.xss) 275 of 544; or 51. % Complete\n", + "+ Printing Structure Summary (*.xss) 300 of 544; or 55. % Complete\n", + "+ Printing Structure Summary (*.xss) 325 of 544; or 60. % Complete\n", + "+ Printing Structure Summary (*.xss) 350 of 544; or 65. % Complete\n", + "+ Printing Structure Summary (*.xss) 375 of 544; or 69. % Complete\n", + "+ Printing Structure Summary (*.xss) 400 of 544; or 74. % Complete\n", + "+ Printing Structure Summary (*.xss) 425 of 544; or 78. % Complete\n", + "+ Printing Structure Summary (*.xss) 450 of 544; or 83. % Complete\n", + "+ Printing Structure Summary (*.xss) 475 of 544; or 88. % Complete\n", + "+ Printing Structure Summary (*.xss) 500 of 544; or 92. % Complete\n", + "+ Printing Structure Summary (*.xss) 525 of 544; or 97. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine Outifr\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutPln \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutWW \n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Run output files are:\n", + " \n", + " Diversion output: *.xdd\n", + " Reservoir output: *.xre\n", + " Operating Rule Info: *.xop\n", + " Instream Reach Info: *.xir\n", + " Structure Summary: *.xss\n", + " Call (Control) Summary: *.xca\n", + " Plan Output: *.xpl\n", + " WWSP Output: *.xww\n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Termination\n", + " Statem; See detailed messages in dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S1_1/gm2015B_S1_1.log \n", + " Stop 0\n" + ] + } + ], "source": [ "# set realization and sample\n", "realization = 1\n", @@ -355,41 +4807,41 @@ "\n", "# read RSP template\n", "with open(eva_template_file) as template_obj:\n", + " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", + " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - "\n", + " \n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"EVA\": f\"../../input_files/gm2015B_{scenario}.eva\"}\n", - "\n", + " \n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - "\n", + " \n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - "\n", + " \n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"gm2015B_{scenario}.rsp\")\n", - "\n", + " \n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - "\n", + " \n", " # construct simulated basin path\n", - " simulated_basin_path = os.path.join(\n", - " simulated_scenario_dir, f\"gm2015B_{scenario}\"\n", - " )\n", + " simulated_basin_path = os.path.join(simulated_scenario_dir, f\"gm2015B_{scenario}\")\n", "\n", " # run StateMod\n", " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", - " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])" + " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n" ] }, { @@ -410,7 +4862,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "308aa3e5-b705-4a4f-83e4-39e96b18303c", "metadata": { "tags": [] @@ -418,45 +4870,43 @@ "outputs": [], "source": [ "# SOW 1\n", - "output_dir = os.path.join(scenarios_dir, \"S0_1\")\n", + "output_dir= os.path.join(scenarios_dir, \"S0_1\")\n", "\n", "# path the the xre file\n", - "xre_file = os.path.join(output_dir, \"gm2015B_S0_1.xre\")\n", + "xre_file = os.path.join(output_dir, \"gm2015B_S0_1.xre\") \n", "\n", "# structure ID for reservoir of interest\n", - "structure_ID = \"6203532\"\n", + "structure_ID = '6203532' \n", "\n", "# name of the reservoir\n", - "structure_name = \"Blue_Mesa\"\n", + "structure_name = 'Blue_Mesa' \n", "\n", "# extract the target info into a Pandas data frame\n", - "df = stm.extract_xre_data(\n", - " structure_name=structure_name,\n", - " structure_id=structure_ID,\n", - " input_file=xre_file,\n", - " basin_name=None,\n", - " output_directory=output_dir,\n", - " write_csv=True,\n", - " write_parquet=None,\n", + "df = stm.extract_xre_data(structure_name=structure_name,\n", + " structure_id=structure_ID,\n", + " input_file=xre_file,\n", + " basin_name=None,\n", + " output_directory=output_dir,\n", + " write_csv=True,\n", + " write_parquet=None\n", ")\n", "\n", "# SOW 2\n", - "output_dir = os.path.join(scenarios_dir, \"S1_1\")\n", + "output_dir= os.path.join(scenarios_dir, \"S1_1\")\n", "\n", "# path the the xre file\n", - "xre_file = os.path.join(output_dir, \"gm2015B_S1_1.xre\")\n", + "xre_file = os.path.join(output_dir, \"gm2015B_S1_1.xre\") \n", "\n", "\n", "# extract the target info into a Pandas data frame\n", - "df = stm.extract_xre_data(\n", - " structure_name=structure_name,\n", - " structure_id=structure_ID,\n", - " input_file=xre_file,\n", - " basin_name=None,\n", - " output_directory=output_dir,\n", - " write_csv=True,\n", - " write_parquet=None,\n", - ")" + "df = stm.extract_xre_data(structure_name=structure_name,\n", + " structure_id=structure_ID,\n", + " input_file=xre_file,\n", + " basin_name=None,\n", + " output_directory=output_dir,\n", + " write_csv=True,\n", + " write_parquet=None\n", + ")\n" ] }, { @@ -469,45 +4919,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "6bb1ff51-b736-46dd-98f7-d9b4af9121b9", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# historic reservoir directory\n", "historic_res_dir = os.path.join(data_dir, \"historic_reservoir_levels\")\n", "blue_mesa_file = os.path.join(historic_res_dir, \"Blue_Mesa_xre_data.csv\")\n", "\n", - "# Import baseline dataframe\n", - "baseline = pd.read_csv(\n", - " blue_mesa_file, index_col=False, usecols=[\"Year\", \"Init. Storage\"]\n", - ")\n", - "baseline = baseline.groupby(\"Year\").mean().reset_index()\n", + "# Import baseline dataframe \n", + "baseline = pd.read_csv(blue_mesa_file, index_col=False, usecols=['Year','Init. Storage']) \n", + "baseline = baseline.groupby('Year').mean().reset_index()\n", "\n", "# Import SOW1\n", "s0_1_file = os.path.join(scenarios_dir, \"S0_1\", \"Blue_Mesa_xre_data.csv\")\n", - "SOW1 = pd.read_csv(s0_1_file, index_col=False, usecols=[\"Year\", \"Init. Storage\"])\n", - "SOW1 = SOW1.groupby(\"Year\").mean().reset_index()\n", - "\n", + "SOW1 = pd.read_csv(s0_1_file, index_col=False, usecols=['Year','Init. Storage']) \n", + "SOW1 = SOW1.groupby('Year').mean().reset_index()\n", + " \n", "# Import SOW2\n", "s1_1_file = os.path.join(scenarios_dir, \"S1_1\", \"Blue_Mesa_xre_data.csv\")\n", - "SOW2 = pd.read_csv(s1_1_file, index_col=False, usecols=[\"Year\", \"Init. Storage\"])\n", - "SOW2 = SOW2.groupby(\"Year\").mean().reset_index()\n", - "\n", - "# Plot reservoir levels\n", + "SOW2 = pd.read_csv(s1_1_file, index_col=False, usecols=['Year','Init. Storage']) \n", + "SOW2 = SOW2.groupby('Year').mean().reset_index()\n", + " \n", + "# Plot reservoir levels \n", "fig, ax = plt.subplots()\n", "\n", - "plt.plot(baseline[\"Year\"], baseline[\"Init. Storage\"], label=\"Baseline\")\n", - "plt.plot(SOW1[\"Year\"], SOW1[\"Init. Storage\"], label=\"Reduced Evaporation\")\n", - "plt.plot(SOW2[\"Year\"], SOW2[\"Init. Storage\"], label=\"Increased Evaporation\")\n", + "plt.plot(baseline['Year'], baseline['Init. Storage'],label='Baseline')\n", + "plt.plot(SOW1['Year'], SOW1['Init. Storage'],label='Reduced Evaporation')\n", + "plt.plot(SOW2['Year'], SOW2['Init. Storage'],label='Increased Evaporation')\n", "\n", "plt.title(\"Blue Mesa Storage\")\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Reservoir Storage (AF)\")\n", "\n", - "plt.legend()" + "plt.legend()\n" ] }, { diff --git a/notebooks/N3_Reservoir_File_Modification.ipynb b/notebooks/N3_Reservoir_File_Modification.ipynb index 2798178..f440f62 100644 --- a/notebooks/N3_Reservoir_File_Modification.ipynb +++ b/notebooks/N3_Reservoir_File_Modification.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "46286883-98cc-4c5b-94a2-86de7f92449e", "metadata": { "tags": [] @@ -37,13 +37,12 @@ "import logging\n", "import os\n", "import pickle\n", - "import subprocess\n", "from string import Template\n", + "import subprocess\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd\n", - "\n", + "import pandas as pd \n", "import statemodify as stm" ] }, @@ -69,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "7e2ddfdd-15db-4bc4-a7eb-98b136f6adf9", "metadata": { "tags": [] @@ -89,7 +88,12 @@ "statemod_exe = os.path.join(root_dir, \"statemod\")\n", "\n", "# data directory and root name for the target basin\n", - "data_dir = os.path.join(home_dir, \"data\", \"cm2015_StateMod\", \"StateMod\")\n", + "data_dir = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"cm2015_StateMod\",\n", + " \"StateMod\"\n", + ")\n", "\n", "# directory to the target basin input files with root name for the basin\n", "basin_path = os.path.join(data_dir, \"cm2015B\")\n", @@ -102,7 +106,11 @@ "\n", "\n", "# path to res template file\n", - "res_template_file = os.path.join(home_dir, \"data\", \"cm2015B_template_res.rsp\")" + "res_template_file = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"cm2015B_template_res.rsp\"\n", + ")" ] }, { @@ -119,19 +127,2793 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "d86d0e12-515a-4af5-94b6-0297b468de2f", "metadata": { "scrolled": true, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Parse; Command line argument: \n", + " cm2015B -simulate \n", + "________________________________________________________________________\n", + "\n", + " StateMod \n", + " State of Colorado - Water Supply Planning Model \n", + "\n", + " Version: 15.00.01\n", + " Last revision date: 2015/10/28\n", + "\n", + "________________________________________________________________________\n", + "\n", + " Opening log file cm2015B.log \n", + " \n", + " Subroutine Execut\n", + " Subroutine Datinp\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Control File (*.ctl) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Network File (*.rin)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Reservoir Station File (*.res)\n", + " Subroutine GetRes\n", + "\n", + " GetRes; Reservoir Station File (*.res) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Diversion Station File (*.dds)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Station File (*.ris)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Instream Flow Station File (*.ifs) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Well Station File (*.wes) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Plan Station File (*.pln) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Gage File (*.rig) \n", + " Subroutine Riginp\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Instream Flow Right File (*.ifr) 20 1 98\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Reservoir Right File (*.rer) \n", + "\n", + "________________________________________________________________________\n", + " Riginp; Direct Diversion Right File (*.ddr) \n", + "\n", + "________________________________________________________________________\n", + " Oprinp; Operational Right File (*.opr) \n", + " Mdainp.for\n", + "\n", + "________________________________________________________________________\n", + " Mdainp; Instream flow Demand file - Annual (*.ifa) \n", + " Subroutine Execut\n", + "\n", + " Mdainp.for\n", + "+ Execut; Year 1908 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1908 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1908 Month DEC Day 1 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Execut; Year 1941 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1941 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1941 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1941 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1941 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1942 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1942 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1942 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1942 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1942 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1942 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1942 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1942 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1942 Month SEP Day 1 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DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1945 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1945 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1945 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1945 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1945 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1945 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1945 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1945 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1945 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1945 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1945 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1945 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1946 Month JAN Day 1 Reoperation 1 Annual Total 3\n", 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Execut; Year 1948 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1948 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1948 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1948 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1948 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1948 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1948 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1948 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1949 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1949 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1949 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1949 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1949 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1949 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1949 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1949 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1949 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1949 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1949 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1949 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1950 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1950 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1950 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1950 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1950 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1950 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1950 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1950 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1950 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1950 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1950 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1950 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1951 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1951 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1951 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1951 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1951 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1951 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1951 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1951 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1951 Month SEP Day 1 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DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1954 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1954 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1954 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1954 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1954 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1954 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1954 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1954 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1954 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1954 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1954 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1954 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1955 Month JAN Day 1 Reoperation 1 Annual Total 3\n", 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Execut; Year 1957 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1957 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1957 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1957 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1957 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1957 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1957 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1957 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1958 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1958 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1958 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1958 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1958 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1958 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1958 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1958 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1958 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1958 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1958 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1958 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1959 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1959 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1959 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1959 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1959 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1959 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1959 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1959 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1959 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1959 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1959 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1959 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1960 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1960 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1960 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1960 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1960 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1960 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1960 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1960 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1960 Month SEP Day 1 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Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1992 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1992 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1992 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1992 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1992 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1992 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1992 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1992 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1992 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1993 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1993 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1993 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1993 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1993 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1993 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1993 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1993 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1993 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1993 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1993 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1993 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1994 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1994 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1994 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1994 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1994 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1994 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1994 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1994 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1994 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1994 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1994 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1994 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1995 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1995 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1995 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1995 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1995 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1995 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1995 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1995 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1995 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1995 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1995 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1995 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1996 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1996 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1996 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1996 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1996 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1996 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1996 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1996 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1996 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1996 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1996 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1996 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1997 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1997 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1997 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1997 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1997 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1997 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1997 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1997 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1997 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1997 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1997 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1997 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1998 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1998 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1998 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1998 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1998 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1998 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1998 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1998 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1998 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1998 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1998 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1998 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1999 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1999 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1999 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1999 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1999 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1999 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1999 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1999 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1999 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1999 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1999 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1999 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2000 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2000 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2000 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2000 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2000 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2000 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2000 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2000 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2000 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2000 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2000 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2000 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2001 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2001 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2001 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2001 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2001 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2001 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2001 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2001 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2001 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2001 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2001 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2001 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2002 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2002 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2002 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2002 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2002 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2002 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2002 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2002 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2002 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2002 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2002 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2002 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2003 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2003 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2003 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2003 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2003 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2003 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2003 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2003 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2003 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2003 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2003 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2003 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2004 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2004 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2004 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2004 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2004 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2004 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2004 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2004 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2004 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2004 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2004 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2004 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2005 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2005 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2005 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2005 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2005 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2005 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2005 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2005 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2005 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2005 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2005 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2005 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2006 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2006 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2006 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2006 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2006 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2006 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2006 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2006 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2006 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2006 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2006 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2006 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2007 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2007 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2007 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2007 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2007 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2007 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2007 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2007 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2007 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2007 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2007 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2007 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2008 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2008 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2008 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2008 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2008 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2008 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2008 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2008 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2008 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2008 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2008 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2008 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2009 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2009 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2009 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2009 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2009 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2009 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2009 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2009 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2009 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2009 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2009 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2009 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2010 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2010 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2010 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2010 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2010 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2010 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2010 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2010 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2010 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2010 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2010 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2010 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2011 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2011 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2011 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2011 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2011 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2011 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2011 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2011 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2011 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2011 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2011 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2011 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2012 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2012 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2012 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2012 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2012 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2012 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2012 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2012 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2012 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2012 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2012 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2012 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2013 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2013 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2013 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2013 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2013 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2013 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2013 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2013 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2013 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + "\n", + "________________________________________________________________________\n", + " Execut; On Year 2013 Month SEP Day 1\n", + " The maximum number of reoperations 1\n", + "\n", + "________________________________________________________________________\n", + " Execut; Writing reports\n", + "\n", + "________________________________________________________________________\n", + " Execut; \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutRes \n", + "+ Printing Reservoir Summary 1 of 36; or 3. % Complete\n", + "+ Printing Reservoir Summary 2 of 36; or 6. % Complete\n", + "+ Printing Reservoir Summary 3 of 36; or 8. % Complete\n", + "+ Printing Reservoir Summary 4 of 36; or 11. % Complete\n", + "+ Printing Reservoir Summary 5 of 36; or 14. % Complete\n", + "+ Printing Reservoir Summary 8 of 36; or 22. % Complete\n", + "+ Printing Reservoir Summary 9 of 36; or 25. % Complete\n", + "+ Printing Reservoir Summary 10 of 36; or 28. % Complete\n", + "+ Printing Reservoir Summary 11 of 36; or 31. % Complete\n", + "+ Printing Reservoir Summary 12 of 36; or 33. % Complete\n", + "+ Printing Reservoir Summary 13 of 36; or 36. % Complete\n", + "+ Printing Reservoir Summary 14 of 36; or 39. % Complete\n", + "+ Printing Reservoir Summary 15 of 36; or 42. % Complete\n", + "+ Printing Reservoir Summary 16 of 36; or 44. % Complete\n", + "+ Printing Reservoir Summary 17 of 36; or 47. % Complete\n", + "+ Printing Reservoir Summary 18 of 36; or 50. % Complete\n", + "+ Printing Reservoir Summary 19 of 36; or 53. % Complete\n", + "+ Printing Reservoir Summary 20 of 36; or 56. % Complete\n", + "+ Printing Reservoir Summary 21 of 36; or 58. % Complete\n", + "+ Printing Reservoir Summary 22 of 36; or 61. % Complete\n", + "+ Printing Reservoir Summary 23 of 36; or 64. % Complete\n", + "+ Printing Reservoir Summary 24 of 36; or 67. % Complete\n", + "+ Printing Reservoir Summary 25 of 36; or 69. % Complete\n", + "+ Printing Reservoir Summary 26 of 36; or 72. % Complete\n", + "+ Printing Reservoir Summary 27 of 36; or 75. % Complete\n", + "+ Printing Reservoir Summary 28 of 36; or 78. % Complete\n", + "+ Printing Reservoir Summary 29 of 36; or 81. % Complete\n", + "+ Printing Reservoir Summary 30 of 36; or 83. % Complete\n", + "+ Printing Reservoir Summary 31 of 36; or 86. % Complete\n", + "+ Printing Reservoir Summary 32 of 36; or 89. % Complete\n", + "+ Printing Reservoir Summary 33 of 36; or 92. % Complete\n", + "+ Printing Reservoir Summary 34 of 36; or 94. % Complete\n", + "+ Printing Reservoir Summary 35 of 36; or 97. % Complete\n", + "+ Printing Reservoir Summary 36 of 36; or 100. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutDivW \n", + "+ Printing Diversion & Stream Summary 1 of 807; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 2 of 807; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 3 of 807; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 4 of 807; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 5 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 6 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 7 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 8 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 9 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 10 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 11 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 12 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 13 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 14 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 15 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 16 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 17 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 18 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 19 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 20 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 21 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 22 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 23 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 24 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 25 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 26 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 27 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 28 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 29 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 30 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 31 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 32 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 33 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 34 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 35 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 36 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 37 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 38 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 39 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 40 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 41 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 42 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 43 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 44 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 45 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 46 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 47 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 48 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 49 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 50 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 51 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 52 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 53 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 54 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 55 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 56 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 57 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 58 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 59 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 60 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 61 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 62 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 63 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 64 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 65 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 66 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 67 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 68 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 69 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 70 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 71 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 72 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 73 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 74 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 75 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 76 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 77 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 78 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 79 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 80 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 81 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 82 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 83 of 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Printing Diversion & Stream Summary 97 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 98 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 99 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 100 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 101 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 102 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 103 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 104 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 105 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 106 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 107 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 108 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 109 of 807; or 14. % Complete\n", + "+ Printing Diversion & Stream 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of 443; or 92. % Complete\n", + "+ Printing Structure Summary (*.xss) 409 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 410 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 411 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 412 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 413 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 414 of 443; or 94. % Complete\n", + "+ Printing Structure Summary (*.xss) 415 of 443; or 94. % Complete\n", + "+ Printing Structure Summary (*.xss) 416 of 443; or 94. % Complete\n", + "+ Printing Structure Summary (*.xss) 417 of 443; or 94. % Complete\n", + "+ Printing Structure Summary (*.xss) 418 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 419 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 420 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 421 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 422 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 423 of 443; or 96. % Complete\n", + "+ Printing Structure Summary (*.xss) 424 of 443; or 96. % Complete\n", + "+ Printing Structure Summary (*.xss) 425 of 443; or 96. % Complete\n", + "+ Printing Structure Summary (*.xss) 426 of 443; or 96. % Complete\n", + "+ Printing Structure Summary (*.xss) 427 of 443; or 97. % Complete\n", + "+ Printing Structure Summary (*.xss) 428 of 443; or 97. % Complete\n", + "+ Printing Structure Summary (*.xss) 429 of 443; or 97. % Complete\n", + "+ Printing Structure Summary (*.xss) 430 of 443; or 97. % Complete\n", + "+ Printing Structure Summary (*.xss) 431 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 432 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 433 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 434 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 435 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 436 of 443; or 99. % Complete\n", + "+ Printing Structure Summary (*.xss) 437 of 443; or 99. % Complete\n", + "+ Printing Structure Summary (*.xss) 438 of 443; or 99. % Complete\n", + "+ Printing Structure Summary (*.xss) 439 of 443; or 99. % Complete\n", + "+ Printing Structure Summary (*.xss) 440 of 443; or 100. % Complete\n", + "+ Printing Structure Summary (*.xss) 441 of 443; or 100. % Complete\n", + "+ Printing Structure Summary (*.xss) 442 of 443; or 100. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine Outifr\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutPln \n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Run output files are:\n", + " \n", + " Diversion output: *.xdd\n", + " Reservoir output: *.xre\n", + " Operating Rule Info: *.xop\n", + " Instream Reach Info: *.xir\n", + " Structure Summary: *.xss\n", + " Call (Control) Summary: *.xca\n", + " Plan Output: *.xpl\n", + " Replacement Reesrvoir Output: *.xrp\n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Termination\n", + " Statem; See detailed messages in file: cm2015B.log \n", + " Stop 0\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Change directories first\n", - "os.chdir(\n", - " data_dir\n", - ") # This is needed specific to the Upper Colorado model as the path name is too long for the model to accept\n", - "subprocess.call([statemod_exe, \"cm2015B\", \"-simulate\"])" + "# Change directories first \n", + "os.chdir(data_dir) #This is needed specific to the Upper Colorado model as the path name is too long for the model to accept\n", + "subprocess.call([statemod_exe, \"cm2015B\", \"-simulate\"])\n" ] }, { @@ -146,16 +2928,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "0758c26d-9690-4bc5-869a-73feed2b9126", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 17.73it/s]\n" + ] + } + ], "source": [ - "# Extract shortages using statemodify convert_xdd() function\n", + "#Extract shortages using statemodify convert_xdd() function \n", "\n", - "# create a directory to store the historic shortages\n", + "# create a directory to store the historic shortages \n", "output_dir = os.path.join(data_dir, \"historic_shortages\")\n", "\n", "# create a directory to store the new files in if it does not exist\n", @@ -171,9 +2961,12 @@ " # path, glob, or a list of paths/globs to the .xdd files you want to convert\n", " xdd_files=os.path.join(data_dir, \"*.xdd\"),\n", " # if the output .parquet files should only contain a subset of structure ids, list them here; None for all\n", - " id_subset=[\"3601008\"],\n", + " id_subset=['3601008'],\n", " # how many .xdd files to convert in parallel; optimally you will want 2-4 CPUs per parallel process\n", " parallel_jobs=2,\n", + " # convert to natural data types\n", + " preserve_string_dtype=False\n", + " \n", ")" ] }, @@ -187,23 +2980,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "70d66198-db40-4874-8cb5-61b1d1346240", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "data = pd.read_parquet(output_dir + \"/cm2015B.parquet\", engine=\"pyarrow\")\n", - "\n", - "\n", - "data[\"shortage_total\"] = data[\"shortage_total\"].astype(float)\n", - "data[\"year\"] = data[\"year\"].astype(int)\n", + "data=pd.read_parquet(output_dir +'/cm2015B.parquet',engine='pyarrow')\n", "\n", "fig, ax = plt.subplots()\n", "\n", - "for name, group in data.groupby(\"structure_id\"):\n", - " ax.scatter(group[\"year\"], group[\"shortage_total\"], label=name)\n", + "for name, group in data.groupby('structure_id'):\n", + " ax.scatter(\n", + " group['year'], group['shortage_total'], label=name)\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", @@ -237,7 +3048,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "fc08da33-396c-443b-b6ff-dadbc7a1fae3", "metadata": { "tags": [] @@ -258,16 +3069,14 @@ "# number of samples to generate\n", "n_samples = 1\n", "\n", - "stm.modify_res(\n", - " output_dir=output_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " target_structure_id_list=[\"3603575\", \"3604512\"],\n", - " seed_value=seed_value,\n", - " n_jobs=n_jobs,\n", - " n_samples=n_samples,\n", - " save_sample=True,\n", - ")" + "stm.modify_res(output_dir=output_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " target_structure_id_list=['3603575','3604512'],\n", + " seed_value=seed_value,\n", + " n_jobs=n_jobs,\n", + " n_samples=n_samples,\n", + " save_sample=True)\n" ] }, { @@ -280,16 +3089,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "01b67e22-69ff-46ce-9f1d-a4d6c01f71d8", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.86911215])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import numpy as np\n", - "\n", - "sample_array = np.load(output_directory + \"/res_1-samples_scenario-1.npy\")\n", + "sample_array = np.load(output_directory+'/res_1-samples_scenario-1.npy')\n", "sample_array" ] }, @@ -311,13 +3130,2832 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "eaaec47d-0a39-4c7b-b8ed-54156d251a1f", "metadata": { "scrolled": true, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running: S0_1\n", + " Parse; Command line argument: \n", + " cm2015B_S0_1 -simulate \n", + "________________________________________________________________________\n", + "\n", + " StateMod \n", + " State of Colorado - Water Supply Planning Model \n", + "\n", + " Version: 15.00.01\n", + " Last revision date: 2015/10/28\n", + "\n", + "________________________________________________________________________\n", + "\n", + " Opening log file cm2015B_S0_1.log \n", + " \n", + " Subroutine Execut\n", + " Subroutine Datinp\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Control File (*.ctl) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Network File (*.rin)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Reservoir Station File (*.res)\n", + " Subroutine GetRes\n", + "\n", + " GetRes; Reservoir Station File (*.res) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Diversion Station File (*.dds)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Station File (*.ris)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Instream Flow Station File (*.ifs) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Well Station File (*.wes) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Plan Station File (*.pln) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Gage File (*.rig) \n", + " Subroutine Riginp\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Instream Flow Right File (*.ifr) 20 1 98\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Reservoir Right File (*.rer) \n", + "\n", + "________________________________________________________________________\n", + " Riginp; Direct Diversion Right File (*.ddr) \n", + "\n", + "________________________________________________________________________\n", + " Oprinp; Operational Right File (*.opr) \n", + " Mdainp.for\n", + "\n", + "________________________________________________________________________\n", + " Mdainp; Instream flow Demand file - Annual (*.ifa) \n", + " Subroutine Execut\n", + "\n", + " Mdainp.for\n", + "+ Execut; Year 1908 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1908 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1908 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1909 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1909 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1909 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1909 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1909 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1909 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1909 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1909 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1909 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1909 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1909 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1909 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1910 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1910 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1910 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1910 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1910 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1910 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1910 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1910 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1910 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1910 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1910 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1910 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1911 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1911 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1911 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1911 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1911 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1911 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1911 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1911 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1911 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1911 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1911 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1911 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1912 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1912 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1912 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1912 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1912 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1912 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1912 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1912 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1912 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1912 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1912 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1912 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; 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Year 1914 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1914 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1914 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1914 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1914 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1914 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1914 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1914 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1914 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1914 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1915 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1915 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1915 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1915 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1915 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1915 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1915 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1915 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1915 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1915 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1915 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1915 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1916 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1916 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1916 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1916 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1916 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1916 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; 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Year 1917 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1917 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1917 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1917 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1918 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1918 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1918 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1918 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1918 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1918 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1918 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1918 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1918 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1918 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1918 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1918 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1919 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1919 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1919 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1919 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1919 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1919 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1919 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1919 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1919 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1919 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1919 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1919 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; 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Year 1923 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1923 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1923 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1923 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1923 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1923 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1924 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1924 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1924 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1924 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1924 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1924 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1924 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1924 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; 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Year 1925 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1925 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1926 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1926 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1926 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1926 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1926 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1926 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1926 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1926 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1926 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1926 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1926 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1926 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1927 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1927 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1927 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1927 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1927 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1927 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1927 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1927 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1927 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1927 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1927 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1927 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1928 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1928 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; 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Year 1929 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1929 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1929 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1929 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1929 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1929 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1929 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1929 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1930 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1930 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1930 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1930 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1930 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1930 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1930 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1930 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1930 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1930 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1930 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1930 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1931 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1931 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1931 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1931 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1931 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1931 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1931 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1931 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1931 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1931 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1931 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1931 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1932 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1932 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1932 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1932 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1932 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1932 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1932 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1932 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1932 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1932 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; 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Year 1934 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1934 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1934 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1934 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1934 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1934 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1934 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1934 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1934 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1934 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1934 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1934 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1935 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1935 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; 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Year 1939 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1939 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1940 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1940 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1940 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1940 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1940 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1940 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1940 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1940 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1940 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1940 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1940 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1940 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1941 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1941 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1941 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1941 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1941 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1941 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1941 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1941 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1941 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1941 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1941 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1941 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1942 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1942 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1942 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1942 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1942 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1942 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1942 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1942 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1942 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1942 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1942 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1942 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1943 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1943 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1943 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1943 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1943 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1943 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1943 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1943 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1943 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1943 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1943 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1943 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1944 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1944 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1944 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1944 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1944 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1944 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1944 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1944 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1944 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1944 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1944 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1944 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1945 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1945 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1945 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1945 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1945 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1945 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1945 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1945 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1945 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1945 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1945 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1945 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1946 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1946 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1946 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1946 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1946 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1946 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1946 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1946 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1946 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1946 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1946 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1946 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1947 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1947 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1947 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1947 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1947 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1947 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1947 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1947 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1947 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1947 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1947 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1947 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1948 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1948 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1948 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1948 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1948 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1948 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1948 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1948 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1948 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1948 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1948 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1948 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1949 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1949 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1949 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1949 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1949 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1949 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1949 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1949 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1949 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1949 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1949 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1949 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1950 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1950 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1950 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1950 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1950 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1950 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1950 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1950 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1950 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1950 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1950 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1950 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1951 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1951 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1951 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1951 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1951 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1951 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1951 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1951 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1951 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1951 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1951 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1951 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1952 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1952 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1952 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1952 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1952 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1952 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1952 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1952 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1952 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1952 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1952 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1952 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1953 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1953 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1953 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1953 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1953 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1953 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1953 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1953 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1953 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1953 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1953 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1953 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1954 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1954 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1954 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1954 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1954 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1954 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1954 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1954 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1954 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1954 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1954 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1954 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1955 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1955 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1955 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1955 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1955 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1955 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1955 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1955 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1955 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1955 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1955 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1955 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1956 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1956 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1956 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1956 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1956 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1956 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1956 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1956 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1956 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1956 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1956 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1956 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1957 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1957 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1957 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1957 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1957 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1957 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1957 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1957 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1957 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1957 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1957 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1957 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1958 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1958 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1958 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1958 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1958 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1958 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1958 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1958 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1958 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1958 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1958 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1958 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1959 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1959 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1959 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1959 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1959 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1959 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1959 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1959 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1959 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1959 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1959 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1959 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1960 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1960 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1960 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1960 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1960 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1960 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1960 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1960 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1960 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1960 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1960 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1960 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1961 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1961 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1961 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1961 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1961 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1961 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1961 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1961 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1961 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1961 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1961 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1961 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1962 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1962 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1962 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1962 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1962 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1962 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1962 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1962 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1962 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1962 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1962 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1962 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1963 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1963 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1963 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1963 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1963 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1963 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1963 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1963 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1963 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1963 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1963 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1963 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1964 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1964 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1964 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1964 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1964 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1964 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1964 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1964 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1964 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1964 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1964 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1964 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1965 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1965 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1965 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1965 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1965 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1965 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1965 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1965 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1965 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1965 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1965 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1965 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1966 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1966 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1966 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1966 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1966 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1966 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1966 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1966 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1966 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1966 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1966 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1966 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1967 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1967 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1967 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1967 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1967 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1967 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1967 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1967 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1967 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1967 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1967 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1967 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1968 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1968 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1968 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1968 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1968 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1968 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1968 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1968 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1968 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1968 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1968 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1968 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1969 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1969 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1969 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1969 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1969 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1969 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1969 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1969 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1969 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1969 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1969 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1969 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1970 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1970 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1970 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1970 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1970 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1970 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1970 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1970 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1970 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1970 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1970 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1970 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1971 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1971 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1971 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1971 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1971 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1971 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1971 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1971 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1971 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1971 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1971 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1971 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1972 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1972 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1972 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1972 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1972 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1972 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1972 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1972 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1972 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1972 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1972 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1972 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1973 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1973 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1973 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1973 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1973 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1973 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1973 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1973 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1973 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1973 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1973 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1973 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1974 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1974 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1974 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1974 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1974 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1974 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1974 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1974 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1974 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1974 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1974 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1974 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1975 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1975 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1975 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1975 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1975 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1975 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1975 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1975 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1975 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1975 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1975 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1975 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1976 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1976 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1976 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1976 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1976 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1976 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1976 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1976 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1976 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1976 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1976 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1976 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1977 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1977 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1977 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1977 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1977 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1977 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1977 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1977 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1977 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1977 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1977 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1977 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1978 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1978 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1978 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1978 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1978 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1978 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1978 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1978 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1978 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1978 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1978 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1978 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1979 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1979 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1979 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1979 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1979 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1979 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1979 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1979 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1979 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1979 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1979 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1979 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1980 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1980 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1980 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1980 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1980 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1980 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1980 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1980 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1980 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1980 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1980 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1980 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1981 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1981 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1981 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1981 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1981 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1981 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1981 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1981 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1981 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1981 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1981 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1981 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1982 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1982 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1982 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1982 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1982 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1982 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1982 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1982 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1982 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1982 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1982 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1982 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1983 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1983 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1983 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1983 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1983 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1983 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1983 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1983 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1983 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1983 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1983 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1983 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1984 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1984 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1984 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1984 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1984 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1984 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1984 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1984 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1984 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1984 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1984 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1984 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1985 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1985 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1985 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1985 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1985 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1985 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1985 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1985 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1985 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1985 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1985 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1985 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1986 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1986 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1986 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1986 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1986 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1986 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1986 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1986 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1986 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1986 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1986 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1986 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1987 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1987 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1987 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1987 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1987 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1987 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1987 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1987 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1987 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1987 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1987 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1987 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1988 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1988 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1988 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1988 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1988 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1988 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1988 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1988 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1988 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1988 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1988 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1988 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1989 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1989 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1989 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1989 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1989 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1989 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1989 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1989 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1989 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1989 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1989 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1989 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1990 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1990 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1990 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1990 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1990 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1990 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1990 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1990 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1990 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1990 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1990 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1990 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1991 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1991 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1991 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1991 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1991 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1991 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1991 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1991 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1991 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1991 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1991 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1991 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1992 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1992 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1992 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1992 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1992 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1992 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1992 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1992 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1992 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1992 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1992 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1992 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1993 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1993 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1993 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1993 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1993 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1993 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; 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Year 1994 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1994 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1994 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1994 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1995 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1995 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1995 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1995 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1995 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1995 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1995 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1995 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1995 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1995 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1995 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1995 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1996 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1996 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1996 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1996 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1996 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1996 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1996 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1996 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1996 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1996 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1996 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1996 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1997 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1997 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1997 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1997 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1997 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1997 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1997 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1997 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1997 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1997 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1997 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1997 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1998 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1998 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1998 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1998 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1998 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1998 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1998 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1998 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1998 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1998 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1998 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1998 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1999 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1999 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1999 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1999 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1999 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1999 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1999 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1999 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1999 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1999 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1999 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1999 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2000 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2000 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2000 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2000 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2000 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2000 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2000 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2000 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2000 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2000 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2000 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2000 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2001 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2001 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2001 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2001 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2001 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2001 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2001 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2001 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2001 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2001 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2001 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2001 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2002 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2002 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2002 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2002 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2002 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2002 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2002 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2002 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2002 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2002 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2002 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2002 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2003 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2003 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2003 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2003 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2003 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2003 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2003 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2003 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2003 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2003 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2003 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2003 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2004 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2004 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2004 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2004 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2004 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2004 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2004 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2004 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2004 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2004 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2004 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2004 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2005 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2005 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2005 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2005 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2005 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2005 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2005 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2005 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2005 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2005 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2005 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2005 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2006 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2006 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2006 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2006 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2006 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2006 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2006 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2006 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2006 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2006 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2006 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2006 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2007 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2007 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2007 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2007 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2007 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2007 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2007 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2007 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2007 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2007 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2007 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2007 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2008 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2008 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2008 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2008 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2008 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2008 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2008 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2008 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2008 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2008 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2008 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2008 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2009 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2009 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2009 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2009 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2009 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2009 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2009 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2009 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2009 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2009 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2009 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2009 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2010 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2010 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2010 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2010 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2010 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2010 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2010 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2010 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2010 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2010 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2010 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2010 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2011 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2011 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2011 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2011 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2011 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2011 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2011 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2011 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2011 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2011 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2011 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2011 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2012 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2012 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2012 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2012 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2012 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2012 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2012 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2012 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2012 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2012 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2012 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2012 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2013 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2013 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2013 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2013 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2013 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2013 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2013 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2013 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2013 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + "\n", + "________________________________________________________________________\n", + " Execut; On Year 2013 Month SEP Day 1\n", + " The maximum number of reoperations 1\n", + "\n", + "________________________________________________________________________\n", + " Execut; Writing reports\n", + "\n", + "________________________________________________________________________\n", + " Execut; \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutRes \n", + "+ Printing Reservoir Summary 1 of 36; or 3. % Complete\n", + "+ Printing Reservoir Summary 2 of 36; or 6. % Complete\n", + "+ Printing Reservoir Summary 3 of 36; or 8. % Complete\n", + "+ Printing Reservoir Summary 4 of 36; or 11. % Complete\n", + "+ Printing Reservoir Summary 5 of 36; or 14. % Complete\n", + "+ Printing Reservoir Summary 8 of 36; or 22. % Complete\n", + "+ Printing Reservoir Summary 9 of 36; or 25. % Complete\n", + "+ Printing Reservoir Summary 10 of 36; or 28. % Complete\n", + "+ Printing Reservoir Summary 11 of 36; or 31. % Complete\n", + "+ Printing Reservoir Summary 12 of 36; or 33. % Complete\n", + "+ Printing Reservoir Summary 13 of 36; or 36. % Complete\n", + "+ Printing Reservoir Summary 14 of 36; or 39. % Complete\n", + "+ Printing Reservoir Summary 15 of 36; or 42. % Complete\n", + "+ Printing Reservoir Summary 16 of 36; or 44. % Complete\n", + "+ Printing Reservoir Summary 17 of 36; or 47. % Complete\n", + "+ Printing Reservoir Summary 18 of 36; or 50. % Complete\n", + "+ Printing Reservoir Summary 19 of 36; or 53. % Complete\n", + "+ Printing Reservoir Summary 20 of 36; or 56. % Complete\n", + "+ Printing Reservoir Summary 21 of 36; or 58. % Complete\n", + "+ Printing Reservoir Summary 22 of 36; or 61. % Complete\n", + "+ Printing Reservoir Summary 23 of 36; or 64. % Complete\n", + "+ Printing Reservoir Summary 24 of 36; or 67. % Complete\n", + "+ Printing Reservoir Summary 25 of 36; or 69. % Complete\n", + "+ Printing Reservoir Summary 26 of 36; or 72. % Complete\n", + "+ Printing Reservoir Summary 27 of 36; or 75. % Complete\n", + "+ Printing Reservoir Summary 28 of 36; or 78. % Complete\n", + "+ Printing Reservoir Summary 29 of 36; or 81. % Complete\n", + "+ Printing Reservoir Summary 30 of 36; or 83. % Complete\n", + "+ Printing Reservoir Summary 31 of 36; or 86. % Complete\n", + "+ Printing Reservoir Summary 32 of 36; or 89. % Complete\n", + "+ Printing Reservoir Summary 33 of 36; or 92. % Complete\n", + "+ Printing Reservoir Summary 34 of 36; or 94. % Complete\n", + "+ Printing Reservoir Summary 35 of 36; or 97. % Complete\n", + "+ Printing Reservoir Summary 36 of 36; or 100. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutDivW \n", + "+ Printing Diversion & Stream Summary 1 of 807; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 2 of 807; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 3 of 807; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 4 of 807; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 5 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 6 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 7 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 8 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 9 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 10 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 11 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 12 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 13 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 14 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 15 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 16 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 17 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 18 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 19 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 20 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 21 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 22 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 23 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 24 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 25 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 26 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 27 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 28 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 29 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 30 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 31 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 32 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 33 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 34 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 35 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 36 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 37 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 38 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 39 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 40 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 41 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 42 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 43 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 44 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 45 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 46 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 47 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 48 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 49 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 50 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 51 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 52 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 53 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 54 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 55 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 56 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 57 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 58 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 59 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 60 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 61 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 62 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 63 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 64 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 65 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 66 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 67 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 68 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 69 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 70 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 71 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 72 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 73 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 74 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 75 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 76 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 77 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 78 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 79 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 80 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 81 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 82 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 83 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 84 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 85 of 807; or 11. % Complete\n", + "+ Printing Diversion & Stream Summary 86 of 807; or 11. % Complete\n", + "+ Printing Diversion & Stream Summary 87 of 807; or 11. % Complete\n", + "+ Printing Diversion & Stream Summary 88 of 807; or 11. % Complete\n", + "+ Printing Diversion & Stream Summary 89 of 807; or 11. % Complete\n", + "+ Printing Diversion & Stream Summary 90 of 807; or 11. % Complete\n", + "+ Printing Diversion & Stream Summary 91 of 807; or 11. % Complete\n", + "+ Printing Diversion & Stream Summary 92 of 807; or 11. % Complete\n", + "+ Printing Diversion & Stream Summary 93 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 94 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 95 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 96 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 97 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 98 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 99 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 100 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 101 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 102 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 103 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 104 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 105 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 106 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 107 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 108 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 109 of 807; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 110 of 807; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 111 of 807; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 112 of 807; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 113 of 807; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 114 of 807; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 115 of 807; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 116 of 807; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 117 of 807; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 118 of 807; or 15. % Complete\n", + "+ Printing Diversion & Stream Summary 119 of 807; or 15. % Complete\n", + "+ Printing Diversion & Stream Summary 120 of 807; or 15. % Complete\n", + "+ Printing Diversion & Stream Summary 121 of 807; or 15. % Complete\n", + "+ Printing Diversion & Stream Summary 122 of 807; or 15. % Complete\n", + "+ Printing Diversion & Stream Summary 123 of 807; or 15. % Complete\n", + "+ Printing Diversion & Stream Summary 124 of 807; or 15. % Complete\n", + "+ Printing Diversion & Stream Summary 125 of 807; or 15. % Complete\n", + "+ Printing Diversion & Stream Summary 126 of 807; or 16. % Complete\n", + "+ Printing Diversion & Stream Summary 127 of 807; or 16. % Complete\n", + "+ Printing Diversion & Stream Summary 128 of 807; or 16. % Complete\n", + "+ Printing Diversion & Stream Summary 129 of 807; or 16. % Complete\n", + "+ Printing Diversion & Stream Summary 130 of 807; or 16. % Complete\n", + "+ Printing Diversion & Stream Summary 131 of 807; or 16. % Complete\n", + "+ Printing Diversion & Stream Summary 132 of 807; or 16. % Complete\n", + "+ Printing Diversion & Stream Summary 133 of 807; or 16. % Complete\n", + "+ Printing Diversion & Stream Summary 134 of 807; or 17. % Complete\n", + "+ Printing Diversion & Stream Summary 135 of 807; or 17. % Complete\n", + "+ Printing Diversion & Stream Summary 136 of 807; or 17. % Complete\n", + "+ Printing Diversion & Stream Summary 137 of 807; or 17. % Complete\n", + "+ Printing Diversion & Stream Summary 138 of 807; or 17. % Complete\n", + "+ Printing Diversion & Stream Summary 139 of 807; or 17. % Complete\n", + "+ Printing Diversion & Stream Summary 140 of 807; or 17. % Complete\n", + "+ Printing Diversion & Stream Summary 141 of 807; or 17. % Complete\n", + "+ Printing Diversion & Stream Summary 142 of 807; or 18. % Complete\n", + "+ Printing Diversion & Stream Summary 143 of 807; or 18. % Complete\n", + "+ Printing Diversion & Stream Summary 144 of 807; or 18. % Complete\n", + "+ Printing Diversion & Stream Summary 145 of 807; or 18. % Complete\n", + "+ Printing Diversion & Stream Summary 146 of 807; or 18. % Complete\n", + "+ Printing Diversion & Stream Summary 147 of 807; or 18. % Complete\n", + "+ Printing Diversion & Stream Summary 148 of 807; or 18. % Complete\n", + "+ Printing Diversion & Stream Summary 149 of 807; or 18. % Complete\n", + "+ Printing Diversion & Stream Summary 150 of 807; or 19. % Complete\n", + "+ Printing Diversion & Stream Summary 151 of 807; or 19. % Complete\n", + "+ Printing Diversion & Stream Summary 152 of 807; or 19. % Complete\n", + "+ Printing Diversion & Stream Summary 153 of 807; or 19. % Complete\n", + "+ Printing Diversion & Stream Summary 154 of 807; or 19. % Complete\n", + "+ Printing Diversion & Stream Summary 155 of 807; or 19. % Complete\n", + "+ Printing Diversion & Stream Summary 156 of 807; or 19. % Complete\n", + "+ Printing Diversion & Stream Summary 157 of 807; or 19. % Complete\n", + "+ Printing Diversion & Stream Summary 158 of 807; or 20. % Complete\n", + "+ Printing Diversion & Stream Summary 159 of 807; or 20. % Complete\n", + "+ Printing Diversion & Stream Summary 160 of 807; or 20. % Complete\n", + "+ Printing Diversion & Stream Summary 161 of 807; or 20. % Complete\n", + "+ Printing Diversion & Stream Summary 162 of 807; or 20. % Complete\n", + "+ Printing Diversion & Stream Summary 163 of 807; or 20. % Complete\n", + "+ Printing Diversion & Stream Summary 164 of 807; or 20. % Complete\n", + "+ Printing Diversion & Stream Summary 165 of 807; or 20. % Complete\n", + "+ Printing Diversion & Stream Summary 166 of 807; or 21. % Complete\n", + "+ Printing Diversion & Stream Summary 167 of 807; or 21. % Complete\n", + "+ Printing Diversion & Stream Summary 168 of 807; or 21. % Complete\n", + "+ Printing Diversion & Stream Summary 169 of 807; or 21. % Complete\n", + "+ Printing Diversion & Stream Summary 170 of 807; or 21. % Complete\n", + "+ Printing Diversion & Stream Summary 171 of 807; or 21. % Complete\n", + "+ Printing Diversion & Stream Summary 172 of 807; or 21. % Complete\n", + "+ Printing Diversion & Stream Summary 173 of 807; or 21. % Complete\n", + "+ Printing Diversion & Stream Summary 174 of 807; or 22. % Complete\n", + "+ Printing Diversion & Stream Summary 175 of 807; or 22. % Complete\n", + "+ Printing Diversion & Stream Summary 176 of 807; or 22. % Complete\n", + "+ Printing Diversion & Stream Summary 177 of 807; or 22. % Complete\n", + "+ Printing Diversion & Stream Summary 178 of 807; or 22. % Complete\n", + "+ Printing Diversion & Stream Summary 179 of 807; or 22. % Complete\n", + "+ Printing Diversion & Stream Summary 180 of 807; or 22. % Complete\n", + "+ Printing Diversion & Stream Summary 181 of 807; or 22. % Complete\n", + "+ Printing Diversion & Stream Summary 182 of 807; or 23. % Complete\n", + "+ Printing Diversion & Stream Summary 183 of 807; or 23. % Complete\n", + "+ Printing Diversion & Stream Summary 184 of 807; or 23. % Complete\n", + "+ Printing Diversion & Stream Summary 185 of 807; or 23. % Complete\n", + "+ Printing Diversion & Stream Summary 186 of 807; or 23. % Complete\n", + "+ Printing Diversion & Stream Summary 187 of 807; or 23. % Complete\n", + "+ Printing Diversion & Stream Summary 188 of 807; or 23. % Complete\n", + "+ Printing Diversion & Stream Summary 189 of 807; or 23. % Complete\n", + "+ Printing Diversion & Stream Summary 190 of 807; or 24. % Complete\n", + "+ Printing Diversion & Stream Summary 191 of 807; or 24. % Complete\n", + "+ Printing Diversion & Stream Summary 192 of 807; or 24. % Complete\n", + "+ Printing Diversion & Stream Summary 193 of 807; or 24. % Complete\n", + "+ Printing Diversion & Stream Summary 194 of 807; or 24. % Complete\n", + "+ Printing Diversion & Stream Summary 195 of 807; or 24. % Complete\n", + "+ Printing Diversion & Stream Summary 196 of 807; or 24. % Complete\n", + "+ Printing Diversion & Stream Summary 197 of 807; or 24. % Complete\n", + "+ Printing Diversion & Stream Summary 198 of 807; or 25. % Complete\n", + "+ Printing Diversion & Stream Summary 199 of 807; or 25. % Complete\n", + "+ Printing Diversion & Stream Summary 200 of 807; or 25. % Complete\n", + "+ Printing Diversion & Stream Summary 201 of 807; or 25. % Complete\n", + "+ Printing Diversion & Stream Summary 202 of 807; or 25. % Complete\n", + "+ Printing Diversion & Stream Summary 203 of 807; or 25. % Complete\n", + "+ Printing Diversion & Stream Summary 204 of 807; or 25. % Complete\n", + "+ Printing Diversion & Stream Summary 205 of 807; or 25. % Complete\n", + "+ Printing Diversion & Stream Summary 206 of 807; or 26. % Complete\n", + "+ Printing Diversion & Stream Summary 207 of 807; or 26. % Complete\n", + "+ Printing Diversion & Stream Summary 208 of 807; or 26. % Complete\n", + "+ Printing Diversion & Stream Summary 209 of 807; or 26. % Complete\n", + "+ Printing Diversion & Stream Summary 210 of 807; or 26. % Complete\n", + "+ Printing Diversion & Stream Summary 211 of 807; or 26. % Complete\n", + "+ Printing Diversion & Stream Summary 212 of 807; or 26. % Complete\n", + "+ Printing Diversion & Stream Summary 213 of 807; or 26. % Complete\n", + "+ Printing Diversion & Stream Summary 214 of 807; or 27. % Complete\n", + "+ Printing Diversion & Stream Summary 215 of 807; or 27. % Complete\n", + "+ Printing Diversion & Stream Summary 216 of 807; or 27. % Complete\n", + "+ Printing Diversion & Stream Summary 217 of 807; or 27. % Complete\n", + "+ Printing Diversion & Stream Summary 218 of 807; or 27. % Complete\n", + "+ Printing Diversion & Stream Summary 219 of 807; or 27. % Complete\n", + "+ Printing Diversion & Stream Summary 220 of 807; or 27. % Complete\n", + "+ Printing Diversion & Stream Summary 221 of 807; or 27. % Complete\n", + "+ Printing Diversion & Stream Summary 222 of 807; or 28. % Complete\n", + "+ Printing Diversion & Stream Summary 223 of 807; or 28. % Complete\n", + "+ Printing Diversion & Stream Summary 224 of 807; or 28. % Complete\n", + "+ Printing Diversion & Stream Summary 225 of 807; or 28. % Complete\n", + "+ Printing Diversion & Stream Summary 226 of 807; or 28. % Complete\n", + "+ Printing Diversion & Stream Summary 227 of 807; or 28. % Complete\n", + "+ Printing Diversion & Stream Summary 228 of 807; or 28. % Complete\n", + "+ Printing Diversion & Stream Summary 229 of 807; or 28. % Complete\n", + "+ Printing Diversion & Stream Summary 230 of 807; or 29. % Complete\n", + "+ Printing Diversion & Stream Summary 231 of 807; or 29. % Complete\n", + "+ Printing Diversion & Stream Summary 232 of 807; or 29. % Complete\n", + "+ Printing Diversion & Stream Summary 233 of 807; or 29. % Complete\n", + "+ Printing Diversion & Stream Summary 234 of 807; or 29. % Complete\n", + "+ Printing Diversion & Stream Summary 235 of 807; or 29. % Complete\n", + "+ Printing Diversion & Stream Summary 236 of 807; or 29. % Complete\n", + "+ Printing Diversion & Stream Summary 237 of 807; or 29. % Complete\n", + "+ Printing Diversion & Stream Summary 238 of 807; or 29. % Complete\n", + "+ Printing Diversion & Stream Summary 239 of 807; or 30. % Complete\n", + "+ Printing Diversion & Stream Summary 240 of 807; or 30. % Complete\n", + "+ Printing Diversion & Stream Summary 241 of 807; or 30. % Complete\n", + "+ Printing Diversion & Stream Summary 242 of 807; or 30. % Complete\n", + "+ Printing Diversion & Stream Summary 243 of 807; or 30. % Complete\n", + "+ Printing Diversion & Stream Summary 244 of 807; or 30. % Complete\n", + "+ Printing Diversion & Stream Summary 245 of 807; or 30. % Complete\n", + "+ Printing Diversion & Stream Summary 246 of 807; or 30. % Complete\n", + "+ Printing Diversion & Stream Summary 247 of 807; or 31. % Complete\n", + "+ Printing Diversion & Stream Summary 248 of 807; or 31. % Complete\n", + "+ Printing Diversion & Stream Summary 249 of 807; or 31. % Complete\n", + "+ Printing Diversion & Stream Summary 250 of 807; or 31. % Complete\n", + "+ Printing Diversion & Stream Summary 251 of 807; or 31. % Complete\n", + "+ Printing Diversion & Stream Summary 252 of 807; or 31. % Complete\n", + "+ Printing Diversion & Stream Summary 253 of 807; or 31. % Complete\n", + "+ Printing Diversion & Stream Summary 254 of 807; or 31. % Complete\n", + "+ Printing Diversion & Stream Summary 255 of 807; or 32. % Complete\n", + "+ Printing Diversion & Stream Summary 256 of 807; or 32. % Complete\n", + "+ Printing Diversion & Stream Summary 257 of 807; or 32. % Complete\n", + "+ Printing Diversion & Stream Summary 258 of 807; or 32. % Complete\n", + "+ Printing Diversion & Stream Summary 259 of 807; or 32. % Complete\n", + "+ Printing Diversion & Stream Summary 260 of 807; or 32. % Complete\n", + "+ Printing Diversion & Stream Summary 261 of 807; or 32. % Complete\n", + "+ Printing Diversion & Stream Summary 262 of 807; or 32. % Complete\n", + "+ Printing Diversion & Stream Summary 263 of 807; or 33. % Complete\n", + "+ Printing Diversion & Stream Summary 264 of 807; or 33. % Complete\n", + "+ Printing Diversion & Stream Summary 265 of 807; or 33. % Complete\n", + "+ Printing Diversion & Stream Summary 266 of 807; or 33. % Complete\n", + "+ Printing Diversion & Stream Summary 267 of 807; or 33. % Complete\n", + "+ Printing Diversion & Stream Summary 268 of 807; or 33. % Complete\n", + "+ Printing Diversion & Stream Summary 269 of 807; or 33. % Complete\n", + "+ Printing Diversion & Stream Summary 270 of 807; or 33. % Complete\n", + "+ Printing Diversion & Stream Summary 271 of 807; or 34. % Complete\n", + "+ Printing Diversion & Stream Summary 272 of 807; or 34. % Complete\n", + "+ Printing Diversion & Stream Summary 273 of 807; or 34. % Complete\n", + "+ Printing Diversion & Stream Summary 274 of 807; or 34. % Complete\n", + "+ Printing Diversion & Stream Summary 275 of 807; or 34. % Complete\n", + "+ Printing Diversion & Stream Summary 276 of 807; or 34. % Complete\n", + "+ Printing Diversion & Stream Summary 277 of 807; or 34. % Complete\n", + "+ Printing Diversion & Stream Summary 278 of 807; or 34. % Complete\n", + "+ Printing Diversion & Stream Summary 279 of 807; or 35. % Complete\n", + "+ Printing Diversion & Stream Summary 280 of 807; or 35. % Complete\n", + "+ Printing Diversion & Stream Summary 281 of 807; or 35. % Complete\n", + "+ Printing Diversion & Stream Summary 282 of 807; or 35. % Complete\n", + "+ Printing Diversion & Stream Summary 283 of 807; or 35. % Complete\n", + "+ Printing Diversion & Stream Summary 284 of 807; or 35. % Complete\n", + "+ Printing Diversion & Stream Summary 285 of 807; or 35. % Complete\n", + "+ Printing Diversion & Stream Summary 286 of 807; or 35. % Complete\n", + "+ Printing Diversion & Stream Summary 287 of 807; or 36. % Complete\n", + "+ Printing Diversion & Stream Summary 288 of 807; or 36. % Complete\n", + "+ Printing Diversion & Stream Summary 289 of 807; or 36. % Complete\n", + "+ Printing Diversion & Stream Summary 290 of 807; or 36. % Complete\n", + "+ Printing Diversion & Stream Summary 291 of 807; or 36. % Complete\n", + "+ Printing Diversion & Stream Summary 292 of 807; or 36. % Complete\n", + "+ Printing Diversion & Stream Summary 293 of 807; or 36. % Complete\n", + "+ Printing Diversion & Stream Summary 294 of 807; or 36. % Complete\n", + "+ Printing Diversion & Stream Summary 295 of 807; or 37. % Complete\n", + "+ Printing Diversion & Stream Summary 296 of 807; or 37. % Complete\n", + "+ Printing Diversion & Stream Summary 297 of 807; or 37. % Complete\n", + "+ Printing Diversion & Stream Summary 298 of 807; or 37. % Complete\n", + "+ Printing Diversion & Stream Summary 299 of 807; or 37. % Complete\n", + "+ Printing Diversion & Stream Summary 300 of 807; or 37. % Complete\n", + "+ Printing Diversion & Stream Summary 301 of 807; or 37. % Complete\n", + "+ Printing Diversion & Stream Summary 302 of 807; or 37. % Complete\n", + "+ Printing Diversion & Stream Summary 303 of 807; or 38. % Complete\n", + "+ Printing Diversion & Stream Summary 304 of 807; or 38. % Complete\n", + "+ Printing Diversion & Stream Summary 305 of 807; or 38. % Complete\n", + "+ Printing Diversion & Stream Summary 306 of 807; or 38. % Complete\n", + "+ Printing Diversion & Stream Summary 307 of 807; or 38. % Complete\n", + "+ Printing Diversion & Stream Summary 308 of 807; or 38. % Complete\n", + "+ Printing Diversion & Stream Summary 309 of 807; or 38. % Complete\n", + "+ Printing Diversion & Stream Summary 310 of 807; or 38. % Complete\n", + "+ Printing Diversion & Stream Summary 311 of 807; or 39. % Complete\n", + "+ Printing Diversion & Stream Summary 312 of 807; or 39. % Complete\n", + "+ Printing Diversion & Stream Summary 313 of 807; or 39. % Complete\n", + "+ Printing Diversion & Stream Summary 314 of 807; or 39. % Complete\n", + "+ Printing Diversion & Stream Summary 315 of 807; or 39. % Complete\n", + "+ Printing Diversion & Stream Summary 316 of 807; or 39. % Complete\n", + "+ Printing Diversion & Stream Summary 317 of 807; or 39. % Complete\n", + "+ Printing Diversion & Stream Summary 318 of 807; or 39. % Complete\n", + "+ Printing Diversion & Stream Summary 319 of 807; or 40. % Complete\n", + "+ Printing Diversion & Stream Summary 320 of 807; or 40. % Complete\n", + "+ Printing Diversion & Stream Summary 321 of 807; or 40. % Complete\n", + "+ Printing Diversion & Stream Summary 322 of 807; or 40. % Complete\n", + "+ Printing Diversion & Stream Summary 323 of 807; or 40. % Complete\n", + "+ Printing Diversion & Stream Summary 324 of 807; or 40. % Complete\n", + "+ Printing Diversion & Stream Summary 325 of 807; or 40. % Complete\n", + "+ Printing Diversion & Stream Summary 326 of 807; or 40. % Complete\n", + "+ Printing Diversion & Stream Summary 327 of 807; or 41. % Complete\n", + "+ Printing Diversion & Stream Summary 328 of 807; or 41. % Complete\n", + "+ Printing Diversion & Stream Summary 329 of 807; or 41. % Complete\n", + "+ Printing Diversion & Stream Summary 330 of 807; or 41. % Complete\n", + "+ Printing Diversion & Stream Summary 331 of 807; or 41. % Complete\n", + "+ Printing Diversion & Stream Summary 332 of 807; or 41. % Complete\n", + "+ Printing Diversion & Stream Summary 333 of 807; or 41. % Complete\n", + "+ Printing Diversion & Stream Summary 334 of 807; or 41. % Complete\n", + "+ Printing Diversion & Stream Summary 335 of 807; or 42. % Complete\n", + "+ Printing Diversion & Stream Summary 336 of 807; or 42. % Complete\n", + "+ Printing Diversion & Stream Summary 337 of 807; or 42. % Complete\n", + "+ Printing Diversion & Stream Summary 338 of 807; or 42. % Complete\n", + "+ Printing Diversion & Stream Summary 339 of 807; or 42. % Complete\n", + "+ Printing Diversion & Stream Summary 340 of 807; or 42. % Complete\n", + "+ Printing Diversion & Stream Summary 341 of 807; or 42. % Complete\n", + "+ Printing Diversion & Stream Summary 342 of 807; or 42. % Complete\n", + "+ Printing Diversion & Stream Summary 343 of 807; or 43. % Complete\n", + "+ Printing Diversion & Stream Summary 344 of 807; or 43. % Complete\n", + "+ Printing Diversion & Stream Summary 345 of 807; or 43. % Complete\n", + "+ Printing Diversion & Stream Summary 346 of 807; or 43. % Complete\n", + "+ Printing Diversion & Stream Summary 347 of 807; or 43. % Complete\n", + "+ Printing Diversion & Stream Summary 348 of 807; or 43. % Complete\n", + "+ Printing Diversion & Stream Summary 349 of 807; or 43. % Complete\n", + "+ Printing Diversion & Stream Summary 350 of 807; or 43. % Complete\n", + "+ Printing Diversion & Stream Summary 351 of 807; or 43. % Complete\n", + "+ Printing Diversion & Stream Summary 352 of 807; or 44. % Complete\n", + "+ Printing Diversion & Stream Summary 353 of 807; or 44. % Complete\n", + "+ Printing Diversion & Stream Summary 354 of 807; or 44. % Complete\n", + "+ Printing Diversion & Stream Summary 355 of 807; or 44. % Complete\n", + "+ Printing Diversion & Stream Summary 356 of 807; or 44. % Complete\n", + "+ Printing Diversion & Stream Summary 357 of 807; or 44. % Complete\n", + "+ Printing Diversion & Stream Summary 358 of 807; or 44. % Complete\n", + "+ Printing Diversion & Stream Summary 359 of 807; or 44. % Complete\n", + "+ Printing Diversion & Stream Summary 360 of 807; or 45. % Complete\n", + "+ Printing Diversion & Stream Summary 361 of 807; or 45. % Complete\n", + "+ Printing Diversion & Stream Summary 362 of 807; or 45. % Complete\n", + "+ Printing Diversion & Stream Summary 363 of 807; or 45. % Complete\n", + "+ Printing Diversion & Stream Summary 364 of 807; or 45. % Complete\n", + "+ Printing Diversion & Stream Summary 365 of 807; 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or 47. % Complete\n", + "+ Printing Diversion & Stream Summary 380 of 807; or 47. % Complete\n", + "+ Printing Diversion & Stream Summary 381 of 807; or 47. % Complete\n", + "+ Printing Diversion & Stream Summary 382 of 807; or 47. % Complete\n", + "+ Printing Diversion & Stream Summary 383 of 807; or 47. % Complete\n", + "+ Printing Diversion & Stream Summary 384 of 807; or 48. % Complete\n", + "+ Printing Diversion & Stream Summary 385 of 807; or 48. % Complete\n", + "+ Printing Diversion & Stream Summary 386 of 807; or 48. % Complete\n", + "+ Printing Diversion & Stream Summary 387 of 807; or 48. % Complete\n", + "+ Printing Diversion & Stream Summary 388 of 807; or 48. % Complete\n", + "+ Printing Diversion & Stream Summary 389 of 807; or 48. % Complete\n", + "+ Printing Diversion & Stream Summary 390 of 807; or 48. % Complete\n", + "+ Printing Diversion & Stream Summary 391 of 807; or 48. % Complete\n", + "+ Printing Diversion & Stream Summary 392 of 807; or 49. % Complete\n", + "+ Printing Diversion & Stream Summary 393 of 807; or 49. % Complete\n", + "+ Printing Diversion & Stream Summary 394 of 807; or 49. % Complete\n", + "+ Printing Diversion & Stream Summary 395 of 807; or 49. % Complete\n", + "+ Printing Diversion & Stream Summary 396 of 807; or 49. % Complete\n", + "+ Printing Diversion & Stream Summary 397 of 807; or 49. % Complete\n", + "+ Printing Diversion & Stream Summary 398 of 807; or 49. % Complete\n", + "+ Printing Diversion & Stream Summary 399 of 807; or 49. % Complete\n", + "+ Printing Diversion & Stream Summary 400 of 807; or 50. % Complete\n", + "+ Printing Diversion & Stream Summary 401 of 807; or 50. % Complete\n", + "+ Printing Diversion & Stream Summary 402 of 807; or 50. % Complete\n", + "+ Printing Diversion & Stream Summary 403 of 807; or 50. % Complete\n", + "+ Printing Diversion & Stream Summary 404 of 807; or 50. % Complete\n", + "+ Printing Diversion & Stream Summary 405 of 807; or 50. % Complete\n", + "+ Printing Diversion & Stream Summary 406 of 807; or 50. % Complete\n", + "+ Printing Diversion & Stream Summary 407 of 807; or 50. % Complete\n", + "+ Printing Diversion & Stream Summary 408 of 807; or 51. % Complete\n", + "+ Printing Diversion & Stream Summary 409 of 807; or 51. % Complete\n", + "+ Printing Diversion & Stream Summary 410 of 807; or 51. % Complete\n", + "+ Printing Diversion & Stream Summary 411 of 807; or 51. % Complete\n", + "+ Printing Diversion & Stream Summary 412 of 807; or 51. % Complete\n", + "+ Printing Diversion & Stream Summary 413 of 807; or 51. % Complete\n", + "+ Printing Diversion & Stream Summary 414 of 807; or 51. % Complete\n", + "+ Printing Diversion & Stream Summary 415 of 807; or 51. % Complete\n", + "+ Printing Diversion & Stream Summary 416 of 807; or 52. % Complete\n", + "+ Printing Diversion & Stream Summary 417 of 807; or 52. % Complete\n", + "+ Printing Diversion & Stream Summary 418 of 807; or 52. % Complete\n", + "+ Printing Diversion & Stream Summary 419 of 807; or 52. % Complete\n", + "+ Printing Diversion & Stream Summary 420 of 807; or 52. % Complete\n", + "+ Printing Diversion & Stream Summary 421 of 807; or 52. % Complete\n", + "+ Printing Diversion & Stream Summary 422 of 807; or 52. % Complete\n", + "+ Printing Diversion & Stream Summary 423 of 807; or 52. % Complete\n", + "+ Printing Diversion & Stream Summary 424 of 807; or 53. % Complete\n", + "+ Printing Diversion & Stream Summary 425 of 807; or 53. % Complete\n", + "+ Printing Diversion & Stream Summary 426 of 807; or 53. % Complete\n", + "+ Printing Diversion & Stream Summary 427 of 807; or 53. % Complete\n", + "+ Printing Diversion & Stream Summary 428 of 807; or 53. % Complete\n", + "+ Printing Diversion & Stream Summary 429 of 807; or 53. % Complete\n", + "+ Printing Diversion & Stream Summary 430 of 807; or 53. % Complete\n", + "+ Printing Diversion & Stream Summary 431 of 807; or 53. % Complete\n", + "+ Printing Diversion & Stream Summary 432 of 807; or 54. % Complete\n", + "+ Printing Diversion & Stream Summary 433 of 807; or 54. % Complete\n", + "+ Printing Diversion & Stream Summary 434 of 807; or 54. % Complete\n", + "+ Printing Diversion & Stream Summary 435 of 807; or 54. % Complete\n", + "+ Printing Diversion & Stream Summary 436 of 807; or 54. % Complete\n", + "+ Printing Diversion & Stream Summary 437 of 807; or 54. % Complete\n", + "+ Printing Diversion & Stream Summary 438 of 807; or 54. % Complete\n", + "+ Printing Diversion & Stream Summary 439 of 807; or 54. % Complete\n", + "+ Printing Diversion & Stream Summary 440 of 807; or 55. % Complete\n", + "+ Printing Diversion & Stream Summary 441 of 807; or 55. % Complete\n", + "+ Printing Diversion & Stream Summary 442 of 807; or 55. % Complete\n", + "+ Printing Diversion & Stream Summary 443 of 807; or 55. % Complete\n", + "+ Printing Diversion & Stream Summary 444 of 807; or 55. % Complete\n", + "+ Printing Diversion & Stream Summary 445 of 807; or 55. % Complete\n", + "+ Printing Diversion & Stream Summary 446 of 807; or 55. % Complete\n", + "+ Printing Diversion & Stream Summary 447 of 807; or 55. % Complete\n", + "+ Printing Diversion & Stream Summary 448 of 807; or 56. % Complete\n", + "+ Printing Diversion & Stream Summary 449 of 807; or 56. % Complete\n", + "+ Printing Diversion & Stream Summary 450 of 807; or 56. % Complete\n", + "+ Printing Diversion & Stream Summary 451 of 807; or 56. % Complete\n", + "+ Printing Diversion & Stream Summary 452 of 807; or 56. % Complete\n", + "+ Printing Diversion & Stream Summary 453 of 807; or 56. % Complete\n", + "+ Printing Diversion & Stream Summary 454 of 807; or 56. % Complete\n", + "+ Printing Diversion & Stream Summary 455 of 807; or 56. % Complete\n", + "+ Printing Diversion & Stream Summary 456 of 807; or 57. % Complete\n", + "+ Printing Diversion & Stream Summary 457 of 807; or 57. % Complete\n", + "+ Printing Diversion & Stream Summary 458 of 807; or 57. % Complete\n", + "+ Printing Diversion & Stream Summary 459 of 807; or 57. % Complete\n", + "+ Printing Diversion & Stream Summary 460 of 807; or 57. % Complete\n", + "+ Printing Diversion & Stream Summary 461 of 807; or 57. % Complete\n", + "+ Printing Diversion & Stream Summary 462 of 807; or 57. % Complete\n", + "+ Printing Diversion & Stream Summary 463 of 807; or 57. % Complete\n", + "+ Printing Diversion & Stream Summary 464 of 807; or 57. % Complete\n", + "+ Printing Diversion & Stream Summary 465 of 807; or 58. % Complete\n", + "+ Printing Diversion & Stream Summary 466 of 807; or 58. % Complete\n", + "+ Printing Diversion & Stream Summary 467 of 807; or 58. % Complete\n", + "+ Printing Diversion & Stream Summary 468 of 807; or 58. % Complete\n", + "+ Printing Diversion & Stream Summary 469 of 807; or 58. % Complete\n", + "+ Printing Diversion & Stream Summary 470 of 807; or 58. % Complete\n", + "+ Printing Diversion & Stream Summary 471 of 807; or 58. % Complete\n", + "+ Printing Diversion & Stream Summary 472 of 807; or 58. % Complete\n", + "+ Printing Diversion & Stream Summary 473 of 807; or 59. % Complete\n", + "+ Printing Diversion & Stream Summary 474 of 807; or 59. % Complete\n", + "+ Printing Diversion & Stream Summary 475 of 807; or 59. % Complete\n", + "+ Printing Diversion & Stream Summary 476 of 807; or 59. % Complete\n", + "+ Printing Diversion & Stream Summary 477 of 807; or 59. % Complete\n", + "+ Printing Diversion & Stream Summary 478 of 807; or 59. % Complete\n", + "+ Printing Diversion & Stream Summary 479 of 807; or 59. % Complete\n", + "+ Printing Diversion & Stream Summary 480 of 807; or 59. % Complete\n", + "+ Printing Diversion & Stream Summary 481 of 807; or 60. % Complete\n", + "+ Printing Diversion & Stream Summary 482 of 807; or 60. % Complete\n", + "+ Printing Diversion & Stream Summary 483 of 807; or 60. % Complete\n", + "+ Printing Diversion & Stream Summary 484 of 807; or 60. % Complete\n", + "+ Printing Diversion & Stream Summary 485 of 807; or 60. % Complete\n", + "+ Printing Diversion & Stream Summary 486 of 807; or 60. % Complete\n", + "+ Printing Diversion & Stream Summary 487 of 807; or 60. % Complete\n", + "+ Printing Diversion & Stream Summary 488 of 807; or 60. % Complete\n", + "+ Printing Diversion & Stream Summary 489 of 807; or 61. % Complete\n", + "+ Printing Diversion & Stream Summary 490 of 807; or 61. % Complete\n", + "+ Printing Diversion & Stream Summary 491 of 807; or 61. % Complete\n", + "+ Printing Diversion & Stream Summary 492 of 807; or 61. % Complete\n", + "+ Printing Diversion & Stream Summary 493 of 807; or 61. % Complete\n", + "+ Printing Diversion & Stream Summary 494 of 807; or 61. % Complete\n", + "+ Printing Diversion & Stream Summary 495 of 807; or 61. % Complete\n", + "+ Printing Diversion & Stream Summary 496 of 807; or 61. % Complete\n", + "+ Printing Diversion & Stream Summary 497 of 807; or 62. % Complete\n", + "+ Printing Diversion & Stream Summary 498 of 807; or 62. % Complete\n", + "+ Printing Diversion & Stream Summary 499 of 807; or 62. % Complete\n", + "+ Printing Diversion & Stream Summary 500 of 807; or 62. % Complete\n", + "+ Printing Diversion & Stream Summary 501 of 807; or 62. % Complete\n", + "+ Printing Diversion & Stream Summary 502 of 807; or 62. % Complete\n", + "+ Printing Diversion & Stream Summary 503 of 807; or 62. % Complete\n", + "+ Printing Diversion & Stream Summary 504 of 807; or 62. % Complete\n", + "+ Printing Diversion & Stream Summary 505 of 807; or 63. % Complete\n", + "+ Printing Diversion & Stream Summary 506 of 807; or 63. % Complete\n", + "+ Printing Diversion & Stream Summary 507 of 807; or 63. % Complete\n", + "+ Printing Diversion & Stream Summary 508 of 807; or 63. % Complete\n", + "+ Printing Diversion & Stream Summary 509 of 807; or 63. % Complete\n", + "+ Printing Diversion & Stream Summary 510 of 807; or 63. % Complete\n", + "+ Printing Diversion & Stream Summary 511 of 807; or 63. % Complete\n", + "+ Printing Diversion & Stream Summary 512 of 807; or 63. % Complete\n", + "+ Printing Diversion & Stream Summary 513 of 807; or 64. % Complete\n", + "+ Printing Diversion & Stream Summary 514 of 807; or 64. % Complete\n", + "+ Printing Diversion & Stream Summary 515 of 807; or 64. % Complete\n", + "+ Printing Diversion & Stream Summary 516 of 807; or 64. % Complete\n", + "+ Printing Diversion & Stream Summary 517 of 807; or 64. % Complete\n", + "+ Printing Diversion & Stream Summary 518 of 807; or 64. % Complete\n", + "+ Printing Diversion & Stream Summary 519 of 807; or 64. % Complete\n", + "+ Printing Diversion & Stream Summary 520 of 807; or 64. % Complete\n", + "+ Printing Diversion & Stream Summary 521 of 807; or 65. % Complete\n", + "+ Printing Diversion & Stream Summary 522 of 807; or 65. % Complete\n", + "+ Printing Diversion & Stream Summary 523 of 807; or 65. % Complete\n", + "+ Printing Diversion & Stream Summary 524 of 807; or 65. % Complete\n", + "+ Printing Diversion & Stream Summary 525 of 807; or 65. % Complete\n", + "+ Printing Diversion & Stream Summary 526 of 807; or 65. % Complete\n", + "+ Printing Diversion & Stream Summary 527 of 807; or 65. % Complete\n", + "+ Printing Diversion & Stream Summary 528 of 807; or 65. % Complete\n", + "+ Printing Diversion & Stream Summary 529 of 807; or 66. % Complete\n", + "+ Printing Diversion & Stream Summary 530 of 807; or 66. % Complete\n", + "+ Printing Diversion & Stream Summary 531 of 807; or 66. % Complete\n", + "+ Printing Diversion & Stream Summary 532 of 807; or 66. % Complete\n", + "+ Printing Diversion & Stream Summary 533 of 807; or 66. % Complete\n", + "+ Printing Diversion & Stream Summary 534 of 807; or 66. % Complete\n", + "+ Printing Diversion & Stream Summary 535 of 807; or 66. % Complete\n", + "+ Printing Diversion & Stream Summary 536 of 807; or 66. % Complete\n", + "+ Printing Diversion & Stream Summary 537 of 807; or 67. % Complete\n", + "+ Printing Diversion & Stream Summary 538 of 807; or 67. % Complete\n", + "+ Printing Diversion & Stream Summary 539 of 807; or 67. % Complete\n", + "+ Printing Diversion & Stream Summary 540 of 807; or 67. % Complete\n", + "+ Printing Diversion & Stream Summary 541 of 807; or 67. % Complete\n", + "+ Printing Diversion & Stream Summary 542 of 807; or 67. % Complete\n", + "+ Printing Diversion & Stream Summary 543 of 807; or 67. % Complete\n", + "+ Printing Diversion & Stream Summary 544 of 807; or 67. % Complete\n", + "+ Printing Diversion & Stream Summary 545 of 807; or 68. % Complete\n", + "+ Printing Diversion & Stream Summary 546 of 807; or 68. % Complete\n", + "+ Printing Diversion & Stream Summary 547 of 807; or 68. % Complete\n", + "+ Printing Diversion & Stream Summary 548 of 807; or 68. % Complete\n", + "+ Printing Diversion & Stream Summary 549 of 807; or 68. % Complete\n", + "+ Printing Diversion & Stream Summary 550 of 807; or 68. % Complete\n", + "+ Printing Diversion & Stream Summary 551 of 807; or 68. % Complete\n", + "+ Printing Diversion & Stream Summary 552 of 807; or 68. % Complete\n", + "+ Printing Diversion & Stream Summary 553 of 807; or 69. % Complete\n", + "+ Printing Diversion & Stream Summary 554 of 807; or 69. % Complete\n", + "+ Printing Diversion & Stream Summary 555 of 807; or 69. % Complete\n", + "+ Printing Diversion & Stream Summary 556 of 807; or 69. % Complete\n", + "+ Printing Diversion & Stream Summary 557 of 807; or 69. % Complete\n", + "+ Printing Diversion & Stream Summary 558 of 807; or 69. % Complete\n", + "+ Printing Diversion & Stream Summary 559 of 807; or 69. % Complete\n", + "+ Printing Diversion & Stream Summary 560 of 807; or 69. % Complete\n", + "+ Printing Diversion & Stream Summary 561 of 807; 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or 92. % Complete\n", + "+ Printing Structure Summary (*.xss) 409 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 410 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 411 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 412 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 413 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 414 of 443; or 94. % Complete\n", + "+ Printing Structure Summary (*.xss) 415 of 443; or 94. % Complete\n", + "+ Printing Structure Summary (*.xss) 416 of 443; or 94. % Complete\n", + "+ Printing Structure Summary (*.xss) 417 of 443; or 94. % Complete\n", + "+ Printing Structure Summary (*.xss) 418 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 419 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 420 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 421 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 422 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 423 of 443; or 96. % Complete\n", + "+ Printing Structure Summary (*.xss) 424 of 443; or 96. % Complete\n", + "+ Printing Structure Summary (*.xss) 425 of 443; or 96. % Complete\n", + "+ Printing Structure Summary (*.xss) 426 of 443; or 96. % Complete\n", + "+ Printing Structure Summary (*.xss) 427 of 443; or 97. % Complete\n", + "+ Printing Structure Summary (*.xss) 428 of 443; or 97. % Complete\n", + "+ Printing Structure Summary (*.xss) 429 of 443; or 97. % Complete\n", + "+ Printing Structure Summary (*.xss) 430 of 443; or 97. % Complete\n", + "+ Printing Structure Summary (*.xss) 431 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 432 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 433 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 434 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 435 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 436 of 443; or 99. % Complete\n", + "+ Printing Structure Summary (*.xss) 437 of 443; or 99. % Complete\n", + "+ Printing Structure Summary (*.xss) 438 of 443; or 99. % Complete\n", + "+ Printing Structure Summary (*.xss) 439 of 443; or 99. % Complete\n", + "+ Printing Structure Summary (*.xss) 440 of 443; or 100. % Complete\n", + "+ Printing Structure Summary (*.xss) 441 of 443; or 100. % Complete\n", + "+ Printing Structure Summary (*.xss) 442 of 443; or 100. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine Outifr\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutPln \n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Run output files are:\n", + " \n", + " Diversion output: *.xdd\n", + " Reservoir output: *.xre\n", + " Operating Rule Info: *.xop\n", + " Instream Reach Info: *.xir\n", + " Structure Summary: *.xss\n", + " Call (Control) Summary: *.xca\n", + " Plan Output: *.xpl\n", + " Replacement Reesrvoir Output: *.xrp\n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Termination\n", + " Statem; See detailed messages in file: cm2015B_S0_1.log \n", + " Stop 0\n", + "creating parquet for S0_1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 586.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running: S1_1\n", + " Parse; Command line argument: \n", + " cm2015B_S1_1 -simulate \n", + "________________________________________________________________________\n", + "\n", + " StateMod \n", + " State of Colorado - Water Supply Planning Model \n", + "\n", + " Version: 15.00.01\n", + " Last revision date: 2015/10/28\n", + "\n", + "________________________________________________________________________\n", + "\n", + " Opening log file cm2015B_S1_1.log \n", + " \n", + " Subroutine Execut\n", + " Subroutine Datinp\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Control File (*.ctl) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Network File (*.rin)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Reservoir Station File (*.res)\n", + "\n", + "________________________________________________________________________\n", + " GetFile; Stopped in GetFile, see the log file (*.log)\n", + " Stop 1\n", + "creating parquet for S1_1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 589.42it/s]\n" + ] + } + ], "source": [ "# set realization and sample\n", "realization = 1\n", @@ -325,31 +5963,33 @@ "\n", "# read RSP template\n", "with open(res_template_file) as template_obj:\n", + " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", + " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - "\n", + " \n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"RES\": f\"../../input_files/cm2015B_{scenario}.res\"}\n", - "\n", + " \n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - "\n", + " \n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir_res, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - "\n", + " \n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"cm2015B_{scenario}.rsp\")\n", - "\n", + " \n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - "\n", + " \n", " # construct simulated basin path\n", " simulated_basin_path = f\"cm2015B_{scenario}\"\n", "\n", @@ -358,26 +5998,22 @@ " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - "\n", - " # Save output to parquet files\n", - " print(\"creating parquet for \" + scenario)\n", - "\n", - " output_directory = os.path.join(parquet_dir_res + \"/scenario/\" + scenario)\n", - "\n", + " \n", + " #Save output to parquet files \n", + " print('creating parquet for ' + scenario)\n", + " \n", + " output_directory = os.path.join(parquet_dir_res+\"/scenario/\"+ scenario)\n", + " \n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - "\n", + " \n", " stm.xdd.convert_xdd(\n", " output_path=output_directory,\n", " allow_overwrite=True,\n", - " xdd_files=scenarios_dir_res\n", - " + \"/\"\n", - " + scenario\n", - " + \"/cm2015B_\"\n", - " + scenario\n", - " + \".xdd\",\n", - " id_subset=[\"3601008\"],\n", + " xdd_files=scenarios_dir_res + \"/\"+ scenario + \"/cm2015B_\"+scenario+\".xdd\",\n", + " id_subset=['3601008'],\n", " parallel_jobs=2,\n", + " preserve_string_dtype=False\n", " )" ] }, @@ -391,34 +6027,100 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "71a991e2-7e76-4e96-b4c0-70a641e66de3", + "execution_count": 10, + "id": "e10e1299-c52c-48bc-8581-32f860fc49fa", "metadata": { "tags": [] }, "outputs": [], "source": [ - "baseline = pd.read_parquet(\n", - " data_dir + \"/\" + \"historic_shortages/cm2015B.parquet\", engine=\"pyarrow\"\n", - ")\n", - "SOW_1 = pd.read_parquet(\n", - " parquet_dir_res + \"/scenario/S0_1/cm2015B_S0_1.parquet\", engine=\"pyarrow\"\n", - ")\n", + "baseline=pd.read_parquet(data_dir+'/'+'historic_shortages/cm2015B.parquet',engine='pyarrow')\n", + "SOW_1=pd.read_parquet(parquet_dir_res+'/scenario/S0_1/cm2015B_S0_1.parquet',engine='pyarrow')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5b68edf9-0d56-4809-9338-a81e605bae37", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "283920 0.\n", + "283921 0.\n", + "283922 0.\n", + "283923 220.\n", + "283924 201.\n", + " ... \n", + "285280 0.\n", + "285281 0.\n", + "285282 0.\n", + "285283 0.\n", + "285284 0.\n", + "Name: shortage_total, Length: 1365, dtype: object" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "baseline[\"shortage_total\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "71a991e2-7e76-4e96-b4c0-70a641e66de3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "baseline=pd.read_parquet(data_dir+'/'+'historic_shortages/cm2015B.parquet',engine='pyarrow')\n", + "SOW_1=pd.read_parquet(parquet_dir_res+'/scenario/S0_1/cm2015B_S0_1.parquet',engine='pyarrow')\n", "\n", - "# Subtract shortages with respect to the baseline\n", - "subset_df = pd.concat(\n", - " [baseline[\"year\"], baseline[\"shortage_total\"], SOW_1[\"shortage_total\"]], axis=1\n", - ")\n", - "subset_df = subset_df.set_axis([\"Year\", \"Baseline\", \"SOW_1\"], axis=1)\n", - "subset_df[\"Baseline\"] = subset_df[\"Baseline\"].astype(float)\n", - "subset_df[\"SOW_1\"] = subset_df[\"SOW_1\"].astype(float)\n", - "subset_df[\"Year\"] = subset_df[\"Year\"].astype(int)\n", - "subset_df[\"SOW_1_diff\"] = subset_df[\"SOW_1\"] - subset_df[\"Baseline\"]\n", + "#Subtract shortages with respect to the baseline\n", + "subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total']],axis=1)\n", + "subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1'], axis=1)\n", + "subset_df['SOW_1_diff']=subset_df['SOW_1']-subset_df['Baseline']\n", "\n", - "# Plot shortages\n", + "#Plot shortages\n", "fig, ax = plt.subplots()\n", "\n", - "ax.scatter(subset_df[\"Year\"], subset_df[\"SOW_1_diff\"])\n", + "ax.scatter(subset_df['Year'], subset_df['SOW_1_diff'])\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", diff --git a/notebooks/N4_Streamflow_File_Modification.ipynb b/notebooks/N4_Streamflow_File_Modification.ipynb index 5aecfd7..0d34272 100644 --- a/notebooks/N4_Streamflow_File_Modification.ipynb +++ b/notebooks/N4_Streamflow_File_Modification.ipynb @@ -3,7 +3,9 @@ { "cell_type": "markdown", "id": "06c1bdf6-ceb5-4ae8-8bbb-2922f512484d", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "## `statemodify` Quickstarter Notebook #4: Using External Methods to Generate Synthetic Streamflow Traces" ] @@ -56,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "c0cc93cd-8db4-4884-9bae-22388fe71724", "metadata": { "tags": [] @@ -67,13 +69,12 @@ "import logging\n", "import os\n", "import pickle\n", - "import subprocess\n", "from string import Template\n", + "import subprocess\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd\n", - "\n", + "import pandas as pd \n", "import statemodify as stm" ] }, @@ -87,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "688cee15-bf70-47e9-ab13-e760476c6f5a", "metadata": { "tags": [] @@ -107,7 +108,12 @@ "statemod_exe = os.path.join(root_dir, \"statemod\")\n", "\n", "# data directory and root name for the target basin\n", - "data_dir = os.path.join(home_dir, \"data\", \"cm2015_StateMod\", \"StateMod\")\n", + "data_dir = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"cm2015_StateMod\",\n", + " \"StateMod\"\n", + ")\n", "\n", "# directory to the target basin input files with root name for the basin\n", "basin_path = os.path.join(data_dir, \"cm2015B\")\n", @@ -115,8 +121,12 @@ "# scenarios output directory\n", "scenarios_dir = os.path.join(data_dir, \"scenarios\")\n", "\n", - "# path to iwr/xbm file\n", - "xbm_iwr_template_file = os.path.join(home_dir, \"data\", \"cm2015B_template_xbm_iwr.rsp\")" + "# path to iwr/xbm file \n", + "xbm_iwr_template_file = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"cm2015B_template_xbm_iwr.rsp\"\n", + ")" ] }, { @@ -129,14 +139,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "0ce17caa-a645-40c8-8bc0-3a35b3034f21", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# Make directory to store HMM parameters\n", + "#Make directory to store HMM parameters\n", "\n", "output_dir = os.path.join(data_dir, \"HMM_parameters\")\n", "\n", @@ -148,9 +158,9 @@ "# choice to save parameters to NumPy array files\n", "save_parameters = True\n", "\n", - "fit_array_dict = stm.hmm_multisite_fit(\n", - " n_basins=n_basins, save_parameters=save_parameters, output_directory=output_dir\n", - ")\n", + "fit_array_dict = stm.hmm_multisite_fit(n_basins=n_basins,\n", + " save_parameters=save_parameters,\n", + " output_directory=output_dir)\n", "\n", "# unpack output dictionary\n", "unconditional_dry = fit_array_dict[\"unconditional_dry\"]\n", @@ -160,7 +170,7 @@ "covariance_matrix_wet = fit_array_dict[\"covariance_matrix_wet\"]\n", "covariance_matrix_dry = fit_array_dict[\"covariance_matrix_dry\"]\n", "wet_state_means = fit_array_dict[\"wet_state_means\"]\n", - "dry_state_means = fit_array_dict[\"dry_state_means\"]" + "dry_state_means = fit_array_dict[\"dry_state_means\"]\n" ] }, { @@ -181,14 +191,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "7e7bdd2e-d9f3-47a2-b917-6ee413cee9f3", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# Create a folder to store the runs\n", + "#Create a folder to store the runs\n", "\n", "output_dir = os.path.join(data_dir, \"HMM_Runs\")\n", "\n", @@ -196,17 +206,15 @@ " os.makedirs(output_dir)\n", "\n", "# using the outputs of the fit function above; this function write output sample files to the output directory\n", - "stm.hmm_multisite_sample(\n", - " logAnnualQ_h,\n", - " transition_matrix,\n", - " unconditional_dry,\n", - " dry_state_means,\n", - " wet_state_means,\n", - " covariance_matrix_dry,\n", - " covariance_matrix_wet,\n", - " n_basins=n_basins,\n", - " output_directory=output_dir,\n", - ")" + "stm.hmm_multisite_sample(logAnnualQ_h,\n", + " transition_matrix,\n", + " unconditional_dry,\n", + " dry_state_means,\n", + " wet_state_means,\n", + " covariance_matrix_dry,\n", + " covariance_matrix_wet,\n", + " n_basins=n_basins,\n", + " output_directory=output_dir)" ] }, { @@ -219,20 +227,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "d26adf0d-f856-4a47-abc3-3dd10ee8a822", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "stm.plot_flow_duration_curves(\n", - " flow_realizations_directory=output_dir,\n", - " save_figure=True,\n", - " output_directory=output_dir,\n", - " figure_name=\"FDC\",\n", - " dpi=300,\n", - ")" + "stm.plot_flow_duration_curves(flow_realizations_directory=output_dir,\n", + " save_figure=True,output_directory=output_dir,\n", + " figure_name= 'FDC',\n", + " dpi= 300)" ] }, { @@ -255,23 +271,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "ad95781b-e5b6-429e-9841-244c16808a20", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# Make directory to store input files\n", + "#Make directory to store input files \n", "\n", "output_dir = os.path.join(data_dir, \"input_files\")\n", "\n", "if not os.path.exists(output_dir):\n", " os.makedirs(output_dir)\n", - "\n", + " \n", "\n", "flow_realizations_directory = os.path.join(data_dir, \"HMM_Runs\")\n", - "\n", + " \n", "scenario = \"1\"\n", "\n", "# basin name to process\n", @@ -287,17 +303,15 @@ "n_samples = 1\n", "\n", "# generate a batch of files using generated LHS\n", - "stm.modify_xbm_iwr(\n", - " output_dir=output_dir,\n", - " flow_realizations_directory=flow_realizations_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " seed_value=seed_value,\n", - " n_jobs=n_jobs,\n", - " n_samples=n_samples,\n", - " save_sample=True,\n", - " randomly_select_flow_sample=True,\n", - ")" + "stm.modify_xbm_iwr(output_dir=output_dir,\n", + " flow_realizations_directory=flow_realizations_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " seed_value=seed_value,\n", + " n_jobs=n_jobs,\n", + " n_samples=n_samples,\n", + " save_sample=True,\n", + " randomly_select_flow_sample=True) " ] }, { @@ -332,13 +346,2781 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "1a2e87bb-5676-4a51-ad85-e0b88ab0437d", "metadata": { "scrolled": true, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running: S0_1\n", + " Parse; Command line argument: \n", + " cm2015B_S0_1 -simulate \n", + "________________________________________________________________________\n", + "\n", + " StateMod \n", + " State of Colorado - Water Supply Planning Model \n", + "\n", + " Version: 15.00.01\n", + " Last revision date: 2015/10/28\n", + "\n", + "________________________________________________________________________\n", + "\n", + " Opening log file cm2015B_S0_1.log \n", + " \n", + " Subroutine Execut\n", + " Subroutine Datinp\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Control File (*.ctl) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Network File (*.rin)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Reservoir Station File (*.res)\n", + " Subroutine GetRes\n", + "\n", + " GetRes; Reservoir Station File (*.res) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Diversion Station File (*.dds)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Station File (*.ris)\n", + "\n", + "________________________________________________________________________\n", + " Datinp; Instream Flow Station File (*.ifs) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Well Station File (*.wes) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; Plan Station File (*.pln) \n", + "\n", + "________________________________________________________________________\n", + " Datinp; River Gage File (*.rig) \n", + " Subroutine Riginp\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Instream Flow Right File (*.ifr) 20 1 98\n", + "\n", + "________________________________________________________________________\n", + " Riginp; Reservoir Right File (*.rer) \n", + "\n", + "________________________________________________________________________\n", + " Riginp; Direct Diversion Right File (*.ddr) \n", + "\n", + "________________________________________________________________________\n", + " Oprinp; Operational Right File (*.opr) \n", + " Mdainp.for\n", + "\n", + "________________________________________________________________________\n", + " Mdainp; Instream flow Demand file - Annual (*.ifa) \n", + " Subroutine Execut\n", + "\n", + " Mdainp.for\n", + "+ Execut; Year 1908 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1908 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1908 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 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Execut; Year 1937 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1937 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1937 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1937 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1937 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1937 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1937 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1937 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1937 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1937 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1938 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1938 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1938 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1938 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1938 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1938 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1938 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1938 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1938 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1938 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1938 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1938 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1939 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1939 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1939 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1939 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1939 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1939 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1939 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1939 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1939 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1939 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1939 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1939 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1940 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1940 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1940 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1940 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1940 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1940 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1940 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1940 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1940 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1940 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1940 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1940 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1941 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1941 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1941 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1941 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1941 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1941 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1941 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1941 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1941 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1941 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1941 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1941 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1942 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1942 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1942 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1942 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1942 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1942 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1942 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1942 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1942 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1942 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1942 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1942 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1943 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1943 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1943 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1943 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1943 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1943 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1943 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1943 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1943 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1943 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1943 Month NOV Day 1 Reoperation 1 Annual Total 1\n", 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Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1945 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1945 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1945 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1945 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1945 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1945 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1945 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1945 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1945 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1945 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1945 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1946 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1946 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1946 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1946 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1946 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1946 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1946 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1946 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1946 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1946 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1946 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1946 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1947 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1947 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1947 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1947 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1947 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1947 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1947 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1947 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1947 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1947 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1947 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1947 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1948 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1948 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1948 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1948 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1948 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1948 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1948 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1948 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1948 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1948 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1948 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1948 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1949 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1949 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1949 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1949 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1949 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1949 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1949 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1949 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1949 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1949 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1949 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1949 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1950 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1950 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1950 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1950 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1950 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1950 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1950 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1950 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1950 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1950 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1950 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1950 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1951 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1951 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1951 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1951 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1951 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1951 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1951 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1951 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1951 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1951 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1951 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1951 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1952 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1952 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1952 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1952 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1952 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1952 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1952 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1952 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1952 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1952 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1952 Month NOV Day 1 Reoperation 1 Annual Total 1\n", 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Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1954 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1954 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1954 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1954 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1954 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1954 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1954 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1954 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1954 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1954 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1954 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1955 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1955 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1955 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1955 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1955 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1955 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1955 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1955 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1955 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1955 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1955 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1955 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1956 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1956 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1956 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1956 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1956 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1956 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1956 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1956 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1956 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1956 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1956 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1956 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1957 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1957 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1957 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1957 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1957 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1957 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1957 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1957 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1957 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1957 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1957 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1957 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1958 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1958 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1958 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1958 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1958 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1958 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1958 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1958 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1958 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1958 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1958 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1958 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1959 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1959 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1959 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1959 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1959 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1959 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1959 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1959 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1959 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1959 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1959 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1959 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1960 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1960 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1960 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1960 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1960 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1960 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1960 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1960 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1960 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1960 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1960 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1960 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1961 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1961 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1961 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1961 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1961 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1961 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1961 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1961 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1961 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1961 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1961 Month NOV Day 1 Reoperation 1 Annual Total 1\n", 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Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1963 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1963 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1963 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1963 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1963 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1963 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1963 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1963 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1963 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1963 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1963 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1964 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1964 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ 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Execut; Year 1993 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1993 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1993 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1993 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1993 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1993 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1993 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1994 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1994 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1994 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1994 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1994 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1994 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1994 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1994 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1994 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1994 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1994 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1994 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1995 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1995 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1995 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1995 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1995 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1995 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1995 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1995 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1995 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1995 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1995 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1995 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1996 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1996 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1996 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1996 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1996 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1996 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1996 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1996 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1996 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1996 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1996 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1996 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1997 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1997 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1997 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1997 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1997 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1997 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1997 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1997 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1997 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1997 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1997 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1997 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1998 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1998 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1998 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1998 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1998 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1998 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1998 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1998 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1998 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1998 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1998 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1998 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 1999 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 1999 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 1999 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 1999 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 1999 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 1999 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 1999 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 1999 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 1999 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 1999 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 1999 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 1999 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2000 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2000 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2000 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2000 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2000 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2000 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2000 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2000 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2000 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2000 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2000 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2000 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2001 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2001 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2001 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2001 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2001 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2001 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2001 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2001 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2001 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2001 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2001 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2001 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2002 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2002 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2002 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2002 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2002 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2002 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2002 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2002 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2002 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2002 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2002 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2002 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2003 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2003 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2003 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2003 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2003 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2003 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2003 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2003 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2003 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2003 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2003 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2003 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2004 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2004 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2004 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2004 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2004 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2004 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2004 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2004 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2004 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2004 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2004 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2004 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2005 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2005 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2005 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2005 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2005 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2005 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2005 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2005 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2005 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2005 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2005 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2005 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2006 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2006 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2006 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2006 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2006 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2006 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2006 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2006 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2006 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2006 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2006 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2006 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2007 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2007 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2007 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2007 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2007 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2007 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2007 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2007 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2007 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2007 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2007 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2007 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2008 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2008 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2008 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2008 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2008 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2008 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2008 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2008 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2008 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2008 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2008 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2008 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2009 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2009 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2009 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2009 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2009 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2009 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2009 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2009 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2009 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2009 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2009 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2009 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2010 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2010 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2010 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2010 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2010 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2010 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2010 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2010 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2010 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2010 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2010 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2010 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2011 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2011 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2011 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2011 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2011 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2011 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2011 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2011 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2011 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2011 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2011 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2011 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2012 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2012 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2012 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2012 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2012 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2012 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2012 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2012 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2012 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + " Mdainp.for\n", + "+ Execut; Year 2012 Month OCT Day 1 Reoperation 1 Annual Total 0\n", + "+ Execut; Year 2012 Month NOV Day 1 Reoperation 1 Annual Total 1\n", + "+ Execut; Year 2012 Month DEC Day 1 Reoperation 1 Annual Total 2\n", + "+ Execut; Year 2013 Month JAN Day 1 Reoperation 1 Annual Total 3\n", + "+ Execut; Year 2013 Month FEB Day 1 Reoperation 1 Annual Total 4\n", + "+ Execut; Year 2013 Month MAR Day 1 Reoperation 1 Annual Total 5\n", + "+ Execut; Year 2013 Month APR Day 1 Reoperation 1 Annual Total 6\n", + "+ Execut; Year 2013 Month MAY Day 1 Reoperation 1 Annual Total 7\n", + "+ Execut; Year 2013 Month JUN Day 1 Reoperation 1 Annual Total 8\n", + "+ Execut; Year 2013 Month JUL Day 1 Reoperation 1 Annual Total 9\n", + "+ Execut; Year 2013 Month AUG Day 1 Reoperation 1 Annual Total 10\n", + "+ Execut; Year 2013 Month SEP Day 1 Reoperation 1 Annual Total 11\n", + "\n", + "________________________________________________________________________\n", + " Execut; On Year 2013 Month SEP Day 1\n", + " The maximum number of reoperations 1\n", + "\n", + "________________________________________________________________________\n", + " Execut; Writing reports\n", + "\n", + "________________________________________________________________________\n", + " Execut; \n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutRes \n", + "+ Printing Reservoir Summary 1 of 36; or 3. % Complete\n", + "+ Printing Reservoir Summary 2 of 36; or 6. % Complete\n", + "+ Printing Reservoir Summary 3 of 36; or 8. % Complete\n", + "+ Printing Reservoir Summary 4 of 36; or 11. % Complete\n", + "+ Printing Reservoir Summary 5 of 36; or 14. % Complete\n", + "+ Printing Reservoir Summary 8 of 36; or 22. % Complete\n", + "+ Printing Reservoir Summary 9 of 36; or 25. % Complete\n", + "+ Printing Reservoir Summary 10 of 36; or 28. % Complete\n", + "+ Printing Reservoir Summary 11 of 36; or 31. % Complete\n", + "+ Printing Reservoir Summary 12 of 36; or 33. % Complete\n", + "+ Printing Reservoir Summary 13 of 36; or 36. % Complete\n", + "+ Printing Reservoir Summary 14 of 36; or 39. % Complete\n", + "+ Printing Reservoir Summary 15 of 36; or 42. % Complete\n", + "+ Printing Reservoir Summary 16 of 36; or 44. % Complete\n", + "+ Printing Reservoir Summary 17 of 36; or 47. % Complete\n", + "+ Printing Reservoir Summary 18 of 36; or 50. % Complete\n", + "+ Printing Reservoir Summary 19 of 36; or 53. % Complete\n", + "+ Printing Reservoir Summary 20 of 36; or 56. % Complete\n", + "+ Printing Reservoir Summary 21 of 36; or 58. % Complete\n", + "+ Printing Reservoir Summary 22 of 36; or 61. % Complete\n", + "+ Printing Reservoir Summary 23 of 36; or 64. % Complete\n", + "+ Printing Reservoir Summary 24 of 36; or 67. % Complete\n", + "+ Printing Reservoir Summary 25 of 36; or 69. % Complete\n", + "+ Printing Reservoir Summary 26 of 36; or 72. % Complete\n", + "+ Printing Reservoir Summary 27 of 36; or 75. % Complete\n", + "+ Printing Reservoir Summary 28 of 36; or 78. % Complete\n", + "+ Printing Reservoir Summary 29 of 36; or 81. % Complete\n", + "+ Printing Reservoir Summary 30 of 36; or 83. % Complete\n", + "+ Printing Reservoir Summary 31 of 36; or 86. % Complete\n", + "+ Printing Reservoir Summary 32 of 36; or 89. % Complete\n", + "+ Printing Reservoir Summary 33 of 36; or 92. % Complete\n", + "+ Printing Reservoir Summary 34 of 36; or 94. % Complete\n", + "+ Printing Reservoir Summary 35 of 36; or 97. % Complete\n", + "+ Printing Reservoir Summary 36 of 36; or 100. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutDivW \n", + "+ Printing Diversion & Stream Summary 1 of 807; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 2 of 807; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 3 of 807; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 4 of 807; or 0. % Complete\n", + "+ Printing Diversion & Stream Summary 5 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 6 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 7 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 8 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 9 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 10 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 11 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 12 of 807; or 1. % Complete\n", + "+ Printing Diversion & Stream Summary 13 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 14 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 15 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 16 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 17 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 18 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 19 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 20 of 807; or 2. % Complete\n", + "+ Printing Diversion & Stream Summary 21 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 22 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 23 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 24 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 25 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 26 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 27 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 28 of 807; or 3. % Complete\n", + "+ Printing Diversion & Stream Summary 29 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 30 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 31 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 32 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 33 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 34 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 35 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 36 of 807; or 4. % Complete\n", + "+ Printing Diversion & Stream Summary 37 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 38 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 39 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 40 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 41 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 42 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 43 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 44 of 807; or 5. % Complete\n", + "+ Printing Diversion & Stream Summary 45 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 46 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 47 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 48 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 49 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 50 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 51 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 52 of 807; or 6. % Complete\n", + "+ Printing Diversion & Stream Summary 53 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 54 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 55 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 56 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 57 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 58 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 59 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 60 of 807; or 7. % Complete\n", + "+ Printing Diversion & Stream Summary 61 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 62 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 63 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 64 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 65 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 66 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 67 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 68 of 807; or 8. % Complete\n", + "+ Printing Diversion & Stream Summary 69 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 70 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 71 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 72 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 73 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 74 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 75 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 76 of 807; or 9. % Complete\n", + "+ Printing Diversion & Stream Summary 77 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 78 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 79 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 80 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 81 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 82 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 83 of 807; or 10. % Complete\n", + "+ Printing Diversion & Stream Summary 84 of 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Printing Diversion & Stream Summary 98 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 99 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 100 of 807; or 12. % Complete\n", + "+ Printing Diversion & Stream Summary 101 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 102 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 103 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 104 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 105 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 106 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 107 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 108 of 807; or 13. % Complete\n", + "+ Printing Diversion & Stream Summary 109 of 807; or 14. % Complete\n", + "+ Printing Diversion & Stream Summary 110 of 807; or 14. % Complete\n", + "+ Printing Diversion & Stream 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Summary (*.xss) 234 of 443; or 53. % Complete\n", + "+ Printing Structure Summary (*.xss) 235 of 443; or 53. % Complete\n", + "+ Printing Structure Summary (*.xss) 236 of 443; or 53. % Complete\n", + "+ Printing Structure Summary (*.xss) 237 of 443; or 54. % Complete\n", + "+ Printing Structure Summary (*.xss) 238 of 443; or 54. % Complete\n", + "+ Printing Structure Summary (*.xss) 239 of 443; or 54. % Complete\n", + "+ Printing Structure Summary (*.xss) 240 of 443; or 54. % Complete\n", + "+ Printing Structure Summary (*.xss) 241 of 443; or 55. % Complete\n", + "+ Printing Structure Summary (*.xss) 242 of 443; or 55. % Complete\n", + "+ Printing Structure Summary (*.xss) 243 of 443; or 55. % Complete\n", + "+ Printing Structure Summary (*.xss) 244 of 443; or 55. % Complete\n", + "+ Printing Structure Summary (*.xss) 245 of 443; or 56. % Complete\n", + "+ Printing Structure Summary (*.xss) 246 of 443; or 56. % Complete\n", + "+ Printing Structure Summary (*.xss) 247 of 443; or 56. % Complete\n", + "+ Printing Structure Summary (*.xss) 248 of 443; or 56. % Complete\n", + "+ Printing Structure Summary (*.xss) 249 of 443; or 56. % Complete\n", + "+ Printing Structure Summary (*.xss) 250 of 443; or 57. % Complete\n", + "+ Printing Structure Summary (*.xss) 251 of 443; or 57. % Complete\n", + "+ Printing Structure Summary (*.xss) 252 of 443; or 57. % Complete\n", + "+ Printing Structure Summary (*.xss) 253 of 443; or 57. % Complete\n", + "+ Printing Structure Summary (*.xss) 254 of 443; or 58. % Complete\n", + "+ Printing Structure Summary (*.xss) 255 of 443; or 58. % Complete\n", + "+ Printing Structure Summary (*.xss) 256 of 443; or 58. % Complete\n", + "+ Printing Structure Summary (*.xss) 257 of 443; or 58. % Complete\n", + "+ Printing Structure Summary (*.xss) 258 of 443; or 58. % Complete\n", + "+ Printing Structure Summary (*.xss) 259 of 443; or 59. % Complete\n", + "+ Printing Structure Summary (*.xss) 260 of 443; or 59. % Complete\n", + "+ Printing Structure 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Complete\n", + "+ Printing Structure Summary (*.xss) 275 of 443; or 62. % Complete\n", + "+ Printing Structure Summary (*.xss) 276 of 443; or 63. % Complete\n", + "+ Printing Structure Summary (*.xss) 277 of 443; or 63. % Complete\n", + "+ Printing Structure Summary (*.xss) 278 of 443; or 63. % Complete\n", + "+ Printing Structure Summary (*.xss) 279 of 443; or 63. % Complete\n", + "+ Printing Structure Summary (*.xss) 280 of 443; or 63. % Complete\n", + "+ Printing Structure Summary (*.xss) 281 of 443; or 64. % Complete\n", + "+ Printing Structure Summary (*.xss) 282 of 443; or 64. % Complete\n", + "+ Printing Structure Summary (*.xss) 283 of 443; or 64. % Complete\n", + "+ Printing Structure Summary (*.xss) 284 of 443; or 64. % Complete\n", + "+ Printing Structure Summary (*.xss) 285 of 443; or 65. % Complete\n", + "+ Printing Structure Summary (*.xss) 286 of 443; or 65. % Complete\n", + "+ Printing Structure Summary (*.xss) 287 of 443; or 65. % Complete\n", + "+ Printing Structure 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Complete\n", + "+ Printing Structure Summary (*.xss) 302 of 443; or 68. % Complete\n", + "+ Printing Structure Summary (*.xss) 303 of 443; or 69. % Complete\n", + "+ Printing Structure Summary (*.xss) 304 of 443; or 69. % Complete\n", + "+ Printing Structure Summary (*.xss) 305 of 443; or 69. % Complete\n", + "+ Printing Structure Summary (*.xss) 306 of 443; or 69. % Complete\n", + "+ Printing Structure Summary (*.xss) 307 of 443; or 70. % Complete\n", + "+ Printing Structure Summary (*.xss) 308 of 443; or 70. % Complete\n", + "+ Printing Structure Summary (*.xss) 309 of 443; or 70. % Complete\n", + "+ Printing Structure Summary (*.xss) 310 of 443; or 70. % Complete\n", + "+ Printing Structure Summary (*.xss) 311 of 443; or 70. % Complete\n", + "+ Printing Structure Summary (*.xss) 312 of 443; or 71. % Complete\n", + "+ Printing Structure Summary (*.xss) 313 of 443; or 71. % Complete\n", + "+ Printing Structure Summary (*.xss) 314 of 443; or 71. % Complete\n", + "+ Printing Structure 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Complete\n", + "+ Printing Structure Summary (*.xss) 329 of 443; or 74. % Complete\n", + "+ Printing Structure Summary (*.xss) 330 of 443; or 75. % Complete\n", + "+ Printing Structure Summary (*.xss) 331 of 443; or 75. % Complete\n", + "+ Printing Structure Summary (*.xss) 332 of 443; or 75. % Complete\n", + "+ Printing Structure Summary (*.xss) 333 of 443; or 75. % Complete\n", + "+ Printing Structure Summary (*.xss) 334 of 443; or 76. % Complete\n", + "+ Printing Structure Summary (*.xss) 335 of 443; or 76. % Complete\n", + "+ Printing Structure Summary (*.xss) 336 of 443; or 76. % Complete\n", + "+ Printing Structure Summary (*.xss) 337 of 443; or 76. % Complete\n", + "+ Printing Structure Summary (*.xss) 338 of 443; or 77. % Complete\n", + "+ Printing Structure Summary (*.xss) 339 of 443; or 77. % Complete\n", + "+ Printing Structure Summary (*.xss) 340 of 443; or 77. % Complete\n", + "+ Printing Structure Summary (*.xss) 341 of 443; or 77. % Complete\n", + "+ Printing Structure 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Complete\n", + "+ Printing Structure Summary (*.xss) 356 of 443; or 81. % Complete\n", + "+ Printing Structure Summary (*.xss) 357 of 443; or 81. % Complete\n", + "+ Printing Structure Summary (*.xss) 358 of 443; or 81. % Complete\n", + "+ Printing Structure Summary (*.xss) 359 of 443; or 81. % Complete\n", + "+ Printing Structure Summary (*.xss) 360 of 443; or 81. % Complete\n", + "+ Printing Structure Summary (*.xss) 361 of 443; or 82. % Complete\n", + "+ Printing Structure Summary (*.xss) 362 of 443; or 82. % Complete\n", + "+ Printing Structure Summary (*.xss) 363 of 443; or 82. % Complete\n", + "+ Printing Structure Summary (*.xss) 364 of 443; or 82. % Complete\n", + "+ Printing Structure Summary (*.xss) 365 of 443; or 83. % Complete\n", + "+ Printing Structure Summary (*.xss) 366 of 443; or 83. % Complete\n", + "+ Printing Structure Summary (*.xss) 367 of 443; or 83. % Complete\n", + "+ Printing Structure Summary (*.xss) 368 of 443; or 83. % Complete\n", + "+ Printing Structure Summary (*.xss) 369 of 443; or 84. % Complete\n", + "+ Printing Structure Summary (*.xss) 370 of 443; or 84. % Complete\n", + "+ Printing Structure Summary (*.xss) 371 of 443; or 84. % Complete\n", + "+ Printing Structure Summary (*.xss) 372 of 443; or 84. % Complete\n", + "+ Printing Structure Summary (*.xss) 373 of 443; or 84. % Complete\n", + "+ Printing Structure Summary (*.xss) 374 of 443; or 85. % Complete\n", + "+ Printing Structure Summary (*.xss) 375 of 443; or 85. % Complete\n", + "+ Printing Structure Summary (*.xss) 376 of 443; or 85. % Complete\n", + "+ Printing Structure Summary (*.xss) 377 of 443; or 85. % Complete\n", + "+ Printing Structure Summary (*.xss) 378 of 443; or 86. % Complete\n", + "+ Printing Structure Summary (*.xss) 379 of 443; or 86. % Complete\n", + "+ Printing Structure Summary (*.xss) 380 of 443; or 86. % Complete\n", + "+ Printing Structure Summary (*.xss) 381 of 443; or 86. % Complete\n", + "+ Printing Structure Summary (*.xss) 382 of 443; or 86. % Complete\n", + "+ Printing Structure Summary (*.xss) 383 of 443; or 87. % Complete\n", + "+ Printing Structure Summary (*.xss) 384 of 443; or 87. % Complete\n", + "+ Printing Structure Summary (*.xss) 385 of 443; or 87. % Complete\n", + "+ Printing Structure Summary (*.xss) 386 of 443; or 87. % Complete\n", + "+ Printing Structure Summary (*.xss) 387 of 443; or 88. % Complete\n", + "+ Printing Structure Summary (*.xss) 388 of 443; or 88. % Complete\n", + "+ Printing Structure Summary (*.xss) 389 of 443; or 88. % Complete\n", + "+ Printing Structure Summary (*.xss) 390 of 443; or 88. % Complete\n", + "+ Printing Structure Summary (*.xss) 391 of 443; or 88. % Complete\n", + "+ Printing Structure Summary (*.xss) 392 of 443; or 89. % Complete\n", + "+ Printing Structure Summary (*.xss) 393 of 443; or 89. % Complete\n", + "+ Printing Structure Summary (*.xss) 394 of 443; or 89. % Complete\n", + "+ Printing Structure Summary (*.xss) 395 of 443; or 89. % Complete\n", + "+ Printing Structure Summary (*.xss) 396 of 443; or 90. % Complete\n", + "+ Printing Structure Summary (*.xss) 397 of 443; or 90. % Complete\n", + "+ Printing Structure Summary (*.xss) 398 of 443; or 90. % Complete\n", + "+ Printing Structure Summary (*.xss) 399 of 443; or 90. % Complete\n", + "+ Printing Structure Summary (*.xss) 400 of 443; or 91. % Complete\n", + "+ Printing Structure Summary (*.xss) 401 of 443; or 91. % Complete\n", + "+ Printing Structure Summary (*.xss) 402 of 443; or 91. % Complete\n", + "+ Printing Structure Summary (*.xss) 403 of 443; or 91. % Complete\n", + "+ Printing Structure Summary (*.xss) 404 of 443; or 91. % Complete\n", + "+ Printing Structure Summary (*.xss) 405 of 443; or 92. % Complete\n", + "+ Printing Structure Summary (*.xss) 406 of 443; or 92. % Complete\n", + "+ Printing Structure Summary (*.xss) 407 of 443; or 92. % Complete\n", + "+ Printing Structure Summary (*.xss) 408 of 443; or 92. % Complete\n", + "+ Printing Structure Summary (*.xss) 409 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 410 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 411 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 412 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 413 of 443; or 93. % Complete\n", + "+ Printing Structure Summary (*.xss) 414 of 443; or 94. % Complete\n", + "+ Printing Structure Summary (*.xss) 415 of 443; or 94. % Complete\n", + "+ Printing Structure Summary (*.xss) 416 of 443; or 94. % Complete\n", + "+ Printing Structure Summary (*.xss) 417 of 443; or 94. % Complete\n", + "+ Printing Structure Summary (*.xss) 418 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 419 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 420 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 421 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 422 of 443; or 95. % Complete\n", + "+ Printing Structure Summary (*.xss) 423 of 443; or 96. % Complete\n", + "Using sample file: /home/jovyan/data/cm2015_StateMod/StateMod/HMM_Runs/AnnualQ_s69.txt\n", + "+ Printing Structure Summary (*.xss) 424 of 443; or 96. % Complete\n", + "+ Printing Structure Summary (*.xss) 425 of 443; or 96. % Complete\n", + "+ Printing Structure Summary (*.xss) 426 of 443; or 96. % Complete\n", + "+ Printing Structure Summary (*.xss) 427 of 443; or 97. % Complete\n", + "+ Printing Structure Summary (*.xss) 428 of 443; or 97. % Complete\n", + "+ Printing Structure Summary (*.xss) 429 of 443; or 97. % Complete\n", + "+ Printing Structure Summary (*.xss) 430 of 443; or 97. % Complete\n", + "+ Printing Structure Summary (*.xss) 431 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 432 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 433 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 434 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 435 of 443; or 98. % Complete\n", + "+ Printing Structure Summary (*.xss) 436 of 443; or 99. % Complete\n", + "+ Printing Structure Summary (*.xss) 437 of 443; or 99. % Complete\n", + "+ Printing Structure Summary (*.xss) 438 of 443; or 99. % Complete\n", + "+ Printing Structure Summary (*.xss) 439 of 443; or 99. % Complete\n", + "+ Printing Structure Summary (*.xss) 440 of 443; or 100. % Complete\n", + "+ Printing Structure Summary (*.xss) 441 of 443; or 100. % Complete\n", + "+ Printing Structure Summary (*.xss) 442 of 443; or 100. % Complete\n", + "\n", + "________________________________________________________________________\n", + " Subroutine Outifr\n", + "\n", + "________________________________________________________________________\n", + " Subroutine OutPln \n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Run output files are:\n", + " \n", + " Diversion output: *.xdd\n", + " Reservoir output: *.xre\n", + " Operating Rule Info: *.xop\n", + " Instream Reach Info: *.xir\n", + " Structure Summary: *.xss\n", + " Call (Control) Summary: *.xca\n", + " Plan Output: *.xpl\n", + " Replacement Reesrvoir Output: *.xrp\n", + "\n", + "________________________________________________________________________\n", + " Execut; Successful Termination\n", + " Statem; See detailed messages in file: cm2015B_S0_1.log \n", + " Stop 0\n" + ] + } + ], "source": [ "# set realization and sample\n", "realization = 1\n", @@ -346,34 +3128,33 @@ "\n", "# read RSP template\n", "with open(xbm_iwr_template_file) as template_obj:\n", + " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", + " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - "\n", + " \n", " # dictionary holding search keys and replacement values to update the template file\n", - " d = {\n", - " \"XBM\": f\"../../input_files/cm2015B_{scenario}.xbm\",\n", - " \"IWR\": f\"../../input_files/cm2015B_{scenario}.iwr\",\n", - " }\n", - "\n", + " d = {\"XBM\": f\"../../input_files/cm2015B_{scenario}.xbm\",\"IWR\": f\"../../input_files/cm2015B_{scenario}.iwr\"}\n", + " \n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - "\n", + " \n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - "\n", + " \n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"cm2015B_{scenario}.rsp\")\n", - "\n", + " \n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - "\n", + " \n", " # construct simulated basin path\n", " simulated_basin_path = f\"cm2015B_{scenario}\"\n", "\n", @@ -381,7 +3162,7 @@ " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", - " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])" + " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n" ] }, { @@ -394,24 +3175,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "3852e116-c490-4fac-b8a2-34db27520917", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Example with Granby Lake\n", - "zip_file_path = os.path.join(home_dir, \"data\", \"Granby_Dataset.zip\")\n", - "final_directory = os.path.join(home_dir, \"data/\")\n", + "zip_file_path = os.path.join(home_dir, 'data', 'Granby_Dataset.zip')\n", + "final_directory = os.path.join(home_dir, 'data/')\n", "\n", - "!unzip $zip_file_path -d $final_directory\n", - "granby_hmm, granby_hist, granby_hist_mean, granby_hist_1p = stm.read_xre(\n", - " os.path.join(home_dir, \"data/Upper_Colorado/\"), \"Granby\"\n", - ")\n", + "!unzip $zip_file_path -d $final_directory \n", + "granby_hmm, granby_hist, granby_hist_mean, granby_hist_1p = stm.read_xre(os.path.join(home_dir,\"data/Upper_Colorado/\"), 'Granby')\n", "\n", "# Plot quantiles\n", - "stm.plot_res_quantiles(granby_hmm, granby_hist_mean, \"Lake Granby\")" + "stm.plot_res_quantiles(granby_hmm, granby_hist_mean, 'Lake Granby')" ] }, { @@ -432,15 +3222,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "a757bb9c-8c95-4a84-a481-0b878eee6be2", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# using the output of the above `read_xre` function as inputs\n", - "stm.plot_reservoir_boxes(granby_hmm, granby_hist, \"Lake Granby\")" + "stm.plot_reservoir_boxes(granby_hmm, granby_hist, 'Lake Granby')" ] }, { diff --git a/notebooks/N5_Batch_Modification.ipynb b/notebooks/N5_Batch_Modification.ipynb index 0bec3e9..6ef477a 100644 --- a/notebooks/N5_Batch_Modification.ipynb +++ b/notebooks/N5_Batch_Modification.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "id": "fbd29da0-2275-4bee-b232-26f6718aba49", "metadata": { "ExecuteTime": { @@ -33,13 +33,12 @@ "import logging\n", "import os\n", "import pickle\n", - "import subprocess\n", "from string import Template\n", + "import subprocess\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd\n", - "\n", + "import pandas as pd \n", "import statemodify as stm" ] }, @@ -57,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "f0804d05-d034-4c0d-af23-e6e8211a7afe", "metadata": { "ExecuteTime": { @@ -81,7 +80,12 @@ "statemod_exe = os.path.join(root_dir, \"statemod\")\n", "\n", "# data directory and root name for the target basin\n", - "data_dir = os.path.join(home_dir, \"data\", \"cm2015_StateMod\", \"StateMod\")\n", + "data_dir = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"cm2015_StateMod\",\n", + " \"StateMod\"\n", + ")\n", "\n", "# directory to the target basin input files with root name for the basin\n", "basin_path = os.path.join(data_dir, \"cm2015B\")\n", @@ -89,12 +93,16 @@ "# scenarios output directory\n", "scenarios_dir = os.path.join(data_dir, \"scenarios\")\n", "\n", - "# parquet output directory\n", - "parquet_dir = os.path.join(data_dir, \"parquet\")\n", + "#parquet output directory\n", + "parquet_dir=os.path.join(data_dir, \"parquet\")\n", "\n", "\n", "# path to template file\n", - "multi_template_file = os.path.join(home_dir, \"data\", \"cm2015B_template_multi.rsp\")" + "multi_template_file = os.path.join(\n", + " home_dir,\n", + " \"data\",\n", + " \"cm2015B_template_multi.rsp\"\n", + ")" ] }, { @@ -138,14 +146,12 @@ }, "outputs": [ { - "ename": "AttributeError", - "evalue": "module 'statemodify' has no attribute 'modify_batch'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[3], line 49\u001b[0m\n\u001b[0;32m 10\u001b[0m problem_dict \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 11\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_samples\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m 12\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnum_vars\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m3\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 45\u001b[0m }\n\u001b[0;32m 46\u001b[0m }\n\u001b[0;32m 48\u001b[0m \u001b[38;5;66;03m# run in batch\u001b[39;00m\n\u001b[1;32m---> 49\u001b[0m fn_parameter_dict \u001b[38;5;241m=\u001b[39m \u001b[43mstm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodify_batch\u001b[49m(problem_dict\u001b[38;5;241m=\u001b[39mproblem_dict)\n", - "\u001b[1;31mAttributeError\u001b[0m: module 'statemodify' has no attribute 'modify_batch'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Running modify_eva\n", + "Running modify_ddr\n", + "Running modify_ddm\n" ] } ], @@ -161,9 +167,13 @@ "# problem dictionary\n", "problem_dict = {\n", " \"n_samples\": 1,\n", - " \"num_vars\": 3,\n", - " \"names\": [\"modify_eva\", \"modify_ddr\", \"modify_ddm\"],\n", - " \"bounds\": [[-0.5, 1.0], None, [0.5, 1.0]],\n", + " 'num_vars': 3,\n", + " 'names': ['modify_eva', 'modify_ddr', 'modify_ddm'],\n", + " 'bounds': [\n", + " [-0.5, 1.0],\n", + " None,\n", + " [0.5, 1.0]\n", + " ],\n", " # additional settings for each function\n", " \"modify_eva\": {\n", " \"seed_value\": seed_value,\n", @@ -171,7 +181,7 @@ " \"scenario\": scenario,\n", " \"basin_name\": basin_name,\n", " \"query_field\": \"id\",\n", - " \"ids\": [\"10008\", \"10009\"],\n", + " \"ids\": [\"10008\", \"10009\"]\n", " },\n", " \"modify_ddr\": {\n", " \"seed_value\": seed_value,\n", @@ -181,7 +191,7 @@ " \"query_field\": \"id\",\n", " \"ids\": [\"3600507.01\", \"3600507.02\"],\n", " \"admin\": [1, None],\n", - " \"on_off\": [1, 1],\n", + " \"on_off\": [1, 1]\n", " },\n", " \"modify_ddm\": {\n", " \"seed_value\": seed_value,\n", @@ -189,8 +199,8 @@ " \"scenario\": scenario,\n", " \"basin_name\": basin_name,\n", " \"query_field\": \"id\",\n", - " \"ids\": [\"3600507\", \"3600603\"],\n", - " },\n", + " \"ids\": [\"3600507\", \"3600603\"]\n", + " }\n", "}\n", "\n", "# run in batch\n", @@ -475,2537 +485,22 @@ "+ Execut; Year 1921 Month SEP Day 1 Reoperation 1 Annual Total 11\n", " Mdainp.for\n", "+ 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Reoperation 1 Annual Total 4\n", - "+ Execut; Year 2006 Month MAR Day 1 Reoperation 1 Annual Total 5\n", - "+ Execut; Year 2006 Month APR Day 1 Reoperation 1 Annual Total 6\n", - "+ Execut; Year 2006 Month MAY Day 1 Reoperation 1 Annual Total 7\n", - "+ Execut; Year 2006 Month JUN Day 1 Reoperation 1 Annual Total 8\n", - "+ Execut; Year 2006 Month JUL Day 1 Reoperation 1 Annual Total 9\n", - "+ Execut; Year 2006 Month AUG Day 1 Reoperation 1 Annual Total 10\n", - "+ Execut; Year 2006 Month SEP Day 1 Reoperation 1 Annual Total 11\n", - " Mdainp.for\n", - "+ Execut; Year 2006 Month OCT Day 1 Reoperation 1 Annual Total 0\n", - "+ Execut; Year 2006 Month NOV Day 1 Reoperation 1 Annual Total 1\n", - "+ Execut; Year 2006 Month DEC Day 1 Reoperation 1 Annual Total 2\n", - "+ Execut; Year 2007 Month JAN Day 1 Reoperation 1 Annual Total 3\n", - "+ Execut; Year 2007 Month FEB Day 1 Reoperation 1 Annual Total 4\n", - "+ Execut; Year 2007 Month MAR Day 1 Reoperation 1 Annual Total 5\n", - "+ Execut; Year 2007 Month APR Day 1 Reoperation 1 Annual Total 6\n", - "+ Execut; Year 2007 Month MAY Day 1 Reoperation 1 Annual Total 7\n", - "+ Execut; Year 2007 Month JUN Day 1 Reoperation 1 Annual Total 8\n", - "+ Execut; Year 2007 Month JUL Day 1 Reoperation 1 Annual Total 9\n", - "+ Execut; Year 2007 Month AUG Day 1 Reoperation 1 Annual Total 10\n", - "+ Execut; Year 2007 Month SEP Day 1 Reoperation 1 Annual Total 11\n", - " Mdainp.for\n", - "+ Execut; Year 2007 Month OCT Day 1 Reoperation 1 Annual Total 0\n", - "+ Execut; Year 2007 Month NOV Day 1 Reoperation 1 Annual Total 1\n", - "+ Execut; Year 2007 Month DEC Day 1 Reoperation 1 Annual Total 2\n", - "+ Execut; Year 2008 Month JAN Day 1 Reoperation 1 Annual Total 3\n", - "+ Execut; Year 2008 Month FEB Day 1 Reoperation 1 Annual Total 4\n", - "+ Execut; Year 2008 Month MAR Day 1 Reoperation 1 Annual Total 5\n", - "+ Execut; Year 2008 Month APR Day 1 Reoperation 1 Annual Total 6\n", - "+ Execut; Year 2008 Month MAY Day 1 Reoperation 1 Annual Total 7\n", - "+ Execut; Year 2008 Month JUN Day 1 Reoperation 1 Annual Total 8\n", - "+ Execut; Year 2008 Month JUL Day 1 Reoperation 1 Annual Total 9\n", - "+ Execut; Year 2008 Month AUG Day 1 Reoperation 1 Annual Total 10\n", - "+ Execut; Year 2008 Month SEP Day 1 Reoperation 1 Annual Total 11\n", - " Mdainp.for\n", - "+ Execut; Year 2008 Month OCT Day 1 Reoperation 1 Annual Total 0\n", - "+ Execut; Year 2008 Month NOV Day 1 Reoperation 1 Annual Total 1\n", - "+ Execut; Year 2008 Month DEC Day 1 Reoperation 1 Annual Total 2\n", - "+ Execut; Year 2009 Month JAN Day 1 Reoperation 1 Annual Total 3\n", - "+ Execut; Year 2009 Month FEB Day 1 Reoperation 1 Annual Total 4\n", - "+ Execut; Year 2009 Month MAR Day 1 Reoperation 1 Annual Total 5\n", - "+ Execut; Year 2009 Month APR Day 1 Reoperation 1 Annual Total 6\n", - "+ Execut; Year 2009 Month MAY Day 1 Reoperation 1 Annual Total 7\n", - "+ Execut; Year 2009 Month JUN Day 1 Reoperation 1 Annual Total 8\n", - "+ Execut; Year 2009 Month JUL Day 1 Reoperation 1 Annual Total 9\n", - "+ Execut; Year 2009 Month AUG Day 1 Reoperation 1 Annual Total 10\n", - "+ Execut; Year 2009 Month SEP Day 1 Reoperation 1 Annual Total 11\n", - " Mdainp.for\n", - "+ Execut; Year 2009 Month OCT Day 1 Reoperation 1 Annual Total 0\n", - "+ Execut; Year 2009 Month NOV Day 1 Reoperation 1 Annual Total 1\n", - "+ Execut; Year 2009 Month DEC Day 1 Reoperation 1 Annual Total 2\n", - "+ Execut; Year 2010 Month JAN Day 1 Reoperation 1 Annual Total 3\n", - "+ Execut; Year 2010 Month FEB Day 1 Reoperation 1 Annual Total 4\n", - "+ Execut; Year 2010 Month MAR Day 1 Reoperation 1 Annual Total 5\n", - "+ Execut; Year 2010 Month APR Day 1 Reoperation 1 Annual Total 6\n", - "+ Execut; Year 2010 Month MAY Day 1 Reoperation 1 Annual Total 7\n", - "+ Execut; Year 2010 Month JUN Day 1 Reoperation 1 Annual Total 8\n", - "+ Execut; Year 2010 Month JUL Day 1 Reoperation 1 Annual Total 9\n", - "+ Execut; Year 2010 Month AUG Day 1 Reoperation 1 Annual Total 10\n", - "+ Execut; Year 2010 Month SEP Day 1 Reoperation 1 Annual Total 11\n", - " Mdainp.for\n", - "+ Execut; Year 2010 Month OCT Day 1 Reoperation 1 Annual Total 0\n", - "+ Execut; Year 2010 Month NOV Day 1 Reoperation 1 Annual Total 1\n", - "+ Execut; Year 2010 Month DEC Day 1 Reoperation 1 Annual Total 2\n", - "+ Execut; Year 2011 Month JAN Day 1 Reoperation 1 Annual Total 3\n", - "+ Execut; Year 2011 Month FEB Day 1 Reoperation 1 Annual Total 4\n", - "+ Execut; Year 2011 Month MAR Day 1 Reoperation 1 Annual Total 5\n", - "+ Execut; Year 2011 Month APR Day 1 Reoperation 1 Annual Total 6\n", - "+ Execut; Year 2011 Month MAY Day 1 Reoperation 1 Annual Total 7\n", - "+ Execut; Year 2011 Month JUN Day 1 Reoperation 1 Annual Total 8\n", - "+ Execut; Year 2011 Month JUL Day 1 Reoperation 1 Annual Total 9\n", - "+ Execut; Year 2011 Month AUG Day 1 Reoperation 1 Annual Total 10\n", - "+ Execut; Year 2011 Month SEP Day 1 Reoperation 1 Annual Total 11\n", - " Mdainp.for\n", - "+ Execut; Year 2011 Month OCT Day 1 Reoperation 1 Annual Total 0\n", - "+ Execut; Year 2011 Month NOV Day 1 Reoperation 1 Annual Total 1\n", - "+ Execut; Year 2011 Month DEC Day 1 Reoperation 1 Annual Total 2\n", - "+ Execut; Year 2012 Month JAN Day 1 Reoperation 1 Annual Total 3\n", - "+ Execut; Year 2012 Month FEB Day 1 Reoperation 1 Annual Total 4\n", - "+ Execut; Year 2012 Month MAR Day 1 Reoperation 1 Annual Total 5\n", - "+ Execut; Year 2012 Month APR Day 1 Reoperation 1 Annual Total 6\n", - "+ Execut; Year 2012 Month MAY Day 1 Reoperation 1 Annual Total 7\n", - "+ Execut; Year 2012 Month JUN Day 1 Reoperation 1 Annual Total 8\n", - "+ Execut; Year 2012 Month JUL Day 1 Reoperation 1 Annual Total 9\n", - "+ Execut; Year 2012 Month AUG Day 1 Reoperation 1 Annual Total 10\n", - "+ Execut; Year 2012 Month SEP Day 1 Reoperation 1 Annual Total 11\n", - " Mdainp.for\n", - "+ Execut; Year 2012 Month OCT Day 1 Reoperation 1 Annual Total 0\n", - "+ Execut; Year 2012 Month NOV Day 1 Reoperation 1 Annual Total 1\n", - "+ Execut; Year 2012 Month DEC Day 1 Reoperation 1 Annual Total 2\n", - "+ Execut; Year 2013 Month JAN Day 1 Reoperation 1 Annual Total 3\n", - "+ Execut; Year 2013 Month FEB Day 1 Reoperation 1 Annual Total 4\n", - "+ Execut; Year 2013 Month MAR Day 1 Reoperation 1 Annual Total 5\n", - "+ Execut; Year 2013 Month APR Day 1 Reoperation 1 Annual Total 6\n", - "+ Execut; Year 2013 Month MAY Day 1 Reoperation 1 Annual Total 7\n", - "+ Execut; Year 2013 Month JUN Day 1 Reoperation 1 Annual Total 8\n", - "+ Execut; Year 2013 Month JUL Day 1 Reoperation 1 Annual Total 9\n", - "+ Execut; Year 2013 Month AUG Day 1 Reoperation 1 Annual Total 10\n", - "+ Execut; Year 2013 Month SEP Day 1 Reoperation 1 Annual Total 11\n", - "\n", - "________________________________________________________________________\n", - " Execut; On Year 2013 Month SEP Day 1\n", - " The maximum number of reoperations 1\n", - "\n", - "________________________________________________________________________\n", - " Execut; Writing reports\n", - "\n", - "________________________________________________________________________\n", - " Execut; \n", - "\n", - "________________________________________________________________________\n", - " Subroutine OutRes \n", - "+ Printing Reservoir Summary 1 of 36; or 3. % Complete\n", - "+ Printing Reservoir Summary 2 of 36; or 6. % Complete\n", - "+ Printing Reservoir Summary 3 of 36; or 8. % Complete\n", - "+ Printing Reservoir Summary 4 of 36; or 11. % Complete\n", - "+ Printing Reservoir Summary 5 of 36; or 14. % Complete\n", - "+ Printing Reservoir Summary 8 of 36; or 22. % Complete\n", - "+ Printing Reservoir Summary 9 of 36; or 25. % Complete\n", - "+ Printing Reservoir Summary 10 of 36; or 28. % Complete\n", - "+ Printing Reservoir Summary 11 of 36; or 31. % Complete\n", - "+ Printing Reservoir Summary 12 of 36; or 33. % Complete\n", - "+ Printing Reservoir Summary 13 of 36; or 36. % Complete\n", - "+ Printing Reservoir Summary 14 of 36; or 39. % Complete\n", - "+ Printing Reservoir Summary 15 of 36; or 42. % Complete\n", - "+ Printing Reservoir Summary 16 of 36; or 44. % Complete\n", - "+ Printing Reservoir Summary 17 of 36; or 47. % Complete\n", - "+ Printing Reservoir Summary 18 of 36; or 50. % Complete\n", - "+ Printing Reservoir Summary 19 of 36; or 53. % Complete\n", - "+ Printing Reservoir Summary 20 of 36; or 56. % Complete\n", - "+ Printing Reservoir Summary 21 of 36; or 58. % Complete\n", - "+ Printing Reservoir Summary 22 of 36; or 61. % Complete\n", - "+ Printing Reservoir Summary 23 of 36; or 64. % Complete\n", - "+ Printing Reservoir Summary 24 of 36; or 67. % Complete\n", - "+ Printing Reservoir Summary 25 of 36; or 69. % Complete\n", - "+ Printing Reservoir Summary 26 of 36; or 72. % Complete\n", - "+ Printing Reservoir Summary 27 of 36; or 75. % Complete\n", - "+ Printing Reservoir Summary 28 of 36; or 78. % Complete\n", - "+ Printing Reservoir Summary 29 of 36; or 81. % Complete\n", - "+ Printing Reservoir Summary 30 of 36; or 83. % Complete\n", - "+ Printing Reservoir Summary 31 of 36; or 86. % Complete\n", - "+ Printing Reservoir Summary 32 of 36; or 89. % Complete\n", - "+ Printing Reservoir Summary 33 of 36; or 92. % Complete\n", - "+ Printing Reservoir Summary 34 of 36; or 94. % Complete\n", - "+ Printing Reservoir Summary 35 of 36; or 97. % Complete\n", - "+ Printing Reservoir Summary 36 of 36; or 100. % Complete\n", - "\n", - "________________________________________________________________________\n", - " Subroutine OutDivW \n", - "+ Printing Diversion & Stream Summary 1 of 807; or 0. % Complete\n", - "+ Printing Diversion & Stream Summary 2 of 807; or 0. % Complete\n", - "+ Printing Diversion & Stream Summary 3 of 807; or 0. % Complete\n", - "+ Printing Diversion & Stream Summary 4 of 807; or 0. % Complete\n", - "+ Printing Diversion & Stream Summary 5 of 807; or 1. % Complete\n", - "+ Printing Diversion & Stream Summary 6 of 807; or 1. % Complete\n", - "+ Printing Diversion & Stream Summary 7 of 807; or 1. % Complete\n", - "+ Printing Diversion & Stream Summary 8 of 807; or 1. % Complete\n", - "+ Printing Diversion & Stream Summary 9 of 807; or 1. % Complete\n", - "+ Printing Diversion & Stream Summary 10 of 807; or 1. % Complete\n", - "+ Printing Diversion & Stream Summary 11 of 807; or 1. % Complete\n", - "+ Printing Diversion & Stream Summary 12 of 807; or 1. % Complete\n", - "+ Printing Diversion & Stream Summary 13 of 807; or 2. % Complete\n", - "+ Printing Diversion & Stream Summary 14 of 807; or 2. % Complete\n", - "+ Printing Diversion & Stream Summary 15 of 807; or 2. % Complete\n", - "+ Printing Diversion & Stream Summary 16 of 807; or 2. % Complete\n", - "+ Printing Diversion & Stream Summary 17 of 807; or 2. % Complete\n", - "+ Printing Diversion & Stream Summary 18 of 807; or 2. % Complete\n", - "+ Printing Diversion & Stream Summary 19 of 807; or 2. % Complete\n", - "+ Printing Diversion & Stream Summary 20 of 807; or 2. % Complete\n", - "+ Printing Diversion & Stream Summary 21 of 807; or 3. % Complete\n", - "+ Printing Diversion & Stream Summary 22 of 807; or 3. % Complete\n", - "+ Printing Diversion & Stream Summary 23 of 807; or 3. % Complete\n", - "+ Printing Diversion & Stream Summary 24 of 807; or 3. % Complete\n", - "+ Printing Diversion & Stream Summary 25 of 807; or 3. % Complete\n", - "+ Printing Diversion & Stream Summary 26 of 807; or 3. % Complete\n", - "+ Printing Diversion & Stream Summary 27 of 807; or 3. % Complete\n", - "+ Printing Diversion & Stream Summary 28 of 807; or 3. % Complete\n", - "+ Printing Diversion & Stream Summary 29 of 807; or 4. % Complete\n", - "+ Printing Diversion & Stream Summary 30 of 807; or 4. % Complete\n", - "+ Printing Diversion & Stream Summary 31 of 807; or 4. % Complete\n", - "+ Printing Diversion & Stream Summary 32 of 807; or 4. % Complete\n", - "+ Printing Diversion & Stream Summary 33 of 807; or 4. % Complete\n", - "+ Printing Diversion & Stream Summary 34 of 807; or 4. % Complete\n", - "+ Printing Diversion & Stream Summary 35 of 807; or 4. % Complete\n", - "+ Printing Diversion & Stream Summary 36 of 807; or 4. % Complete\n", - "+ Printing Diversion & Stream Summary 37 of 807; or 5. % Complete\n", - "+ Printing Diversion & Stream Summary 38 of 807; or 5. % Complete\n", - "+ Printing Diversion & Stream Summary 39 of 807; or 5. % Complete\n", - "+ Printing Diversion & Stream Summary 40 of 807; or 5. % Complete\n", - "+ Printing Diversion & Stream Summary 41 of 807; or 5. % Complete\n", - "+ Printing Diversion & Stream Summary 42 of 807; or 5. % Complete\n", - "+ Printing Diversion & Stream Summary 43 of 807; or 5. % Complete\n", - "+ Printing Diversion & Stream Summary 44 of 807; or 5. % Complete\n", - "+ Printing Diversion & Stream Summary 45 of 807; or 6. % Complete\n", - "+ Printing Diversion & Stream Summary 46 of 807; or 6. % Complete\n", - "+ Printing Diversion & Stream Summary 47 of 807; or 6. % Complete\n", - "+ Printing Diversion & Stream Summary 48 of 807; or 6. % Complete\n", - "+ Printing Diversion & Stream Summary 49 of 807; or 6. % Complete\n", - "+ Printing Diversion & Stream Summary 50 of 807; or 6. % Complete\n", - "+ Printing Diversion & Stream Summary 51 of 807; or 6. % Complete\n", - "+ Printing Diversion & Stream Summary 52 of 807; or 6. % Complete\n", - "+ Printing Diversion & Stream Summary 53 of 807; or 7. % Complete\n", - "+ Printing Diversion & Stream Summary 54 of 807; or 7. % Complete\n", - "+ Printing Diversion & Stream Summary 55 of 807; or 7. % Complete\n", - "+ Printing Diversion & Stream Summary 56 of 807; or 7. % Complete\n", - "+ Printing Diversion & Stream Summary 57 of 807; or 7. % Complete\n", - "+ Printing Diversion & Stream Summary 58 of 807; or 7. % Complete\n", - "+ Printing Diversion & Stream Summary 59 of 807; or 7. % Complete\n", - "+ Printing Diversion & Stream Summary 60 of 807; or 7. % Complete\n", - "+ Printing Diversion & Stream Summary 61 of 807; or 8. % Complete\n", - "+ Printing Diversion & Stream Summary 62 of 807; or 8. % Complete\n", - "+ Printing Diversion & Stream Summary 63 of 807; or 8. % Complete\n", - "+ Printing Diversion & Stream Summary 64 of 807; or 8. % Complete\n", - "+ Printing Diversion & Stream Summary 65 of 807; or 8. % Complete\n", - "+ Printing Diversion & Stream Summary 66 of 807; or 8. % Complete\n", - "+ Printing Diversion & Stream Summary 67 of 807; or 8. % Complete\n", - "+ Printing Diversion & Stream Summary 68 of 807; or 8. % Complete\n", - "+ Printing Diversion & Stream Summary 69 of 807; or 9. % Complete\n", - "+ Printing Diversion & Stream Summary 70 of 807; or 9. % Complete\n", - "+ Printing Diversion & Stream Summary 71 of 807; or 9. % Complete\n", - "+ Printing Diversion & Stream Summary 72 of 807; or 9. % Complete\n", - "+ Printing Diversion & Stream Summary 73 of 807; or 9. % Complete\n", - "+ Printing Diversion & Stream Summary 74 of 807; or 9. % Complete\n", - "+ Printing Diversion & Stream Summary 75 of 807; or 9. % Complete\n", - "+ Printing Diversion & Stream Summary 76 of 807; or 9. % Complete\n", - "+ Printing Diversion & Stream Summary 77 of 807; or 10. % Complete\n", - "+ Printing Diversion & Stream Summary 78 of 807; or 10. % Complete\n", - "+ Printing Diversion & Stream Summary 79 of 807; or 10. % Complete\n", - "+ Printing Diversion & Stream Summary 80 of 807; or 10. % Complete\n", - "+ Printing Diversion & Stream Summary 81 of 807; or 10. % Complete\n", - "+ Printing Diversion & Stream Summary 82 of 807; or 10. % Complete\n", - "+ Printing Diversion & Stream Summary 83 of 807; or 10. % Complete\n", - "+ Printing Diversion & Stream Summary 84 of 807; or 10. % Complete\n", - "+ Printing Diversion & Stream Summary 85 of 807; or 11. % Complete\n", - "+ Printing Diversion & Stream Summary 86 of 807; or 11. % Complete\n", - "+ Printing Diversion & Stream Summary 87 of 807; or 11. % Complete\n", - "+ Printing Diversion & Stream Summary 88 of 807; or 11. % Complete\n", - "+ Printing Diversion & Stream Summary 89 of 807; or 11. % Complete\n", - "+ Printing Diversion & Stream Summary 90 of 807; or 11. % Complete\n", - "+ Printing Diversion & Stream Summary 91 of 807; or 11. % Complete\n", - "+ Printing Diversion & Stream Summary 92 of 807; or 11. % Complete\n", - "+ Printing Diversion & Stream Summary 93 of 807; or 12. % Complete\n", - "+ Printing Diversion & Stream Summary 94 of 807; or 12. % Complete\n", - "+ Printing Diversion & Stream Summary 95 of 807; or 12. % Complete\n", - "+ Printing Diversion & Stream Summary 96 of 807; or 12. % Complete\n", - "+ Printing Diversion & Stream Summary 97 of 807; or 12. % Complete\n", - "+ Printing Diversion & Stream Summary 98 of 807; or 12. % Complete\n", - "+ Printing Diversion & Stream Summary 99 of 807; or 12. % Complete\n", - "+ Printing Diversion & Stream Summary 100 of 807; or 12. % Complete\n", - "+ Printing Diversion & Stream Summary 101 of 807; or 13. % Complete\n", - "+ Printing Diversion & Stream Summary 102 of 807; or 13. % Complete\n", - "+ Printing Diversion & Stream Summary 103 of 807; or 13. % Complete\n", - "+ Printing Diversion & Stream Summary 104 of 807; or 13. % Complete\n", - "+ Printing Diversion & Stream Summary 105 of 807; or 13. % Complete\n", - "+ Printing Diversion & Stream Summary 106 of 807; or 13. % Complete\n", - "+ Printing Diversion & Stream Summary 107 of 807; or 13. % Complete\n", - "+ Printing Diversion & Stream Summary 108 of 807; or 13. % Complete\n", - "+ Printing Diversion & Stream Summary 109 of 807; or 14. % Complete\n", - "+ Printing Diversion & Stream Summary 110 of 807; or 14. % Complete\n", - "+ Printing Diversion & Stream Summary 111 of 807; or 14. % Complete\n", - "+ Printing Diversion & Stream Summary 112 of 807; or 14. % Complete\n", - "+ Printing Diversion & Stream Summary 113 of 807; or 14. % Complete\n", - "+ Printing Diversion & Stream Summary 114 of 807; or 14. % Complete\n", - "+ Printing Diversion & Stream Summary 115 of 807; or 14. % Complete\n", - "+ Printing Diversion & Stream Summary 116 of 807; or 14. % Complete\n", - "+ Printing Diversion & Stream Summary 117 of 807; or 14. % Complete\n", - "+ Printing Diversion & Stream Summary 118 of 807; or 15. % Complete\n", - "+ Printing Diversion & Stream Summary 119 of 807; or 15. % Complete\n", - "+ Printing Diversion & Stream Summary 120 of 807; or 15. % Complete\n", - "+ Printing Diversion & Stream Summary 121 of 807; or 15. % Complete\n", - "+ Printing Diversion & Stream Summary 122 of 807; or 15. % Complete\n", - "+ Printing Diversion & Stream Summary 123 of 807; or 15. % Complete\n", - "+ Printing Diversion & Stream Summary 124 of 807; or 15. % Complete\n", - "+ Printing Diversion & Stream Summary 125 of 807; or 15. % Complete\n", - "+ Printing Diversion & Stream Summary 126 of 807; or 16. % Complete\n", - "+ Printing Diversion & Stream Summary 127 of 807; or 16. % Complete\n", - "+ Printing Diversion & Stream Summary 128 of 807; or 16. % Complete\n", - "+ Printing Diversion & Stream Summary 129 of 807; or 16. % Complete\n", - "+ Printing Diversion & Stream Summary 130 of 807; or 16. % Complete\n", - "+ Printing Diversion & Stream Summary 131 of 807; or 16. % Complete\n", - "+ Printing Diversion & Stream Summary 132 of 807; or 16. % Complete\n", - "+ Printing Diversion & Stream Summary 133 of 807; or 16. % Complete\n", - "+ Printing Diversion & Stream Summary 134 of 807; or 17. % Complete\n", - "+ Printing Diversion & Stream Summary 135 of 807; or 17. % Complete\n", - "+ Printing Diversion & Stream Summary 136 of 807; or 17. % Complete\n", - "+ Printing Diversion & Stream Summary 137 of 807; or 17. % Complete\n", - "+ Printing Diversion & Stream Summary 138 of 807; or 17. % Complete\n", - "+ Printing Diversion & Stream Summary 139 of 807; or 17. % Complete\n", - "+ Printing Diversion & Stream Summary 140 of 807; or 17. % Complete\n", - "+ Printing Diversion & Stream Summary 141 of 807; or 17. % Complete\n", - "+ Printing Diversion & Stream Summary 142 of 807; or 18. % Complete\n", - "+ Printing Diversion & Stream Summary 143 of 807; or 18. % Complete\n", - "+ Printing Diversion & Stream Summary 144 of 807; or 18. % Complete\n", - "+ Printing Diversion & Stream Summary 145 of 807; or 18. % Complete\n", - "+ Printing Diversion & Stream Summary 146 of 807; or 18. % Complete\n", - "+ Printing Diversion & Stream Summary 147 of 807; or 18. % Complete\n", - "+ Printing Diversion & Stream Summary 148 of 807; or 18. % Complete\n", - "+ Printing Diversion & Stream Summary 149 of 807; or 18. % Complete\n", - "+ Printing Diversion & Stream Summary 150 of 807; or 19. % Complete\n", - "+ Printing Diversion & Stream Summary 151 of 807; or 19. % Complete\n", - "+ Printing Diversion & Stream Summary 152 of 807; or 19. % Complete\n", - "+ Printing Diversion & Stream Summary 153 of 807; or 19. % Complete\n", - "+ Printing Diversion & Stream Summary 154 of 807; or 19. % Complete\n", - "+ Printing Diversion & Stream Summary 155 of 807; or 19. % Complete\n", - "+ Printing Diversion & Stream Summary 156 of 807; or 19. % Complete\n", - "+ Printing Diversion & Stream Summary 157 of 807; or 19. % Complete\n", - "+ Printing Diversion & Stream Summary 158 of 807; or 20. % Complete\n", - "+ Printing Diversion & Stream Summary 159 of 807; or 20. % Complete\n", - "+ Printing Diversion & Stream Summary 160 of 807; or 20. % Complete\n", - "+ Printing Diversion & Stream Summary 161 of 807; or 20. % Complete\n", - "+ Printing Diversion & Stream Summary 162 of 807; or 20. % Complete\n", - "+ Printing Diversion & Stream Summary 163 of 807; or 20. % Complete\n", - "+ Printing Diversion & Stream Summary 164 of 807; or 20. % Complete\n", - "+ Printing Diversion & Stream Summary 165 of 807; or 20. % Complete\n", - "+ Printing Diversion & Stream Summary 166 of 807; or 21. % Complete\n", - "+ Printing Diversion & Stream Summary 167 of 807; or 21. % Complete\n", - "+ Printing Diversion & Stream Summary 168 of 807; or 21. % Complete\n", - "+ Printing Diversion & Stream Summary 169 of 807; or 21. % Complete\n", - "+ Printing Diversion & Stream Summary 170 of 807; or 21. % Complete\n", - "+ Printing Diversion & Stream Summary 171 of 807; or 21. % Complete\n", - "+ Printing Diversion & Stream Summary 172 of 807; or 21. % Complete\n", - "+ Printing Diversion & Stream Summary 173 of 807; or 21. % Complete\n", - "+ Printing Diversion & Stream Summary 174 of 807; or 22. % Complete\n", - "+ Printing Diversion & Stream Summary 175 of 807; or 22. % Complete\n", - "+ Printing Diversion & Stream Summary 176 of 807; or 22. % Complete\n", - "+ Printing Diversion & Stream Summary 177 of 807; or 22. % Complete\n", - "+ Printing Diversion & Stream Summary 178 of 807; or 22. % Complete\n", - "+ Printing Diversion & Stream Summary 179 of 807; or 22. % Complete\n", - "+ Printing Diversion & Stream Summary 180 of 807; or 22. % Complete\n", - "+ Printing Diversion & Stream Summary 181 of 807; or 22. % Complete\n", - "+ Printing Diversion & Stream Summary 182 of 807; or 23. % Complete\n", - "+ Printing Diversion & Stream Summary 183 of 807; or 23. % Complete\n", - "+ Printing Diversion & Stream Summary 184 of 807; or 23. % Complete\n", - "+ Printing Diversion & Stream Summary 185 of 807; or 23. % Complete\n", - "+ Printing Diversion & Stream Summary 186 of 807; or 23. % Complete\n", - "+ Printing Diversion & Stream Summary 187 of 807; or 23. % Complete\n", - "+ Printing Diversion & Stream Summary 188 of 807; or 23. % Complete\n", - "+ Printing Diversion & Stream Summary 189 of 807; or 23. % Complete\n", - "+ Printing Diversion & Stream Summary 190 of 807; or 24. % Complete\n", - "+ Printing Diversion & Stream Summary 191 of 807; or 24. % Complete\n", - "+ Printing Diversion & Stream Summary 192 of 807; or 24. % Complete\n", - "+ Printing Diversion & Stream Summary 193 of 807; or 24. % Complete\n", - "+ Printing Diversion & Stream Summary 194 of 807; or 24. % Complete\n", - "+ Printing Diversion & Stream Summary 195 of 807; or 24. % Complete\n", - "+ Printing Diversion & Stream Summary 196 of 807; or 24. % Complete\n", - "+ Printing Diversion & Stream Summary 197 of 807; or 24. % Complete\n", - "+ Printing Diversion & Stream Summary 198 of 807; or 25. % Complete\n", - "+ Printing Diversion & Stream Summary 199 of 807; or 25. % Complete\n", - "+ Printing Diversion & Stream Summary 200 of 807; or 25. % Complete\n", - "+ Printing Diversion & Stream Summary 201 of 807; or 25. % Complete\n", - "+ Printing Diversion & Stream Summary 202 of 807; or 25. % Complete\n", - "+ Printing Diversion & Stream Summary 203 of 807; or 25. % Complete\n", - "+ Printing Diversion & Stream Summary 204 of 807; or 25. % Complete\n", - "+ Printing Diversion & Stream Summary 205 of 807; or 25. % Complete\n", - "+ Printing Diversion & Stream Summary 206 of 807; or 26. % Complete\n", - "+ Printing Diversion & Stream Summary 207 of 807; or 26. % Complete\n", - "+ Printing Diversion & Stream Summary 208 of 807; or 26. % Complete\n", - "+ Printing Diversion & Stream Summary 209 of 807; or 26. % Complete\n", - "+ Printing Diversion & Stream Summary 210 of 807; or 26. % Complete\n", - "+ Printing Diversion & Stream Summary 211 of 807; or 26. % Complete\n", - "+ Printing Diversion & Stream Summary 212 of 807; or 26. % Complete\n", - "+ Printing Diversion & Stream Summary 213 of 807; or 26. % Complete\n", - "+ Printing Diversion & Stream Summary 214 of 807; or 27. % Complete\n", - "+ Printing Diversion & Stream Summary 215 of 807; or 27. % Complete\n", - "+ Printing Diversion & Stream Summary 216 of 807; or 27. % Complete\n", - "+ Printing Diversion & Stream Summary 217 of 807; or 27. % Complete\n", - "+ Printing Diversion & Stream Summary 218 of 807; or 27. % Complete\n", - "+ Printing Diversion & Stream Summary 219 of 807; or 27. % Complete\n", - "+ Printing Diversion & Stream Summary 220 of 807; or 27. % Complete\n", - "+ Printing Diversion & Stream Summary 221 of 807; or 27. % Complete\n", - "+ Printing Diversion & Stream Summary 222 of 807; or 28. % Complete\n", - "+ Printing Diversion & Stream Summary 223 of 807; or 28. % Complete\n", - "+ Printing Diversion & Stream Summary 224 of 807; or 28. % Complete\n", - "+ Printing Diversion & Stream Summary 225 of 807; or 28. % Complete\n", - "+ Printing Diversion & Stream Summary 226 of 807; or 28. % Complete\n", - "+ Printing Diversion & Stream Summary 227 of 807; or 28. % Complete\n", - "+ Printing Diversion & Stream Summary 228 of 807; or 28. % Complete\n", - "+ Printing Diversion & Stream Summary 229 of 807; or 28. % Complete\n", - "+ Printing Diversion & Stream Summary 230 of 807; or 29. % Complete\n", - "+ Printing Diversion & Stream Summary 231 of 807; or 29. % Complete\n", - "+ Printing Diversion & Stream Summary 232 of 807; or 29. % Complete\n", - "+ Printing Diversion & Stream Summary 233 of 807; or 29. % Complete\n", - "+ 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Stream Summary 247 of 807; or 31. % Complete\n", - "+ Printing Diversion & Stream Summary 248 of 807; or 31. % Complete\n", - "+ Printing Diversion & Stream Summary 249 of 807; or 31. % Complete\n", - "+ Printing Diversion & Stream Summary 250 of 807; or 31. % Complete\n", - "+ Printing Diversion & Stream Summary 251 of 807; or 31. % Complete\n", - "+ Printing Diversion & Stream Summary 252 of 807; or 31. % Complete\n", - "+ Printing Diversion & Stream Summary 253 of 807; or 31. % Complete\n", - "+ Printing Diversion & Stream Summary 254 of 807; or 31. % Complete\n", - "+ Printing Diversion & Stream Summary 255 of 807; or 32. % Complete\n", - "+ Printing Diversion & Stream Summary 256 of 807; or 32. % Complete\n", - "+ Printing Diversion & Stream Summary 257 of 807; or 32. % Complete\n", - "+ Printing Diversion & Stream Summary 258 of 807; or 32. % Complete\n", - "+ Printing Diversion & Stream Summary 259 of 807; or 32. % Complete\n", - "+ Printing Diversion & Stream Summary 260 of 807; or 32. % Complete\n", - "+ Printing Diversion & Stream Summary 261 of 807; or 32. % Complete\n", - "+ Printing Diversion & Stream Summary 262 of 807; or 32. % Complete\n", - "+ Printing Diversion & Stream Summary 263 of 807; or 33. % Complete\n", - "+ Printing Diversion & Stream Summary 264 of 807; or 33. % Complete\n", - "+ Printing Diversion & Stream Summary 265 of 807; or 33. % Complete\n", - "+ Printing Diversion & Stream Summary 266 of 807; or 33. % Complete\n", - "+ Printing Diversion & Stream Summary 267 of 807; or 33. % Complete\n", - "+ Printing Diversion & Stream Summary 268 of 807; or 33. % Complete\n", - "+ Printing Diversion & Stream Summary 269 of 807; or 33. % Complete\n", - "+ Printing Diversion & Stream Summary 270 of 807; or 33. % Complete\n", - "+ Printing Diversion & Stream Summary 271 of 807; or 34. % Complete\n", - "+ Printing Diversion & Stream Summary 272 of 807; or 34. % Complete\n", - "+ Printing Diversion & Stream Summary 273 of 807; or 34. % 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Stream Summary 300 of 807; or 37. % Complete\n", - "+ Printing Diversion & Stream Summary 301 of 807; or 37. % Complete\n", - "+ Printing Diversion & Stream Summary 302 of 807; or 37. % Complete\n", - "+ Printing Diversion & Stream Summary 303 of 807; or 38. % Complete\n", - "+ Printing Diversion & Stream Summary 304 of 807; or 38. % Complete\n", - "+ Printing Diversion & Stream Summary 305 of 807; or 38. % Complete\n", - "+ Printing Diversion & Stream Summary 306 of 807; or 38. % Complete\n", - "+ Printing Diversion & Stream Summary 307 of 807; or 38. % Complete\n", - "+ Printing Diversion & Stream Summary 308 of 807; or 38. % Complete\n", - "+ Printing Diversion & Stream Summary 309 of 807; or 38. % Complete\n", - "+ Printing Diversion & Stream Summary 310 of 807; or 38. % Complete\n", - "+ Printing Diversion & Stream Summary 311 of 807; or 39. % Complete\n", - "+ Printing Diversion & Stream Summary 312 of 807; or 39. % Complete\n", - "+ Printing Diversion & Stream Summary 313 of 807; or 39. % Complete\n", - "+ Printing Diversion & Stream Summary 314 of 807; or 39. % Complete\n", - "+ Printing Diversion & Stream Summary 315 of 807; or 39. % Complete\n", - "+ Printing Diversion & Stream Summary 316 of 807; or 39. % Complete\n", - "+ Printing Diversion & Stream Summary 317 of 807; or 39. % Complete\n", - "+ Printing Diversion & Stream Summary 318 of 807; or 39. % Complete\n", - "+ Printing Diversion & Stream Summary 319 of 807; or 40. % Complete\n", - "+ Printing Diversion & Stream Summary 320 of 807; or 40. % Complete\n", - "+ Printing Diversion & Stream Summary 321 of 807; or 40. % Complete\n", - "+ Printing Diversion & Stream Summary 322 of 807; or 40. % Complete\n", - "+ Printing Diversion & Stream Summary 323 of 807; or 40. % Complete\n", - "+ Printing Diversion & Stream Summary 324 of 807; or 40. % Complete\n", - "+ Printing Diversion & Stream Summary 325 of 807; or 40. % Complete\n", - "+ Printing Diversion & Stream Summary 326 of 807; or 40. % 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Printing Diversion & Stream Summary 340 of 807; or 42. % Complete\n", - "+ Printing Diversion & Stream Summary 341 of 807; or 42. % Complete\n", - "+ Printing Diversion & Stream Summary 342 of 807; or 42. % Complete\n", - "+ Printing Diversion & Stream Summary 343 of 807; or 43. % Complete\n", - "+ Printing Diversion & Stream Summary 344 of 807; or 43. % Complete\n", - "+ Printing Diversion & Stream Summary 345 of 807; or 43. % Complete\n", - "+ Printing Diversion & Stream Summary 346 of 807; or 43. % Complete\n", - "+ Printing Diversion & Stream Summary 347 of 807; or 43. % Complete\n", - "+ Printing Diversion & Stream Summary 348 of 807; or 43. % Complete\n", - "+ Printing Diversion & Stream Summary 349 of 807; or 43. % Complete\n", - "+ Printing Diversion & Stream Summary 350 of 807; or 43. % Complete\n", - "+ Printing Diversion & Stream Summary 351 of 807; or 43. % Complete\n", - "+ Printing Diversion & Stream Summary 352 of 807; or 44. % Complete\n", - "+ Printing Diversion & 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807; or 45. % Complete\n", - "+ Printing Diversion & Stream Summary 367 of 807; or 45. % Complete\n", - "+ Printing Diversion & Stream Summary 368 of 807; or 46. % Complete\n", - "+ Printing Diversion & Stream Summary 369 of 807; or 46. % Complete\n", - "+ Printing Diversion & Stream Summary 370 of 807; or 46. % Complete\n", - "+ Printing Diversion & Stream Summary 371 of 807; or 46. % Complete\n", - "+ Printing Diversion & Stream Summary 372 of 807; or 46. % Complete\n", - "+ Printing Diversion & Stream Summary 373 of 807; or 46. % Complete\n", - "+ Printing Diversion & Stream Summary 374 of 807; or 46. % Complete\n", - "+ Printing Diversion & Stream Summary 375 of 807; or 46. % Complete\n", - "+ Printing Diversion & Stream Summary 376 of 807; or 47. % Complete\n", - "+ Printing Diversion & Stream Summary 377 of 807; or 47. % Complete\n", - "+ Printing Diversion & Stream Summary 378 of 807; or 47. % Complete\n", - "+ Printing Diversion & Stream Summary 379 of 807; or 47. % 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807; or 65. % Complete\n", - "+ Printing Diversion & Stream Summary 526 of 807; or 65. % Complete\n", - "+ Printing Diversion & Stream Summary 527 of 807; or 65. % Complete\n", - "+ Printing Diversion & Stream Summary 528 of 807; or 65. % Complete\n", - "+ Printing Diversion & Stream Summary 529 of 807; or 66. % Complete\n", - "+ Printing Diversion & Stream Summary 530 of 807; or 66. % Complete\n", - "+ Printing Diversion & Stream Summary 531 of 807; or 66. % Complete\n", - "+ Printing Diversion & Stream Summary 532 of 807; or 66. % Complete\n", - "+ Printing Diversion & Stream Summary 533 of 807; or 66. % Complete\n", - "+ Printing Diversion & Stream Summary 534 of 807; or 66. % Complete\n", - "+ Printing Diversion & Stream Summary 535 of 807; or 66. % Complete\n", - "+ Printing Diversion & Stream Summary 536 of 807; or 66. % Complete\n", - "+ Printing Diversion & Stream Summary 537 of 807; or 67. % Complete\n", - "+ Printing Diversion & Stream Summary 538 of 807; or 67. % 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Printing Diversion & Stream Summary 552 of 807; or 68. % Complete\n", - "+ Printing Diversion & Stream Summary 553 of 807; or 69. % Complete\n", - "+ Printing Diversion & Stream Summary 554 of 807; or 69. % Complete\n", - "+ Printing Diversion & Stream Summary 555 of 807; or 69. % Complete\n", - "+ Printing Diversion & Stream Summary 556 of 807; or 69. % Complete\n", - "+ Printing Diversion & Stream Summary 557 of 807; or 69. % Complete\n", - "+ Printing Diversion & Stream Summary 558 of 807; or 69. % Complete\n", - "+ Printing Diversion & Stream Summary 559 of 807; or 69. % Complete\n", - "+ Printing Diversion & Stream Summary 560 of 807; or 69. % Complete\n", - "+ Printing Diversion & Stream Summary 561 of 807; or 70. % Complete\n", - "+ Printing Diversion & Stream Summary 562 of 807; or 70. % Complete\n", - "+ Printing Diversion & Stream Summary 563 of 807; or 70. % Complete\n", - "+ Printing Diversion & Stream Summary 564 of 807; or 70. % Complete\n", - "+ Printing Diversion & Stream Summary 565 of 807; or 70. % Complete\n", - "+ Printing Diversion & Stream Summary 566 of 807; or 70. % Complete\n", - "+ Printing Diversion & Stream Summary 567 of 807; or 70. % Complete\n", - "+ Printing Diversion & Stream Summary 568 of 807; or 70. % Complete\n", - "+ Printing Diversion & Stream Summary 569 of 807; or 71. % Complete\n", - "+ Printing Diversion & Stream Summary 570 of 807; or 71. % Complete\n", - "+ Printing Diversion & Stream Summary 571 of 807; or 71. % Complete\n", - "+ Printing Diversion & Stream Summary 572 of 807; or 71. % Complete\n", - "+ Printing Diversion & Stream Summary 573 of 807; or 71. % Complete\n", - "+ Printing Diversion & Stream Summary 574 of 807; or 71. % Complete\n", - "+ Printing Diversion & Stream Summary 575 of 807; or 71. % Complete\n", - "+ Printing Diversion & Stream Summary 576 of 807; or 71. % Complete\n", - "+ Printing Diversion & Stream Summary 577 of 807; or 71. % Complete\n", - "+ Printing Diversion & Stream Summary 578 of 807; or 72. % Complete\n", - "+ Printing Diversion & Stream Summary 579 of 807; or 72. % Complete\n", - "+ Printing Diversion & Stream Summary 580 of 807; or 72. % Complete\n", - "+ Printing Diversion & Stream Summary 581 of 807; or 72. % Complete\n", - "+ Printing Diversion & Stream Summary 582 of 807; or 72. % Complete\n", - "+ Printing Diversion & Stream Summary 583 of 807; or 72. % Complete\n", - "+ Printing Diversion & Stream Summary 584 of 807; or 72. % Complete\n", - "+ Printing Diversion & Stream Summary 585 of 807; or 72. % Complete\n", - "+ Printing Diversion & Stream Summary 586 of 807; or 73. % Complete\n", - "+ Printing Diversion & Stream Summary 587 of 807; or 73. % Complete\n", - "+ Printing Diversion & Stream Summary 588 of 807; or 73. % Complete\n", - "+ Printing Diversion & Stream Summary 589 of 807; or 73. % Complete\n", - "+ Printing Diversion & Stream Summary 590 of 807; or 73. % Complete\n", - "+ Printing Diversion & Stream Summary 591 of 807; or 73. % 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Printing Diversion & Stream Summary 605 of 807; or 75. % Complete\n", - "+ Printing Diversion & Stream Summary 606 of 807; or 75. % Complete\n", - "+ Printing Diversion & Stream Summary 607 of 807; or 75. % Complete\n", - "+ Printing Diversion & Stream Summary 608 of 807; or 75. % Complete\n", - "+ Printing Diversion & Stream Summary 609 of 807; or 75. % Complete\n", - "+ Printing Diversion & Stream Summary 610 of 807; or 76. % Complete\n", - "+ Printing Diversion & Stream Summary 611 of 807; or 76. % Complete\n", - "+ Printing Diversion & Stream Summary 612 of 807; or 76. % Complete\n", - "+ Printing Diversion & Stream Summary 613 of 807; or 76. % Complete\n", - "+ Printing Diversion & Stream Summary 614 of 807; or 76. % Complete\n", - "+ Printing Diversion & Stream Summary 615 of 807; or 76. % Complete\n", - "+ Printing Diversion & Stream Summary 616 of 807; or 76. % Complete\n", - "+ Printing Diversion & Stream Summary 617 of 807; or 76. % Complete\n", - "+ Printing Diversion & 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(*.xss) 61 of 443; or 14. % Complete\n", - "+ Printing Structure Summary (*.xss) 62 of 443; or 14. % Complete\n", - "+ Printing Structure Summary (*.xss) 63 of 443; or 14. % Complete\n", - "+ Printing Structure Summary (*.xss) 64 of 443; or 15. % Complete\n", - "+ Printing Structure Summary (*.xss) 65 of 443; or 15. % Complete\n", - "+ Printing Structure Summary (*.xss) 66 of 443; or 15. % Complete\n", - "+ Printing Structure Summary (*.xss) 67 of 443; or 15. % Complete\n", - "+ Printing Structure Summary (*.xss) 68 of 443; or 16. % Complete\n", - "+ Printing Structure Summary (*.xss) 69 of 443; or 16. % Complete\n", - "+ Printing Structure Summary (*.xss) 70 of 443; or 16. % Complete\n", - "+ Printing Structure Summary (*.xss) 71 of 443; or 16. % Complete\n", - "+ Printing Structure Summary (*.xss) 72 of 443; or 16. % Complete\n", - "+ Printing Structure Summary (*.xss) 73 of 443; or 17. % Complete\n", - "+ Printing Structure Summary (*.xss) 74 of 443; or 17. % Complete\n", - "+ Printing Structure Summary (*.xss) 75 of 443; or 17. % Complete\n", - "+ Printing Structure Summary (*.xss) 76 of 443; or 17. % Complete\n", - "+ Printing Structure Summary (*.xss) 77 of 443; or 18. % Complete\n", - "+ Printing Structure Summary (*.xss) 78 of 443; or 18. % Complete\n", - "+ Printing Structure Summary (*.xss) 79 of 443; or 18. % Complete\n", - "+ Printing Structure Summary (*.xss) 80 of 443; or 18. % Complete\n", - "+ Printing Structure Summary (*.xss) 81 of 443; or 19. % Complete\n", - "+ Printing Structure Summary (*.xss) 82 of 443; or 19. % Complete\n", - "+ Printing Structure Summary (*.xss) 83 of 443; or 19. % Complete\n", - "+ Printing Structure Summary (*.xss) 84 of 443; or 19. % Complete\n", - "+ Printing Structure Summary (*.xss) 85 of 443; or 19. % Complete\n", - "+ Printing Structure Summary (*.xss) 86 of 443; or 20. % Complete\n", - "+ Printing Structure Summary (*.xss) 87 of 443; or 20. % Complete\n", - "+ Printing Structure Summary (*.xss) 88 of 443; or 20. % Complete\n", - "+ Printing Structure Summary (*.xss) 89 of 443; or 20. % Complete\n", - "+ Printing Structure Summary (*.xss) 90 of 443; or 21. % Complete\n", - "+ Printing Structure Summary (*.xss) 91 of 443; or 21. % Complete\n", - "+ Printing Structure Summary (*.xss) 92 of 443; or 21. % Complete\n", - "+ Printing Structure Summary (*.xss) 93 of 443; or 21. % Complete\n", - "+ Printing Structure Summary (*.xss) 94 of 443; or 21. % Complete\n", - "+ Printing Structure Summary (*.xss) 95 of 443; or 22. % Complete\n", - "+ Printing Structure Summary (*.xss) 96 of 443; or 22. % Complete\n", - "+ Printing Structure Summary (*.xss) 97 of 443; or 22. % Complete\n", - "+ Printing Structure Summary (*.xss) 98 of 443; or 22. % Complete\n", - "+ Printing Structure Summary (*.xss) 99 of 443; or 23. % Complete\n", - "+ Printing Structure Summary (*.xss) 100 of 443; or 23. % Complete\n", - "+ Printing Structure Summary (*.xss) 101 of 443; or 23. % Complete\n", - "+ Printing Structure Summary (*.xss) 102 of 443; or 23. % Complete\n", - "+ Printing Structure Summary (*.xss) 103 of 443; or 23. % Complete\n", - "+ Printing Structure Summary (*.xss) 104 of 443; or 24. % Complete\n", - "+ Printing Structure Summary (*.xss) 105 of 443; or 24. % Complete\n", - "+ Printing Structure Summary (*.xss) 106 of 443; or 24. % Complete\n", - "+ Printing Structure Summary (*.xss) 107 of 443; or 24. % Complete\n", - "+ Printing Structure Summary (*.xss) 108 of 443; or 25. % Complete\n", - "+ Printing Structure Summary (*.xss) 109 of 443; or 25. % Complete\n", - "+ Printing Structure Summary (*.xss) 110 of 443; or 25. % Complete\n", - "+ Printing Structure Summary (*.xss) 111 of 443; or 25. % Complete\n", - "+ Printing Structure Summary (*.xss) 112 of 443; or 26. % Complete\n", - "+ Printing Structure Summary (*.xss) 113 of 443; or 26. % Complete\n", - "+ Printing Structure Summary (*.xss) 114 of 443; or 26. % Complete\n", - "+ Printing Structure Summary (*.xss) 115 of 443; or 26. % Complete\n", - "+ Printing Structure Summary (*.xss) 116 of 443; or 26. % Complete\n", - "+ Printing Structure Summary (*.xss) 117 of 443; or 27. % Complete\n", - "+ Printing Structure Summary (*.xss) 118 of 443; or 27. % Complete\n", - "+ Printing Structure Summary (*.xss) 119 of 443; or 27. % Complete\n", - "+ Printing Structure Summary (*.xss) 120 of 443; or 27. % Complete\n", - "+ Printing Structure Summary (*.xss) 121 of 443; or 28. % Complete\n", - "+ Printing Structure Summary (*.xss) 122 of 443; or 28. % Complete\n", - "+ Printing Structure Summary (*.xss) 123 of 443; or 28. % Complete\n", - "+ Printing Structure Summary (*.xss) 124 of 443; or 28. % Complete\n", - "+ Printing Structure Summary (*.xss) 125 of 443; or 28. % Complete\n", - "+ Printing Structure Summary (*.xss) 126 of 443; or 29. % Complete\n", - "+ Printing Structure Summary (*.xss) 127 of 443; or 29. % Complete\n", - "+ Printing Structure Summary (*.xss) 128 of 443; or 29. % Complete\n", - "+ Printing Structure Summary (*.xss) 129 of 443; or 29. % Complete\n", - "+ Printing Structure Summary (*.xss) 130 of 443; or 30. % Complete\n", - "+ Printing Structure Summary (*.xss) 131 of 443; or 30. % Complete\n", - "+ Printing Structure Summary (*.xss) 132 of 443; or 30. % Complete\n", - "+ Printing Structure Summary (*.xss) 133 of 443; or 30. % Complete\n", - "+ Printing Structure Summary (*.xss) 134 of 443; or 30. % Complete\n", - "+ Printing Structure Summary (*.xss) 135 of 443; or 31. % Complete\n", - "+ Printing Structure Summary (*.xss) 136 of 443; or 31. % Complete\n", - "+ Printing Structure Summary (*.xss) 137 of 443; or 31. % Complete\n", - "+ Printing Structure Summary (*.xss) 138 of 443; or 31. % Complete\n", - "+ Printing Structure Summary (*.xss) 139 of 443; or 32. % Complete\n", - "+ Printing Structure Summary (*.xss) 140 of 443; or 32. % Complete\n", - "+ Printing Structure Summary (*.xss) 141 of 443; or 32. % Complete\n", - "+ Printing Structure Summary (*.xss) 142 of 443; or 32. % Complete\n", - "+ Printing Structure Summary (*.xss) 143 of 443; or 33. % Complete\n", - "+ Printing Structure Summary (*.xss) 144 of 443; or 33. % Complete\n", - "+ Printing Structure Summary (*.xss) 145 of 443; or 33. % Complete\n", - "+ Printing Structure Summary (*.xss) 146 of 443; or 33. % Complete\n", - "+ Printing Structure Summary (*.xss) 147 of 443; or 33. % Complete\n", - "+ Printing Structure Summary (*.xss) 148 of 443; or 34. % Complete\n", - "+ Printing Structure Summary (*.xss) 149 of 443; or 34. % Complete\n", - "+ Printing Structure Summary (*.xss) 150 of 443; or 34. % Complete\n", - "+ Printing Structure Summary (*.xss) 151 of 443; or 34. % Complete\n", - "+ Printing Structure Summary (*.xss) 152 of 443; or 35. % Complete\n", - "+ Printing Structure Summary (*.xss) 153 of 443; or 35. % Complete\n", - "+ Printing Structure Summary (*.xss) 154 of 443; or 35. % Complete\n", - "+ Printing Structure Summary (*.xss) 155 of 443; or 35. % Complete\n", - "+ Printing Structure Summary (*.xss) 156 of 443; or 35. % Complete\n", - "+ Printing Structure Summary (*.xss) 157 of 443; or 36. % Complete\n", - "+ Printing Structure Summary (*.xss) 158 of 443; or 36. % Complete\n", - "+ Printing Structure Summary (*.xss) 159 of 443; or 36. % Complete\n", - "+ Printing Structure Summary (*.xss) 160 of 443; or 36. % Complete\n", - "+ Printing Structure Summary (*.xss) 161 of 443; or 37. % Complete\n", - "+ Printing Structure Summary (*.xss) 162 of 443; or 37. % Complete\n", - "+ Printing Structure Summary (*.xss) 163 of 443; or 37. % Complete\n", - "+ Printing Structure Summary (*.xss) 164 of 443; or 37. % Complete\n", - "+ Printing Structure Summary (*.xss) 165 of 443; or 37. % Complete\n", - "+ Printing Structure Summary (*.xss) 166 of 443; or 38. % Complete\n", - "+ Printing Structure Summary (*.xss) 167 of 443; or 38. % Complete\n", - "+ Printing Structure Summary (*.xss) 168 of 443; or 38. % Complete\n", - "+ Printing Structure Summary (*.xss) 169 of 443; or 38. % Complete\n", - "+ Printing Structure Summary (*.xss) 170 of 443; or 39. % Complete\n", - "+ Printing Structure Summary (*.xss) 171 of 443; or 39. % Complete\n", - "+ Printing Structure Summary (*.xss) 172 of 443; or 39. % Complete\n", - "+ Printing Structure Summary (*.xss) 173 of 443; or 39. % Complete\n", - "+ Printing Structure Summary (*.xss) 174 of 443; or 40. % Complete\n", - "+ Printing Structure Summary (*.xss) 175 of 443; or 40. % Complete\n", - "+ Printing Structure Summary (*.xss) 176 of 443; or 40. % Complete\n", - "+ Printing Structure Summary (*.xss) 177 of 443; or 40. % Complete\n", - "+ Printing Structure Summary (*.xss) 178 of 443; or 40. % Complete\n", - "+ Printing Structure Summary (*.xss) 179 of 443; or 41. % Complete\n", - "+ Printing Structure Summary (*.xss) 180 of 443; or 41. % Complete\n", - "+ Printing Structure Summary (*.xss) 181 of 443; or 41. % Complete\n", - "+ Printing Structure Summary (*.xss) 182 of 443; or 41. % Complete\n", - "+ Printing Structure Summary (*.xss) 183 of 443; or 42. % Complete\n", - "+ Printing Structure Summary (*.xss) 184 of 443; or 42. % Complete\n", - "+ Printing Structure Summary (*.xss) 185 of 443; or 42. % Complete\n", - "+ Printing Structure Summary (*.xss) 186 of 443; or 42. % Complete\n", - "+ Printing Structure Summary (*.xss) 187 of 443; or 42. % Complete\n", - "+ Printing Structure Summary (*.xss) 188 of 443; or 43. % Complete\n", - "+ Printing Structure Summary (*.xss) 189 of 443; or 43. % Complete\n", - "+ Printing Structure Summary (*.xss) 190 of 443; or 43. % Complete\n", - "+ Printing Structure Summary (*.xss) 191 of 443; or 43. % Complete\n", - "+ Printing Structure Summary (*.xss) 192 of 443; or 44. % Complete\n", - "+ Printing Structure Summary (*.xss) 193 of 443; or 44. % Complete\n", - "+ Printing Structure Summary (*.xss) 194 of 443; or 44. % Complete\n", - "+ Printing Structure Summary (*.xss) 195 of 443; or 44. % Complete\n", - "+ Printing Structure Summary (*.xss) 196 of 443; or 44. % Complete\n", - "+ Printing Structure Summary (*.xss) 197 of 443; or 45. % Complete\n", - "+ Printing Structure Summary (*.xss) 198 of 443; or 45. % Complete\n", - "+ Printing Structure Summary (*.xss) 199 of 443; or 45. % Complete\n", - "+ Printing Structure Summary (*.xss) 200 of 443; or 45. % Complete\n", - "+ Printing Structure Summary (*.xss) 201 of 443; or 46. % Complete\n", - "+ Printing Structure Summary (*.xss) 202 of 443; or 46. % Complete\n", - "+ Printing Structure Summary (*.xss) 203 of 443; or 46. % Complete\n", - "+ Printing Structure Summary (*.xss) 204 of 443; or 46. % Complete\n", - "+ Printing Structure Summary (*.xss) 205 of 443; or 47. % Complete\n", - "+ Printing Structure Summary (*.xss) 206 of 443; or 47. % Complete\n", - "+ Printing Structure Summary (*.xss) 207 of 443; or 47. % Complete\n", - "+ Printing Structure Summary (*.xss) 208 of 443; or 47. % Complete\n", - "+ Printing Structure Summary (*.xss) 209 of 443; or 47. % Complete\n", - "+ Printing Structure Summary (*.xss) 210 of 443; or 48. % Complete\n", - "+ Printing Structure Summary (*.xss) 211 of 443; or 48. % Complete\n", - "+ Printing Structure Summary (*.xss) 212 of 443; or 48. % Complete\n", - "+ Printing Structure Summary (*.xss) 213 of 443; or 48. % Complete\n", - "+ Printing Structure Summary (*.xss) 214 of 443; or 49. % Complete\n", - "+ Printing Structure Summary (*.xss) 215 of 443; or 49. % Complete\n", - "+ Printing Structure Summary (*.xss) 216 of 443; or 49. % Complete\n", - "+ Printing Structure Summary (*.xss) 217 of 443; or 49. % Complete\n", - "+ Printing Structure Summary (*.xss) 218 of 443; or 49. % Complete\n", - "+ Printing Structure Summary (*.xss) 219 of 443; or 50. % Complete\n", - "+ Printing Structure Summary (*.xss) 220 of 443; or 50. % Complete\n", - "+ Printing Structure Summary (*.xss) 221 of 443; or 50. % Complete\n", - "+ Printing Structure Summary (*.xss) 222 of 443; or 50. % Complete\n", - "+ Printing Structure Summary (*.xss) 223 of 443; or 51. % Complete\n", - "+ Printing Structure Summary (*.xss) 224 of 443; or 51. % Complete\n", - "+ Printing Structure Summary (*.xss) 225 of 443; or 51. % Complete\n", - "+ Printing Structure Summary (*.xss) 226 of 443; or 51. % Complete\n", - "+ Printing Structure Summary (*.xss) 227 of 443; or 51. % Complete\n", - "+ Printing Structure Summary (*.xss) 228 of 443; or 52. % Complete\n", - "+ Printing Structure Summary (*.xss) 229 of 443; or 52. % Complete\n", - "+ Printing Structure Summary (*.xss) 230 of 443; or 52. % Complete\n", - "+ Printing Structure Summary (*.xss) 231 of 443; or 52. % Complete\n", - "+ Printing Structure Summary (*.xss) 232 of 443; or 53. % Complete\n", - "+ Printing Structure Summary (*.xss) 233 of 443; or 53. % Complete\n", - "+ Printing Structure Summary (*.xss) 234 of 443; or 53. % Complete\n", - "+ Printing Structure Summary (*.xss) 235 of 443; or 53. % Complete\n", - "+ Printing Structure Summary (*.xss) 236 of 443; or 53. % Complete\n", - "+ Printing Structure Summary (*.xss) 237 of 443; or 54. % Complete\n", - "+ Printing Structure Summary (*.xss) 238 of 443; or 54. % Complete\n", - "+ Printing Structure Summary (*.xss) 239 of 443; or 54. % Complete\n", - "+ Printing Structure Summary (*.xss) 240 of 443; or 54. % Complete\n", - "+ Printing Structure Summary (*.xss) 241 of 443; or 55. % Complete\n", - "+ Printing Structure Summary (*.xss) 242 of 443; or 55. % Complete\n", - "+ Printing Structure Summary (*.xss) 243 of 443; or 55. % Complete\n", - "+ Printing Structure Summary (*.xss) 244 of 443; or 55. % Complete\n", - "+ Printing Structure Summary (*.xss) 245 of 443; or 56. % Complete\n", - "+ Printing Structure Summary (*.xss) 246 of 443; or 56. % Complete\n", - "+ Printing Structure Summary (*.xss) 247 of 443; or 56. % Complete\n", - "+ Printing Structure Summary (*.xss) 248 of 443; or 56. % Complete\n", - "+ Printing Structure Summary (*.xss) 249 of 443; or 56. % Complete\n", - "+ Printing Structure Summary (*.xss) 250 of 443; or 57. % Complete\n", - "+ Printing Structure Summary (*.xss) 251 of 443; or 57. % Complete\n", - "+ Printing Structure Summary (*.xss) 252 of 443; or 57. % Complete\n", - "+ Printing Structure Summary (*.xss) 253 of 443; or 57. % Complete\n", - "+ Printing Structure Summary (*.xss) 254 of 443; or 58. % Complete\n", - "+ Printing Structure Summary (*.xss) 255 of 443; or 58. % Complete\n", - "+ Printing Structure Summary (*.xss) 256 of 443; or 58. % Complete\n", - "+ Printing Structure Summary (*.xss) 257 of 443; or 58. % Complete\n", - "+ Printing Structure Summary (*.xss) 258 of 443; or 58. % Complete\n", - "+ Printing Structure Summary (*.xss) 259 of 443; or 59. % Complete\n", - "+ Printing Structure Summary (*.xss) 260 of 443; or 59. % Complete\n", - "+ Printing Structure Summary (*.xss) 261 of 443; or 59. % Complete\n", - "+ Printing Structure Summary (*.xss) 262 of 443; or 59. % Complete\n", - "+ Printing Structure Summary (*.xss) 263 of 443; or 60. % Complete\n", - "+ Printing Structure Summary (*.xss) 264 of 443; or 60. % Complete\n", - "+ Printing Structure Summary (*.xss) 265 of 443; or 60. % Complete\n", - "+ Printing Structure Summary (*.xss) 266 of 443; or 60. % Complete\n", - "+ Printing Structure Summary (*.xss) 267 of 443; or 60. % Complete\n", - "+ Printing Structure Summary (*.xss) 268 of 443; or 61. % Complete\n", - "+ Printing Structure Summary (*.xss) 269 of 443; or 61. % Complete\n", - "+ Printing Structure Summary (*.xss) 270 of 443; or 61. % Complete\n", - "+ Printing Structure Summary (*.xss) 271 of 443; or 61. % Complete\n", - "+ Printing Structure Summary (*.xss) 272 of 443; or 62. % Complete\n", - "+ Printing Structure Summary (*.xss) 273 of 443; or 62. % Complete\n", - "+ Printing Structure Summary (*.xss) 274 of 443; or 62. % Complete\n", - "+ Printing Structure Summary (*.xss) 275 of 443; or 62. % Complete\n", - "+ Printing Structure Summary (*.xss) 276 of 443; or 63. % Complete\n", - "+ Printing Structure Summary (*.xss) 277 of 443; or 63. % Complete\n", - "+ Printing Structure Summary (*.xss) 278 of 443; or 63. % Complete\n", - "+ Printing Structure Summary (*.xss) 279 of 443; or 63. % Complete\n", - "+ Printing Structure Summary (*.xss) 280 of 443; or 63. % Complete\n", - "+ Printing Structure Summary (*.xss) 281 of 443; or 64. % Complete\n", - "+ Printing Structure Summary (*.xss) 282 of 443; or 64. % Complete\n", - "+ Printing Structure Summary (*.xss) 283 of 443; or 64. % Complete\n", - "+ Printing Structure Summary (*.xss) 284 of 443; or 64. % Complete\n", - "+ Printing Structure Summary (*.xss) 285 of 443; or 65. % Complete\n", - "+ Printing Structure Summary (*.xss) 286 of 443; or 65. % Complete\n", - "+ Printing Structure Summary (*.xss) 287 of 443; or 65. % Complete\n", - "+ Printing Structure Summary (*.xss) 288 of 443; or 65. % Complete\n", - "+ Printing Structure Summary (*.xss) 289 of 443; or 65. % Complete\n", - "+ Printing Structure Summary (*.xss) 290 of 443; or 66. % Complete\n", - "+ Printing Structure Summary (*.xss) 291 of 443; or 66. % Complete\n", - "+ Printing Structure Summary (*.xss) 292 of 443; or 66. % Complete\n", - "+ Printing Structure Summary (*.xss) 293 of 443; or 66. % Complete\n", - "+ Printing Structure Summary (*.xss) 294 of 443; or 67. % Complete\n", - "+ Printing Structure Summary (*.xss) 295 of 443; or 67. % Complete\n", - "+ Printing Structure Summary (*.xss) 296 of 443; or 67. % Complete\n", - "+ Printing Structure Summary (*.xss) 297 of 443; or 67. % Complete\n", - "+ Printing Structure Summary (*.xss) 298 of 443; or 67. % Complete\n", - "+ Printing Structure Summary (*.xss) 299 of 443; or 68. % Complete\n", - "+ Printing Structure Summary (*.xss) 300 of 443; or 68. % Complete\n", - "+ Printing Structure Summary (*.xss) 301 of 443; or 68. % Complete\n", - "+ Printing Structure Summary (*.xss) 302 of 443; or 68. % Complete\n", - "+ Printing Structure Summary (*.xss) 303 of 443; or 69. % Complete\n", - "+ Printing Structure Summary (*.xss) 304 of 443; or 69. % Complete\n", - "+ Printing Structure Summary (*.xss) 305 of 443; or 69. % Complete\n", - "+ Printing Structure Summary (*.xss) 306 of 443; or 69. % Complete\n", - "+ Printing Structure Summary (*.xss) 307 of 443; or 70. % Complete\n", - "+ Printing Structure Summary (*.xss) 308 of 443; or 70. % Complete\n", - "+ Printing Structure Summary (*.xss) 309 of 443; or 70. % Complete\n", - "+ Printing Structure Summary (*.xss) 310 of 443; or 70. % Complete\n", - "+ Printing Structure Summary (*.xss) 311 of 443; or 70. % Complete\n", - "+ Printing Structure Summary (*.xss) 312 of 443; or 71. % Complete\n", - "+ Printing Structure Summary (*.xss) 313 of 443; or 71. % Complete\n", - "+ Printing Structure Summary (*.xss) 314 of 443; or 71. % Complete\n", - "+ Printing Structure Summary (*.xss) 315 of 443; or 71. % Complete\n", - "+ Printing Structure Summary (*.xss) 316 of 443; or 72. % Complete\n", - "+ Printing Structure Summary (*.xss) 317 of 443; or 72. % Complete\n", - "+ Printing Structure Summary (*.xss) 318 of 443; or 72. % Complete\n", - "+ Printing Structure Summary (*.xss) 319 of 443; or 72. % Complete\n", - "+ Printing Structure Summary (*.xss) 320 of 443; or 72. % Complete\n", - "+ Printing Structure Summary (*.xss) 321 of 443; or 73. % Complete\n", - "+ Printing Structure Summary (*.xss) 322 of 443; or 73. % Complete\n", - "+ Printing Structure Summary (*.xss) 323 of 443; or 73. % Complete\n", - "+ Printing Structure Summary (*.xss) 324 of 443; or 73. % Complete\n", - "+ Printing Structure Summary (*.xss) 325 of 443; or 74. % Complete\n", - "+ Printing Structure Summary (*.xss) 326 of 443; or 74. % Complete\n", - "+ Printing Structure Summary (*.xss) 327 of 443; or 74. % Complete\n", - "+ Printing Structure Summary (*.xss) 328 of 443; or 74. % Complete\n", - "+ Printing Structure Summary (*.xss) 329 of 443; or 74. % Complete\n", - "+ Printing Structure Summary (*.xss) 330 of 443; or 75. % Complete\n", - "+ Printing Structure Summary (*.xss) 331 of 443; or 75. % Complete\n", - "+ Printing Structure Summary (*.xss) 332 of 443; or 75. % Complete\n", - "+ Printing Structure Summary (*.xss) 333 of 443; or 75. % Complete\n", - "+ Printing Structure Summary (*.xss) 334 of 443; or 76. % Complete\n", - "+ Printing Structure Summary (*.xss) 335 of 443; or 76. % Complete\n", - "+ Printing Structure Summary (*.xss) 336 of 443; or 76. % Complete\n", - "+ Printing Structure Summary (*.xss) 337 of 443; or 76. % Complete\n", - "+ Printing Structure Summary (*.xss) 338 of 443; or 77. % Complete\n", - "+ Printing Structure Summary (*.xss) 339 of 443; or 77. % Complete\n", - "+ Printing Structure Summary (*.xss) 340 of 443; or 77. % Complete\n", - "+ Printing Structure Summary (*.xss) 341 of 443; or 77. % Complete\n", - "+ Printing Structure Summary (*.xss) 342 of 443; or 77. % Complete\n", - "+ Printing Structure Summary (*.xss) 343 of 443; or 78. % Complete\n", - "+ Printing Structure Summary (*.xss) 344 of 443; or 78. % Complete\n", - "+ Printing Structure Summary (*.xss) 345 of 443; or 78. % Complete\n", - "+ Printing Structure Summary (*.xss) 346 of 443; or 78. % Complete\n", - "+ Printing Structure Summary (*.xss) 347 of 443; or 79. % Complete\n", - "+ Printing Structure Summary (*.xss) 348 of 443; or 79. % Complete\n", - "+ Printing Structure Summary (*.xss) 349 of 443; or 79. % Complete\n", - "+ Printing Structure Summary (*.xss) 350 of 443; or 79. % Complete\n", - "+ Printing Structure Summary (*.xss) 351 of 443; or 79. % Complete\n", - "+ Printing Structure Summary (*.xss) 352 of 443; or 80. % Complete\n", - "+ Printing Structure Summary (*.xss) 353 of 443; or 80. % Complete\n", - "+ Printing Structure Summary (*.xss) 354 of 443; or 80. % Complete\n", - "+ Printing Structure Summary (*.xss) 355 of 443; or 80. % Complete\n", - "+ Printing Structure Summary (*.xss) 356 of 443; or 81. % Complete\n", - "+ Printing Structure Summary (*.xss) 357 of 443; or 81. % Complete\n", - "+ Printing Structure Summary (*.xss) 358 of 443; or 81. % Complete\n", - "+ Printing Structure Summary (*.xss) 359 of 443; or 81. % Complete\n", - "+ Printing Structure Summary (*.xss) 360 of 443; or 81. % Complete\n", - "+ Printing Structure Summary (*.xss) 361 of 443; or 82. % Complete\n", - "+ Printing Structure Summary (*.xss) 362 of 443; or 82. % Complete\n", - "+ Printing Structure Summary (*.xss) 363 of 443; or 82. % Complete\n", - "+ Printing Structure Summary (*.xss) 364 of 443; or 82. % Complete\n", - "+ Printing Structure Summary (*.xss) 365 of 443; or 83. % Complete\n", - "+ Printing Structure Summary (*.xss) 366 of 443; or 83. % Complete\n", - "+ Printing Structure Summary (*.xss) 367 of 443; or 83. % Complete\n", - "+ Printing Structure Summary (*.xss) 368 of 443; or 83. % Complete\n", - "+ Printing Structure Summary (*.xss) 369 of 443; or 84. % Complete\n", - "+ Printing Structure Summary (*.xss) 370 of 443; or 84. % Complete\n", - "+ Printing Structure Summary (*.xss) 371 of 443; or 84. % Complete\n", - "+ Printing Structure Summary (*.xss) 372 of 443; or 84. % Complete\n", - "+ Printing Structure Summary (*.xss) 373 of 443; or 84. % Complete\n", - "+ Printing Structure Summary (*.xss) 374 of 443; or 85. % Complete\n", - "+ Printing Structure Summary (*.xss) 375 of 443; or 85. % Complete\n", - "+ Printing Structure Summary (*.xss) 376 of 443; or 85. % Complete\n", - "+ Printing Structure Summary (*.xss) 377 of 443; or 85. % Complete\n", - "+ Printing Structure Summary (*.xss) 378 of 443; or 86. % Complete\n", - "+ Printing Structure Summary (*.xss) 379 of 443; or 86. % Complete\n", - "+ Printing Structure Summary (*.xss) 380 of 443; or 86. % Complete\n", - "+ Printing Structure Summary (*.xss) 381 of 443; or 86. % Complete\n", - "+ Printing Structure Summary (*.xss) 382 of 443; or 86. % Complete\n", - "+ Printing Structure Summary (*.xss) 383 of 443; or 87. % Complete\n", - "+ Printing Structure Summary (*.xss) 384 of 443; or 87. % Complete\n", - "+ Printing Structure Summary (*.xss) 385 of 443; or 87. % Complete\n", - "+ Printing Structure Summary (*.xss) 386 of 443; or 87. % Complete\n", - "+ Printing Structure Summary (*.xss) 387 of 443; or 88. % Complete\n", - "+ Printing Structure Summary (*.xss) 388 of 443; or 88. % Complete\n", - "+ Printing Structure Summary (*.xss) 389 of 443; or 88. % Complete\n", - "+ Printing Structure Summary (*.xss) 390 of 443; or 88. % Complete\n", - "+ Printing Structure Summary (*.xss) 391 of 443; or 88. % Complete\n", - "+ Printing Structure Summary (*.xss) 392 of 443; or 89. % Complete\n", - "+ Printing Structure Summary (*.xss) 393 of 443; or 89. % Complete\n", - "+ Printing Structure Summary (*.xss) 394 of 443; or 89. % Complete\n", - "+ Printing Structure Summary (*.xss) 395 of 443; or 89. % Complete\n", - "+ Printing Structure Summary (*.xss) 396 of 443; or 90. % Complete\n", - "+ Printing Structure Summary (*.xss) 397 of 443; or 90. % Complete\n", - "+ Printing Structure Summary (*.xss) 398 of 443; or 90. % Complete\n", - "+ Printing Structure Summary (*.xss) 399 of 443; or 90. % Complete\n", - "+ Printing Structure Summary (*.xss) 400 of 443; or 91. % Complete\n", - "+ Printing Structure Summary (*.xss) 401 of 443; or 91. % Complete\n", - "+ Printing Structure Summary (*.xss) 402 of 443; or 91. % Complete\n", - "+ Printing Structure Summary (*.xss) 403 of 443; or 91. % Complete\n", - "+ Printing Structure Summary (*.xss) 404 of 443; or 91. % Complete\n", - "+ Printing Structure Summary (*.xss) 405 of 443; or 92. % Complete\n", - "+ Printing Structure Summary (*.xss) 406 of 443; or 92. % Complete\n", - "+ Printing Structure Summary (*.xss) 407 of 443; or 92. % Complete\n", - "+ Printing Structure Summary (*.xss) 408 of 443; or 92. % Complete\n", - "+ Printing Structure Summary (*.xss) 409 of 443; or 93. % Complete\n", - "+ Printing Structure Summary (*.xss) 410 of 443; or 93. % Complete\n", - "+ Printing Structure Summary (*.xss) 411 of 443; or 93. % Complete\n", - "+ Printing Structure Summary (*.xss) 412 of 443; or 93. % Complete\n", - "+ Printing Structure Summary (*.xss) 413 of 443; or 93. % Complete\n", - "+ Printing Structure Summary (*.xss) 414 of 443; or 94. % Complete\n", - "+ Printing Structure Summary (*.xss) 415 of 443; or 94. % Complete\n", - "+ Printing Structure Summary (*.xss) 416 of 443; or 94. % Complete\n", - "+ Printing Structure Summary (*.xss) 417 of 443; or 94. % Complete\n", - "+ Printing Structure Summary (*.xss) 418 of 443; or 95. % Complete\n", - "+ Printing Structure Summary (*.xss) 419 of 443; or 95. % Complete\n", - "+ Printing Structure Summary (*.xss) 420 of 443; or 95. % Complete\n", - "+ Printing Structure Summary (*.xss) 421 of 443; or 95. % Complete\n", - "+ Printing Structure Summary (*.xss) 422 of 443; or 95. % Complete\n", - "+ Printing Structure Summary (*.xss) 423 of 443; or 96. % Complete\n", - "+ Printing Structure Summary (*.xss) 424 of 443; or 96. % Complete\n", - "+ Printing Structure Summary (*.xss) 425 of 443; or 96. % Complete\n", - "+ Printing Structure Summary (*.xss) 426 of 443; or 96. % Complete\n", - "+ Printing Structure Summary (*.xss) 427 of 443; or 97. % Complete\n", - "+ Printing Structure Summary (*.xss) 428 of 443; or 97. % Complete\n", - "+ Printing Structure Summary (*.xss) 429 of 443; or 97. % Complete\n", - "+ Printing Structure Summary (*.xss) 430 of 443; or 97. % Complete\n", - "+ Printing Structure Summary (*.xss) 431 of 443; or 98. % Complete\n", - "+ Printing Structure Summary (*.xss) 432 of 443; or 98. % Complete\n", - "+ Printing Structure Summary (*.xss) 433 of 443; or 98. % Complete\n", - "+ Printing Structure Summary (*.xss) 434 of 443; or 98. % Complete\n", - "+ Printing Structure Summary (*.xss) 435 of 443; or 98. % Complete\n", - "+ Printing Structure Summary (*.xss) 436 of 443; or 99. % Complete\n", - "+ Printing Structure Summary (*.xss) 437 of 443; or 99. % Complete\n", - "+ Printing Structure Summary (*.xss) 438 of 443; or 99. % Complete\n", - "+ Printing Structure Summary (*.xss) 439 of 443; or 99. % Complete\n", - "+ Printing Structure Summary (*.xss) 440 of 443; or 100. % Complete\n", - "+ Printing Structure Summary (*.xss) 441 of 443; or 100. % Complete\n", - "+ Printing Structure Summary (*.xss) 442 of 443; or 100. % Complete\n", - "\n", - "________________________________________________________________________\n", - " Subroutine Outifr\n", - "\n", - "________________________________________________________________________\n", - " Subroutine OutPln \n", - "\n", - "________________________________________________________________________\n", - " Execut; Successful Run output files are:\n", - " \n", - " Diversion output: *.xdd\n", - " Reservoir output: *.xre\n", - " Operating Rule Info: *.xop\n", - " Instream Reach Info: *.xir\n", - " Structure Summary: *.xss\n", - " Call (Control) Summary: *.xca\n", - " Plan Output: *.xpl\n", - " Replacement Reesrvoir Output: *.xrp\n", - "\n", - "________________________________________________________________________\n", - " Execut; Successful Termination\n", - " Statem; See detailed messages in file: cm2015B_S0_1.log \n", - " Stop 0\n", - "creating parquet for S0_1\n" + "+ Execut; Year 1921 Month NOV Day 1 Reoperation 1 Annual Total 1\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:00<00:00, 56.70it/s]\n" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 41\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRunning: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mscenario\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 39\u001b[0m os\u001b[38;5;241m.\u001b[39mchdir(simulated_scenario_dir)\n\u001b[0;32m---> 41\u001b[0m \u001b[43msubprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mstatemod_exe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msimulated_basin_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m-simulate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;66;03m#Save output to parquet files \u001b[39;00m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcreating parquet for \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m scenario)\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/subprocess.py:347\u001b[0m, in \u001b[0;36mcall\u001b[0;34m(timeout, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m 345\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Popen(\u001b[38;5;241m*\u001b[39mpopenargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mas\u001b[39;00m p:\n\u001b[1;32m 346\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 347\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m: \u001b[38;5;66;03m# Including KeyboardInterrupt, wait handled that.\u001b[39;00m\n\u001b[1;32m 349\u001b[0m p\u001b[38;5;241m.\u001b[39mkill()\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/subprocess.py:1209\u001b[0m, in \u001b[0;36mPopen.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 1207\u001b[0m endtime \u001b[38;5;241m=\u001b[39m _time() \u001b[38;5;241m+\u001b[39m timeout\n\u001b[1;32m 1208\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1209\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1210\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m 1211\u001b[0m \u001b[38;5;66;03m# https://bugs.python.org/issue25942\u001b[39;00m\n\u001b[1;32m 1212\u001b[0m \u001b[38;5;66;03m# The first keyboard interrupt waits briefly for the child to\u001b[39;00m\n\u001b[1;32m 1213\u001b[0m \u001b[38;5;66;03m# exit under the common assumption that it also received the ^C\u001b[39;00m\n\u001b[1;32m 1214\u001b[0m \u001b[38;5;66;03m# generated SIGINT and will exit rapidly.\u001b[39;00m\n\u001b[1;32m 1215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/subprocess.py:1943\u001b[0m, in \u001b[0;36mPopen._wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 1941\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturncode \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1942\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m \u001b[38;5;66;03m# Another thread waited.\u001b[39;00m\n\u001b[0;32m-> 1943\u001b[0m (pid, sts) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_try_wait\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1944\u001b[0m \u001b[38;5;66;03m# Check the pid and loop as waitpid has been known to\u001b[39;00m\n\u001b[1;32m 1945\u001b[0m \u001b[38;5;66;03m# return 0 even without WNOHANG in odd situations.\u001b[39;00m\n\u001b[1;32m 1946\u001b[0m \u001b[38;5;66;03m# http://bugs.python.org/issue14396.\u001b[39;00m\n\u001b[1;32m 1947\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pid \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpid:\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/subprocess.py:1901\u001b[0m, in \u001b[0;36mPopen._try_wait\u001b[0;34m(self, wait_flags)\u001b[0m\n\u001b[1;32m 1899\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"All callers to this function MUST hold self._waitpid_lock.\"\"\"\u001b[39;00m\n\u001b[1;32m 1900\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1901\u001b[0m (pid, sts) \u001b[38;5;241m=\u001b[39m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwaitpid\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwait_flags\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1902\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mChildProcessError\u001b[39;00m:\n\u001b[1;32m 1903\u001b[0m \u001b[38;5;66;03m# This happens if SIGCLD is set to be ignored or waiting\u001b[39;00m\n\u001b[1;32m 1904\u001b[0m \u001b[38;5;66;03m# for child processes has otherwise been disabled for our\u001b[39;00m\n\u001b[1;32m 1905\u001b[0m \u001b[38;5;66;03m# process. This child is dead, we can't get the status.\u001b[39;00m\n\u001b[1;32m 1906\u001b[0m pid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpid\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -3016,35 +511,33 @@ "\n", "# read RSP template\n", "with open(multi_template_file) as template_obj:\n", + " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", + " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - "\n", + " \n", " # dictionary holding search keys and replacement values to update the template file\n", - " d = {\n", - " \"EVA\": f\"../../input_files/cm2015B_{scenario}.eva\",\n", - " \"DDM\": f\"../../input_files/cm2015B_{scenario}.ddm\",\n", - " \"DDR\": f\"../../input_files/cm2015B_{scenario}.ddr\",\n", - " }\n", - "\n", + " d = {\"EVA\": f\"../../input_files/cm2015B_{scenario}.eva\",\"DDM\": f\"../../input_files/cm2015B_{scenario}.ddm\",\"DDR\": f\"../../input_files/cm2015B_{scenario}.ddr\"}\n", + " \n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - "\n", + " \n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - "\n", + " \n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"cm2015B_{scenario}.rsp\")\n", - "\n", + " \n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - "\n", + " \n", " # construct simulated basin path\n", " simulated_basin_path = f\"cm2015B_{scenario}\"\n", "\n", @@ -3053,22 +546,16 @@ " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - "\n", - " # Save output to parquet files\n", - " print(\"creating parquet for \" + scenario)\n", - "\n", - " output_directory = os.path.join(parquet_dir + \"/scenario/\" + scenario)\n", - "\n", + " \n", + " #Save output to parquet files \n", + " print('creating parquet for ' + scenario)\n", + " \n", + " output_directory = os.path.join(parquet_dir+ \"/scenario/\"+ scenario)\n", + " \n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - "\n", - " stm.xdd.convert_xdd(\n", - " output_path=output_directory,\n", - " allow_overwrite=False,\n", - " xdd_files=scenarios_dir + \"/\" + scenario + \"/cm2015B_\" + scenario + \".xdd\",\n", - " id_subset=[\"3601008\"],\n", - " parallel_jobs=2,\n", - " )" + " \n", + " stm.xdd.convert_xdd(output_path=output_directory,allow_overwrite=False,xdd_files=scenarios_dir + \"/\"+ scenario + \"/cm2015B_\"+scenario+\".xdd\",id_subset=['3601008'],parallel_jobs=2)\n" ] }, { From f2d72cd9e1d2d9a2d17e9b8af84089988f912492 Mon Sep 17 00:00:00 2001 From: crvernon Date: Wed, 10 Apr 2024 13:58:39 -0400 Subject: [PATCH 11/12] format notebooks for docs --- docs/source/getting-started/quickstarter.rst | 7 + ...1_Demand_WaterRights_File_Modification.rst | 1055 +++++++++++++++++ .../N2_Evaporation_File_Modification.rst | 592 +++++++++ .../N3_Reservoir_File_Modification.rst | 519 ++++++++ .../N4_Streamflow_File_Modification.rst | 450 +++++++ .../notebooks/N5_Batch_Modification.rst | 273 +++++ docs/source/notebooks/output_12_1.png | Bin 0 -> 24108 bytes docs/source/notebooks/output_13_1.png | Bin 0 -> 48244 bytes docs/source/notebooks/output_15_0.png | Bin 0 -> 130888 bytes docs/source/notebooks/output_15_1.png | Bin 0 -> 21936 bytes docs/source/notebooks/output_24_0.png | Bin 0 -> 81287 bytes docs/source/notebooks/output_25_2.png | Bin 0 -> 22269 bytes docs/source/notebooks/output_26_1.png | Bin 0 -> 85078 bytes docs/source/notebooks/output_27_0.png | Bin 0 -> 28457 bytes docs/source/notebooks/output_29_1.png | Bin 0 -> 27775 bytes docs/source/notebooks/output_37_1.png | Bin 0 -> 18172 bytes 16 files changed, 2896 insertions(+) create mode 100644 docs/source/notebooks/N1_Demand_WaterRights_File_Modification.rst create mode 100644 docs/source/notebooks/N2_Evaporation_File_Modification.rst create mode 100644 docs/source/notebooks/N3_Reservoir_File_Modification.rst create mode 100644 docs/source/notebooks/N4_Streamflow_File_Modification.rst create mode 100644 docs/source/notebooks/N5_Batch_Modification.rst create mode 100644 docs/source/notebooks/output_12_1.png create mode 100644 docs/source/notebooks/output_13_1.png create mode 100644 docs/source/notebooks/output_15_0.png create mode 100644 docs/source/notebooks/output_15_1.png create mode 100644 docs/source/notebooks/output_24_0.png create mode 100644 docs/source/notebooks/output_25_2.png create mode 100644 docs/source/notebooks/output_26_1.png create mode 100644 docs/source/notebooks/output_27_0.png create mode 100644 docs/source/notebooks/output_29_1.png create mode 100644 docs/source/notebooks/output_37_1.png diff --git a/docs/source/getting-started/quickstarter.rst b/docs/source/getting-started/quickstarter.rst index aa355f9..f2d6368 100644 --- a/docs/source/getting-started/quickstarter.rst +++ b/docs/source/getting-started/quickstarter.rst @@ -54,3 +54,10 @@ The table below lists the - This notebook looks at how changes in storage in reservoirs in the Upper Colorado subbasin lead to changes to user shortages - This notebook debuts the stationary Hidden Markov Model to generate alternative streamflow scenarios across the basins. - This notebook demonstrates how to create a global Latin hypercube sample to consider multiple uncertainties in a basin. + + +.. include:: ../notebooks/N1_Demand_WaterRights_File_Modification.rst +.. include:: ../notebooks/N2_Evaporation_File_Modification.rst +.. include:: ../notebooks/N3_Reservoir_File_Modification.rst +.. include:: ../notebooks/N4_Streamflow_File_Modification.rst +.. include:: ../notebooks/N5_Batch_Modification.rst diff --git a/docs/source/notebooks/N1_Demand_WaterRights_File_Modification.rst b/docs/source/notebooks/N1_Demand_WaterRights_File_Modification.rst new file mode 100644 index 0000000..6302a1d --- /dev/null +++ b/docs/source/notebooks/N1_Demand_WaterRights_File_Modification.rst @@ -0,0 +1,1055 @@ +``statemodify`` Quickstarter Notebook #1: Getting Started and Using the DDM and DDR Modification Functions in the San Juan River Basin +-------------------------------------------------------------------------------------------------------------------------------------- + +In this series of five notebooks, we demonstrate the functionality of +``statemodify`` using three of the five subbasins on the West Slope +basin in the state of Colorado: Gunnison, San Juan/Dolores, and the +Upper Colorado Basin. There are two classes of adjustments offered in +``statemodify`` that can be used to create alternative future states of +the world for the region: + +1. Application of multipliers or additives to the original dataset which + are sampled from specified bounds using a Latin hypercube sample + +2. Complete swap of input data with data generated from an external + method + +Option 1 is applicable to ``.ddm`` (monthly demand), ``.ddr`` (water +rights), ``.eva`` (reservoir evaporation), ``.res`` (reservoir storage). + +Option 2 is applicable to ``.xbm`` (monthly streamflow) and ``.iwr`` +(irrigation demand). In ``statemodify`` we provide a Hidden Markov Model +(HMM)-based approach to generate synthetic flows across the basins and +tie in irrigation demand to be negatively correlated to increased +streamflow. + +In this first notebook, we will demonstrate now to use the demand +(``modify_ddm()``)and water rights (``modify_ddr()``) modification +functions in the San Juan River Basin. Demands are projected to increase +with the growth of cities and agriculture and water rights will likely +change as discussions on changes to the Colorado Compact and +re-allocation of water across the Colorado River Basin to promote +sustainable development continue. + +.. container:: alert alert-block alert-warning + + Tip: When each StateMod file is mentioned, clicking on the name will + link the user to the StateMod documentation with more information on + that file. + +Step 1: Run a Historical Simulation in StateMod for the San Juan Basin +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Before we start on an exploratory modeling journey, you may be first +interested in understanding water shortages that the basin has +historically experienced. In the container, we have downloaded and +compiled StateMod, ``statemodify``, and the San Juan dataset from the +Colorado’s Decision Support System (CDSS) website. We can run a baseline +simulation below which takes approximately 4 minutes. In this baseline +simulation, we run StateMod over the length of the historical period +(105 years) under the assumption that we are starting from current +conditions. + +.. code:: ipython3 + + import argparse + import logging + import os + import pickle + from string import Template + import subprocess + + import matplotlib.pyplot as plt + import numpy as np + import pandas as pd + import statemodify as stm + +.. container:: alert alert-block alert-info + + NOTE: Each simulation in this notebook is run for the length of the + historical period (from 1909-2013). If you want to reduce the length + of the simulation, navigate to the ``.ctl`` file and adjust the + ``iystr`` and ``iyend`` variables. For this notebook, these files are + located in: + ``data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod Notebook/sj2015.ctl`` + and + ``data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015.ctl`` + +.. code:: ipython3 + + # statemod directory + statemod_dir = "/usr/src/statemodify/statemod_gunnison_sjd" + + # root directory of statemod data for the target basin + root_dir = os.path.join(statemod_dir, "src", "main", "fortran") + + # home directory of notebook instance + home_dir = os.path.dirname(os.getcwd()) + + # path to the statemod executable + statemod_exe = os.path.join(root_dir, "statemod-17.0.3-gfortran-lin-64bit-o3") + + # data directory and root name for the target basin + data_dir = os.path.join( + home_dir, + "data", + "sj2015_StateMod_modified", + "sj2015_StateMod_modified", + "StateMod" + ) + + # directory to the target basin input files with root name for the basin + basin_path = os.path.join(data_dir, "sj2015B") + + # scenarios output directory + scenarios_dir_ddm = os.path.join(data_dir, "scenarios_ddm") + scenarios_dir_ddr = os.path.join(data_dir, "scenarios_ddr") + + # parquet files output directory + parquet_dir_ddm = os.path.join(data_dir, "parquet_ddm") + parquet_dir_ddr = os.path.join(data_dir, "parquet_ddr") + + # path to ddm and ddr template file + ddm_template_file = os.path.join( + home_dir, + "data", + "sj2015B_template_ddm.rsp" + ) + + ddr_template_file = os.path.join( + home_dir, + "data", + "sj2015B_template_ddr.rsp" + ) + +.. code:: ipython3 + + # run statemod + subprocess.call([statemod_exe, basin_path, "-simulate"]) + + + +.. parsed-literal:: + + Startup log file for messages to this point: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/sj2015B.rsp + Closing startup log file: statem.log + Opening dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/sj2015B.log + ________________________________________________________________________ + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 17.0.3 + Last revision date: 2021/09/12 + + ________________________________________________________________________ + + Subroutine Execut + Subroutine Datinp + + ... + + ________________________________________________________________________ + Execut; Successful Termination + Statem; See detailed messages in dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/sj2015B.log + Stop 0 + +Once StateMod has run successfully, we can now extract user shortages +from the +```.xdd`` `__ +output file using the ``statemodify`` output modification function +``convert_xdd()``. We denote a list of user IDs +(‘2900501’,‘2900519’,‘2900555’) who we want to extract shortages for and +then these shortages are saved in a compressed Parquet file format that +can then be read in as a Pandas dataframe in Python. We can also remove +the larger output files once the requested shortages have been extracted +and saved. + +.. code:: ipython3 + + #Extract shortages using statemodify convert_xdd() function + + # create a directory to store the historical shortages + output_dir = os.path.join(data_dir, "historic_shortages") + + # create a directory to store the new files in if it does not exist + output_directory = os.path.join(data_dir, "historic_shortages") + if not os.path.exists(output_directory): + os.makedirs(output_directory) + + stm.xdd.convert_xdd( + # path to a directory where output .parquet files should be written + output_path=output_dir, + # whether to abort if .parquet files already exist at the output_path + allow_overwrite=True, + # path, glob, or a list of paths/globs to the .xdd files you want to convert + xdd_files=os.path.join(data_dir, "*.xdd"), + # if the output .parquet files should only contain a subset of structure ids, list them here; None for all + id_subset=['2900501','2900519','2900555'], + # how many .xdd files to convert in parallel; optimally you will want 2-4 CPUs per parallel process + parallel_jobs=4, + # convert to natural data types + preserve_string_dtype=False + ) + + + +.. parsed-literal:: + + 100%|██████████| 1/1 [00:00<00:00, 29.32it/s] + + +.. code:: ipython3 + + data=pd.read_parquet(os.path.join(output_dir,'sj2015B.parquet'),engine='pyarrow') + data + + +.. raw:: html + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
structure_namestructure_idriver_idyearmonthdemand_totaldemand_cufrom_river_by_priorityfrom_river_by_storagefrom_river_by_other...station_in_out_return_flowstation_in_out_well_depletestation_in_out_from_to_groundwater_storagestation_balance_river_inflowstation_balance_river_divertstation_balance_river_by_wellstation_balance_river_outflowavailable_flowcontrol_locationcontrol_right
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..................................................................
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+

4095 rows × 36 columns

+
+ + + +Upon inspecting the Parquet file above, we see the contents of the +``.xdd`` file, including the shortages experienced by the structures +that we specified for the length of the historical period. + +We can then take these shortages and plot them for our list of users. + +.. code:: ipython3 + + fig, ax = plt.subplots() + + for name, group in data.groupby('structure_id'): + ax.scatter( + group['year'], group['shortage_total'], label=name) + + plt.xlabel("Year") + plt.ylabel("Shortage (AF)") + plt.legend() + + + + +.. parsed-literal:: + + + + + + +.. image:: ../notebooks/output_15_1.png + + +You can look up the names and rights of the users listed above in the +``sj2015.ddr`` file (found at +``data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/sj2015.ddr``). +Here, a higher Admin # denotes lower seniority. You’ll see that the +users chosen here have junior to medium seniority of water rights with +varying amounts of water decreed to them. The figure above shows that +all users have experienced shortages. User 2900501 has experienced the +most frequent shortages respectively, likely due in part to their less +senior water right. Generally, we see a higher magnitude of shortages +for all users during the 2002 drought. + +Step 2a: Modify StateMod Input Files for Exploratory Analyses- Demand Function Example +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Now that we’ve run StateMod in baseline mode, the next step shows how we +can run it in an exploratory analysis mode. To do this, we need to +create some plausible futures and adjust the input files of StateMod to +reflect these changes. In this step, we’ll demonstrate Option 1 for +statemodify adjustments using the +```.ddm`` `__ +file as an example, which involves multiplying the current demand time +series for these users by a value in between 0.5 to 1.5. Here we specify +the IDs of the users and the bounds from which we want to sample +multipliers for the demand. We create 2 alternative states of the world +(SOW) using a Latin hypercube sampling (LHS) procedure and store them in +the ``input_files`` directory. + +.. code:: ipython3 + + # a dictionary to describe what users you want to modify and the bounds for the LHS + setup_dict = { + "ids": ["2900501", "2900519","2900555"], + "bounds": [0.5, 1.5] + } + + output_directory = output_dir = os.path.join(data_dir, "input_files") + + scenario = "1" + + # the number of samples you wish to generate + n_samples = 2 + + # seed value for reproducibility if so desired + seed_value = 1 + + # number of rows to skip in file after comment + skip_rows = 1 + + # name of field to query + query_field = "id" + + # number of jobs to launch in parallel; -1 is all but 1 processor used + n_jobs = -1 + + # basin to process + basin_name = "San_Juan" + + # generate a batch of files using generated LHS + stm.modify_ddm( + modify_dict=setup_dict, + query_field=query_field, + output_dir=output_directory, + scenario=scenario, + basin_name=basin_name, + sampling_method="LHS", + n_samples=n_samples, + skip_rows=skip_rows, + n_jobs=n_jobs, + seed_value=seed_value, + template_file=None, + factor_method="multiply", + data_specification_file=None, + min_bound_value=-0.5, + max_bound_value=1.5, + save_sample=True + ) + + +It’s helpful to set ``save_sample=True`` to see the values of the +multipliers that we are creating. We see below that in our 1st SOW, we +are reducing demand for our users by 30% and then in our 2nd SOW, we are +increasing demand for our users by 36%. + +.. code:: ipython3 + + sample_array = np.load(output_directory+'/ddm_2-samples_scenario-1.npy') + sample_array + + + + +.. parsed-literal:: + + array([[0.708511 ], + [1.36016225]]) + + + +Step 2b: Read in the New Input Files and Run StateMod : Demand Function Example +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Now that we have created the input files, the next step is to run +StateMod with the new input files. The file that StateMod uses to +configure a simulation is called a +```.rsp`` `__ +file. For this dataset, the configuration file is ``sj2015B.rsp``. This +file contains the paths of all of the supporting files that StateMod +needs to run. We create a template .rsp file +(``sj2015B_template_ddm.rsp``) and swap in the path to the two new +alternative ``.ddm`` files that are created. Then we run StateMod for +the two scenarios and store the shortages in Parquet file format. Each +scenario will take approximately 4 minutes. + +.. code:: ipython3 + + # set realization and sample + realization = 1 + sample = np.arange(0, 2, 1) + + # read RSP template + with open(ddm_template_file) as template_obj: + + # read in file + template_rsp = Template(template_obj.read()) + + for i in sample: + + # create scenario name + scenario = f"S{i}_{realization}" + + # dictionary holding search keys and replacement values to update the template file + d = {"DDM": f"../../input_files/sj2015B_{scenario}.ddm"} + + # update the template + new_rsp = template_rsp.safe_substitute(d) + + # construct simulated scenario directory + simulated_scenario_dir = os.path.join(scenarios_dir_ddm, scenario) + if not os.path.exists(simulated_scenario_dir): + os.makedirs(simulated_scenario_dir) + + # target rsp file + rsp_file = os.path.join(simulated_scenario_dir, f"sj2015B_{scenario}.rsp") + + # write updated rsp file + with open(rsp_file, "w") as f1: + f1.write(new_rsp) + + # construct simulated basin path + simulated_basin_path = os.path.join(simulated_scenario_dir, f"sj2015B_{scenario}") + + # run StateMod + print(f"Running: {scenario}") + os.chdir(simulated_scenario_dir) + + subprocess.call([statemod_exe, simulated_basin_path, "-simulate"]) + + #Save output to parquet files + print('creating parquet for ' + scenario) + + output_directory = os.path.join(parquet_dir_ddm+"/scenario/"+ scenario) + + if not os.path.exists(output_directory): + os.makedirs(output_directory) + + stm.xdd.convert_xdd( + output_path=output_directory, + allow_overwrite=False, + xdd_files=scenarios_dir_ddm + "/"+ scenario + "/sj2015B_"+scenario+".xdd", + id_subset=['2900501','2900519','2900555'], + parallel_jobs=4, + preserve_string_dtype=False + ) + + + +.. parsed-literal:: + + Running: S0_1 + Startup log file for messages to this point: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddm/S0_1/sj2015B_S0_1.rsp + Closing startup log file: statem.log + Opening dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddm/S0_1/sj2015B_S0_1.log + ________________________________________________________________________ + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 17.0.3 + Last revision date: 2021/09/12 + + ________________________________________________________________________ + + Subroutine Execut + Subroutine Datinp + + ... + + ________________________________________________________________________ + Execut; Successful Termination + Statem; See detailed messages in dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddm/S0_1/sj2015B_S0_1.log + Stop 0 + creating parquet for S0_1 + + +Step 2c: Visualize Shortages in New SOWs- Demand Function Example +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Now that we have run our simulations, we can visualize the difference in +shortages experienced by the stakeholders in our two SOWs. Let’s focus +on the user: 2900501, a junior user who experienced the most frequent +shortages historically across the stakeholders we looked at. Let’s look +back at the LHS sample and see that SOW 1 is where we have a decreased +demand (0.7 multiplier) and SOW 2 is where we have an increased demand +(1.4 multiplier). + +.. code:: ipython3 + + output_directory = os.path.join(data_dir, "input_files") + sample_array = np.load(output_directory+'/ddm_2-samples_scenario-1.npy') + sample_array + + + + +.. parsed-literal:: + + array([[0.708511 ], + [1.36016225]]) + + + +Now we can define shortages in the alternative states of the world with +respect to the shortages received in the baseline case. + +.. code:: ipython3 + + # Read in raw parquet files + baseline=pd.read_parquet(data_dir+'/historic_shortages/sj2015B.parquet',engine='pyarrow') + SOW_1=pd.read_parquet(parquet_dir_ddm+'/scenario/S0_1/sj2015B_S0_1.parquet',engine='pyarrow') + SOW_2=pd.read_parquet(parquet_dir_ddm+'/scenario/S1_1/sj2015B_S1_1.parquet',engine='pyarrow') + + # Subtract shortages with respect to the baseline + subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total'],SOW_2['shortage_total']],axis=1) + subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1','SOW_2'], axis=1) + subset_df['SOW_1_diff'] = subset_df['SOW_1']-subset_df['Baseline'] + subset_df['SOW_2_diff'] = subset_df['SOW_2']-subset_df['Baseline'] + + # Plot shortages + fig, ax = plt.subplots() + + ax.scatter(subset_df['Year'], subset_df['SOW_1_diff'],label='Decreased Demand') + ax.scatter(subset_df['Year'], subset_df['SOW_2_diff'],label='Increased Demand') + + plt.xlabel("Year") + plt.ylabel("Shortage (AF)") + plt.title("Change in Shortages from the Baseline") + plt.legend() + + + + +.. parsed-literal:: + + + + +.. image:: ../notebooks/output_29_1.png + + +As expected, we see that an increase in demand typically causes an +increase in shortage magnitude and frequency whereas the reduction in +demand leads to the opposite. This finishes our simple example to +demonstrate how adjustments to demand might change the shortages +experienced by a user. + +Step 3a: Modify StateMod Input Files for Exploratory Analyses- Water Rights Function Example +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Following from Step 2, we can run the same analysis for the function +that manipulates the ``sj2015.ddr`` file, which corresponds to users +water rights. In this function, we can specify the IDs of the users and +can can utilize a variety of options for how we want to change the +```.ddr`` `__ +file. We can either sample from some bounds that apply multipliers to +the decree, hard code in values for the decree, or adjust the rank of +the user. In this simple example, we take a very junior user, ID: +2900501, and make them have the highest water right by changing their +rank to 1. + +.. code:: ipython3 + + # a dictionary to describe what you want to modify and the bounds for the LHS + setup_dict = { + # ids can either be 'struct' or 'id' values + "ids": ["2900501"], + + # turn id on or off completely or for a given period + # if 0 = off, 1 = on, YYYY = on for years >= YYYY, -YYYY = off for years > YYYY; see file header + "on_off": [1], + + # apply rank of administrative order where 0 is lowest (senior) and n is highest (junior); None is no change + "admin": [1], + } + + output_directory = os.path.join(data_dir, "input_files") + scenario = "1" + + # the number of samples you wish to generate + n_samples = 1 + + # seed value for reproducibility if so desired + seed_value = 1 + + # number of rows to skip in file after comment + skip_rows = 0 + + # name of field to query + query_field = "struct" + + # number of jobs to launch in parallel; -1 is all but 1 processor used + n_jobs = -1 + + # basin to process + basin_name = "San_Juan" + + # generate a batch of files using generated LHS + stm.modify_ddr( + modify_dict=setup_dict, + query_field=query_field, + output_dir=output_directory, + scenario=scenario, + basin_name=basin_name, + sampling_method="LHS", + n_samples=n_samples, + skip_rows=skip_rows, + n_jobs=n_jobs, + seed_value=seed_value, + template_file=None, + factor_method="multiply", + data_specification_file=None, + min_bound_value=-0.5, + max_bound_value=1.5, + save_sample=True + ) + + +In the ``input_files`` directory, you can open the ``sj2015B_S0_1.ddr`` +file and see that the Admin # of our selected user has now become +1.0000. Now we rerun our code to do the StateMod simulation, this time +using the .\ ``ddr`` template file. + +.. code:: ipython3 + + # set realization and sample + realization = 1 + sample = np.arange(0, 1, 1) + + # read RSP template + with open(ddr_template_file) as template_obj: + + # read in file + template_rsp = Template(template_obj.read()) + + for i in sample: + + # create scenario name + scenario = f"S{i}_{realization}" + + # dictionary holding search keys and replacement values to update the template file + d = {"DDR": f"../../input_files/sj2015B_{scenario}.ddr"} + + # update the template + new_rsp = template_rsp.safe_substitute(d) + + # construct simulated scenario directory + simulated_scenario_dir = os.path.join(scenarios_dir_ddr, scenario) + if not os.path.exists(simulated_scenario_dir): + os.makedirs(simulated_scenario_dir) + + # target rsp file + rsp_file = os.path.join(simulated_scenario_dir, f"sj2015B_{scenario}.rsp") + + # write updated rsp file + with open(rsp_file, "w") as f1: + f1.write(new_rsp) + + # construct simulated basin path + simulated_basin_path = os.path.join(simulated_scenario_dir, f"sj2015B_{scenario}") + + # run StateMod + print(f"Running: {scenario}") + os.chdir(simulated_scenario_dir) + + subprocess.call([statemod_exe, simulated_basin_path, "-simulate"]) + + #Save output to parquet files + print('creating parquet for ' + scenario) + + output_directory = os.path.join(parquet_dir_ddr+"/scenario/"+ scenario) + + if not os.path.exists(output_directory): + os.makedirs(output_directory) + + stm.xdd.convert_xdd( + output_path=output_directory, + allow_overwrite=False, + xdd_files=scenarios_dir_ddr + "/"+ scenario + "/sj2015B_"+scenario+".xdd", + id_subset=['2900501'], + parallel_jobs=2, + preserve_string_dtype=False + ) + + + +.. parsed-literal:: + + Running: S0_1 + Startup log file for messages to this point: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddr/S0_1/sj2015B_S0_1.rsp + Closing startup log file: statem.log + Opening dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddr/S0_1/sj2015B_S0_1.log + ________________________________________________________________________ + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 17.0.3 + Last revision date: 2021/09/12 + + ________________________________________________________________________ + + Subroutine Execut + Subroutine Datinp + + ... + + ________________________________________________________________________ + Execut; Successful Termination + Statem; See detailed messages in dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddr/S0_1/sj2015B_S0_1.log + Stop 0 + creating parquet for S0_1 + + +As before, let’s go ahead and plot the shortages for our User 2900501 +with respect to the baseline shortages. + +.. code:: ipython3 + + # Read in raw parquet files + baseline=pd.read_parquet(data_dir+'/historic_shortages/sj2015B.parquet',engine='pyarrow') + SOW_1=pd.read_parquet(parquet_dir_ddr+ '/scenario/S0_1/sj2015B_S0_1.parquet',engine='pyarrow') + + # Subtract shortages with respect to the baseline + subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total']],axis=1) + subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1'], axis=1) + subset_df['diff']=subset_df['SOW_1']-subset_df['Baseline'] + + # Plot shortages + fig, ax = plt.subplots() + + ax.scatter(subset_df['Year'], subset_df['diff']) + + plt.xlabel("Year") + plt.ylabel("Shortage (AF)") + plt.title("Change in Shortages from the Baseline") + + + + +.. parsed-literal:: + + Text(0.5, 1.0, 'Change in Shortages from the Baseline') + + + + +.. image:: ../notebooks/output_37_1.png + + +We generally see the behavior we expect to see which is that with more +senior water rights, the user sees a decrease in shortage magnitude. + +Now, continue on to Quickstarter Notebook #2 to learn how to use the +reservoir evaporation modification fuction. + +.. container:: alert alert-block alert-warning + + Tip: If you are interested in understanding how to apply + ``statemodify`` functions to your own model, take a look at the + source code found in the repository here: + + .. container:: + + :: + + 1. modify_ddm() + + .. container:: + + :: + + 2. modify_ddr() + diff --git a/docs/source/notebooks/N2_Evaporation_File_Modification.rst b/docs/source/notebooks/N2_Evaporation_File_Modification.rst new file mode 100644 index 0000000..fc96c12 --- /dev/null +++ b/docs/source/notebooks/N2_Evaporation_File_Modification.rst @@ -0,0 +1,592 @@ +``statemodify`` Quickstarter Notebook #2 : Using the EVA Modification Function in the Gunnison River Basin +---------------------------------------------------------------------------------------------------------- + +This notebook demonstrates the reservoir evaporation modification +function using the Gunnison River Basin as an example. Reservoir +evaporation is a pressing concern in the CRB. Lake Powell loses 0.86 +million acre/ft per year to evaporation, which is over 6% of the flow +into the Colorado River and nearly the allocation to the state of Utah. +With warming temperatures driving aridification in the region, +evaporation will play an increasingly important role in shortages to +users. + +Step 1: Run a Historical Simulation in StateMod for the Gunnison Subbasin +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +To explore the importance of evaporation, we first we run baseline +simulation as we did in the first notebook, but this time, using the +dataset for the Gunnison. + +.. code:: ipython3 + + import argparse + import logging + import os + import pickle + from string import Template + import subprocess + + import matplotlib.pyplot as plt + import numpy as np + import pandas as pd + import statemodify as stm + +.. container:: alert alert-block alert-info + + NOTE: Each simulation in this notebook is run for the length of the + historical period (from 1909-2013). If you want to reduce the length + of the simulation, navigate to the ``.ctl`` file and adjust the + ``iystr`` and ``iyend`` variables. For this notebook, this file is + located in: + ``data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015.ctl`` + +.. code:: ipython3 + + # statemod directory + statemod_dir = "/usr/src/statemodify/statemod_gunnison_sjd" + + # root directory of statemod data for the target basin + root_dir = os.path.join(statemod_dir, "src", "main", "fortran") + + # home directory of notebook instance + home_dir = os.path.dirname(os.getcwd()) + + # path to the statemod executable + statemod_exe = os.path.join(root_dir, "statemod-17.0.3-gfortran-lin-64bit-o3") + + # data directory and root name for the target basin + data_dir = os.path.join( + home_dir, + "data", + "gm2015_StateMod_modified", + "gm2015_StateMod_modified", + "StateMod" + ) + + # directory to the target basin input files with root name for the basin + basin_path = os.path.join(data_dir, "gm2015B") + + # scenarios output directory + scenarios_dir = os.path.join(data_dir, "scenarios") + + # path to eva template file + eva_template_file = os.path.join( + home_dir, + "data", + "gm2015B_template_eva.rsp" + ) + +.. code:: ipython3 + + # run statemod + subprocess.call([statemod_exe, basin_path, "-simulate"]) + + + +.. parsed-literal:: + + Startup log file for messages to this point: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015B.rsp + Closing startup log file: statem.log + Opening dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015B.log + ________________________________________________________________________ + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 17.0.3 + Last revision date: 2021/09/12 + + ________________________________________________________________________ + + Subroutine Execut + Subroutine Datinp + + ... + + ________________________________________________________________________ + Execut; Successful Termination + Statem; See detailed messages in dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015B.log + Stop 0 + + + +In this notebook, rather than acquiring user shortages which are found +in the ``.xdd`` output file, we can track reservoir storage which is +found in the +```.xre`` `__ +output file. Thus, in ``statemodify``, we create a method that will +allow us to extract output from the ``gm2015B.xre`` file and save it as +a ``.csv`` file. Here we extract the shortages for Blue Mesa, one of the +most important upstream reservoirs in the Gunnison that is responsible +for supplying emergency water to Lake Powell. + +.. code:: ipython3 + + # create a directory to store the historical reservoir levels at Blue Mesa + output_dir = os.path.join(data_dir, "historic_reservoir_levels") + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + # path the the xre file + xre_file = os.path.join(data_dir, "gm2015B.xre") + + # structure ID for reservoir of interest + structure_ID = '6203532' + + # name of the reservoir + structure_name = 'Blue_Mesa' + + # extract the target info into a Pandas data frame + df = stm.extract_xre_data(structure_name=structure_name, + structure_id=structure_ID, + input_file=xre_file, + basin_name=None, + output_directory=output_dir, + write_csv=True, + write_parquet=None + ) + +We can then create an annual average from our extracted monthly +reservoir storage. + +.. code:: ipython3 + + output_xre_file = os.path.join(output_dir, "Blue_Mesa_xre_data.csv") + + # read output data into a data frame + df = pd.read_csv( + output_xre_file, + usecols=['Year','Init. Storage'], + index_col=False) + + # calculate the annual average + df = df.groupby('Year').mean().reset_index() + + df + + + + + +.. raw:: html + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
YearInit. Storage
01908441516.500000
11909409705.807692
21910378741.903846
31911374242.865385
41912402187.230769
.........
1012009384270.711538
1022010380057.192308
1032011346074.019231
1042012290796.692308
1052013202086.125000
+

106 rows × 2 columns

+
+ + + +Finally, we can plot this annual average over time. We see swings in the +storage that correspond well with the earliest part of the streamflow +record that was relatively wet along with dry periods (large dips around +the 1930s dustbowl and 1950s drought and the severe early 2002 drought). + +.. code:: ipython3 + + fig, ax = plt.subplots() + + plt.plot(df['Year'], df['Init. Storage']) + + plt.title("Blue Mesa Storage") + plt.xlabel("Year") + plt.ylabel("Reservoir Storage (AF)") + + + + + +.. parsed-literal:: + + Text(0, 0.5, 'Reservoir Storage (AF)') + + + + +.. image:: ../notebooks/output_13_1.png + + +Step 2: Modify StateMod Input Files for Exploratory Analyses- Evaporation Function Example +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Now that we’ve run StateMod in baseline mode for the Gunnison, the next +step is to run it in exploratory analysis mode. To do this, we need to +create some plausible futures and adjust the input files for StateMod. +In this step, we’ll demonstrate Option 1 for ``statemodify`` adjustments +using the ``gm2015.eva`` file as an example. Here we apply additives +rather than multipliers. As done in Hadjimichael et al. (2020), we +sample (using LHS) the change of evaporation between -15.24 and 30.46 +cm/month (-0.5 to + 1 ft). The +```.eva`` `__ +file stores information for select larger reservoirs across all West +Slope basins. We choose the ID that corresponds to Blue Mesa (10011). We +create 2 alternative states of the world and store them in the +``input_files`` directory. + +.. code:: ipython3 + + # a dictionary to describe what you want to modify and the bounds for the Latin hypercube sample. + setup_dict = { + "ids": ['10011'], + "bounds": [-0.5, 1.0] + } + + # create a directory to store the new files in if it does not exist + output_directory = os.path.join(data_dir, "input_files") + if not os.path.exists(output_directory): + os.makedirs(output_directory) + + # scenario name + scenario = "1" + + # the number of samples you wish to generate + n_samples = 2 + + # seed value for reproducibility if so desired + seed_value = 1 + + # number of rows to skip in file after comment + skip_rows = 1 + + # name of field to query + query_field = "id" + + # number of jobs to launch in parallel; -1 is all but 1 processor used + n_jobs = -1 + + # basin to process + basin_name = "Gunnison" + + # generate a batch of files using generated LHS + stm.modify_eva(modify_dict=setup_dict, + query_field=query_field, + output_dir=output_directory, + scenario=scenario, + basin_name=basin_name, + sampling_method="LHS", + n_samples=n_samples, + skip_rows=skip_rows, + n_jobs=n_jobs, + seed_value=seed_value, + template_file=None, + factor_method="add", + data_specification_file=None, + min_bound_value=-0.5, + max_bound_value=1.0, + save_sample=True) + + +If we print our two samples below, we see that we’ve created a state of +the world that has reduced evaporation (subtracting 0.18 ft) and one +with increased evaporation (adding 0.79 ft). These samples will be +termed SOW 1 and SOW 2 respectively. + +.. code:: ipython3 + + # path to the numpy file containing the samples + eva_samples_file = os.path.join(output_directory, "eva_2-samples_scenario-1.npy") + + # load samples + sample_array = np.load(eva_samples_file) + + sample_array + + + + + +.. parsed-literal:: + + array([[-0.1872335 ], + [ 0.79024337]]) + + + +Step 3: Read in the New Input Files and Run StateMod : Evaporation Example +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Now that we have created the input files, the next step is to run +StateMod with the new input files. We create a template ``.rsp`` file +(``gm2015B_template_eva.rsp``) and swap in the path to the alternative +``.eva`` files that are created. Then we run StateMod for the two +scenarios and extract the reservoir levels for Blue Mesa. + +.. code:: ipython3 + + # set realization and sample + realization = 1 + sample = np.arange(0, 2, 1) + + # read RSP template + with open(eva_template_file) as template_obj: + + # read in file + template_rsp = Template(template_obj.read()) + + for i in sample: + + # create scenario name + scenario = f"S{i}_{realization}" + + # dictionary holding search keys and replacement values to update the template file + d = {"EVA": f"../../input_files/gm2015B_{scenario}.eva"} + + # update the template + new_rsp = template_rsp.safe_substitute(d) + + # construct simulated scenario directory + simulated_scenario_dir = os.path.join(scenarios_dir, scenario) + if not os.path.exists(simulated_scenario_dir): + os.makedirs(simulated_scenario_dir) + + # target rsp file + rsp_file = os.path.join(simulated_scenario_dir, f"gm2015B_{scenario}.rsp") + + # write updated rsp file + with open(rsp_file, "w") as f1: + f1.write(new_rsp) + + # construct simulated basin path + simulated_basin_path = os.path.join(simulated_scenario_dir, f"gm2015B_{scenario}") + + # run StateMod + print(f"Running: {scenario}") + os.chdir(simulated_scenario_dir) + + subprocess.call([statemod_exe, simulated_basin_path, "-simulate"]) + + + +.. parsed-literal:: + + Running: S0_1 + Startup log file for messages to this point: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S0_1/gm2015B_S0_1.rsp + Closing startup log file: statem.log + Opening dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S0_1/gm2015B_S0_1.log + ________________________________________________________________________ + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 17.0.3 + Last revision date: 2021/09/12 + + ________________________________________________________________________ + + Subroutine Execut + Subroutine Datinp + + ... + + ________________________________________________________________________ + Execut; Successful Termination + Statem; See detailed messages in dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S1_1/gm2015B_S1_1.log + Stop 0 + + +Step 4: Visualize Reservoir Levels in New SOWs +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Using ``extract_xre_data()``, we can then extract the reservoir levels +at Blue Mesa in the two new SOWs. + +.. code:: ipython3 + + # SOW 1 + output_dir= os.path.join(scenarios_dir, "S0_1") + + # path the the xre file + xre_file = os.path.join(output_dir, "gm2015B_S0_1.xre") + + # structure ID for reservoir of interest + structure_ID = '6203532' + + # name of the reservoir + structure_name = 'Blue_Mesa' + + # extract the target info into a Pandas data frame + df = stm.extract_xre_data(structure_name=structure_name, + structure_id=structure_ID, + input_file=xre_file, + basin_name=None, + output_directory=output_dir, + write_csv=True, + write_parquet=None + ) + + # SOW 2 + output_dir= os.path.join(scenarios_dir, "S1_1") + + # path the the xre file + xre_file = os.path.join(output_dir, "gm2015B_S1_1.xre") + + + # extract the target info into a Pandas data frame + df = stm.extract_xre_data(structure_name=structure_name, + structure_id=structure_ID, + input_file=xre_file, + basin_name=None, + output_directory=output_dir, + write_csv=True, + write_parquet=None + ) + + +Finally, we can plot reservoir storage through time in our baseline +world and alternative states of the world. + +.. code:: ipython3 + + # historic reservoir directory + historic_res_dir = os.path.join(data_dir, "historic_reservoir_levels") + blue_mesa_file = os.path.join(historic_res_dir, "Blue_Mesa_xre_data.csv") + + # Import baseline dataframe + baseline = pd.read_csv(blue_mesa_file, index_col=False, usecols=['Year','Init. Storage']) + baseline = baseline.groupby('Year').mean().reset_index() + + # Import SOW1 + s0_1_file = os.path.join(scenarios_dir, "S0_1", "Blue_Mesa_xre_data.csv") + SOW1 = pd.read_csv(s0_1_file, index_col=False, usecols=['Year','Init. Storage']) + SOW1 = SOW1.groupby('Year').mean().reset_index() + + # Import SOW2 + s1_1_file = os.path.join(scenarios_dir, "S1_1", "Blue_Mesa_xre_data.csv") + SOW2 = pd.read_csv(s1_1_file, index_col=False, usecols=['Year','Init. Storage']) + SOW2 = SOW2.groupby('Year').mean().reset_index() + + # Plot reservoir levels + fig, ax = plt.subplots() + + plt.plot(baseline['Year'], baseline['Init. Storage'],label='Baseline') + plt.plot(SOW1['Year'], SOW1['Init. Storage'],label='Reduced Evaporation') + plt.plot(SOW2['Year'], SOW2['Init. Storage'],label='Increased Evaporation') + + plt.title("Blue Mesa Storage") + plt.xlabel("Year") + plt.ylabel("Reservoir Storage (AF)") + + plt.legend() + + + + + +.. parsed-literal:: + + + + + + +.. image:: ../notebooks/output_26_1.png + + +We see that in SOW 1 (which corresponds to reduced evaporation), the +Blue Mesa storage is slightly higher than baseline. However, in SOW 2, +which corresponds to increased evaporation, we see that the reservoir +storage has reduced considerably. + +We now encourage the user to explore how the changes in reservoir +storage impacts user shortages in Quickstarter Notebook #3. + +References +~~~~~~~~~~ + +Hadjimichael, A., Quinn, J., Wilson, E., Reed, P., Basdekas, L., Yates, +D., & Garrison, M. (2020). Defining robustness, vulnerabilities, and +consequential scenarios for diverse stakeholder interests in +institutionally complex river basins. Earth’s Future, 8(7), +e2020EF001503. + +.. container:: alert alert-block alert-warning + + Tip: If you are interested in understanding how to apply + ``statemodify`` functions to your own model, take a look at the + source code found in the repository here: + + .. container:: + + :: + + 1. modify_eva() + diff --git a/docs/source/notebooks/N3_Reservoir_File_Modification.rst b/docs/source/notebooks/N3_Reservoir_File_Modification.rst new file mode 100644 index 0000000..9495edd --- /dev/null +++ b/docs/source/notebooks/N3_Reservoir_File_Modification.rst @@ -0,0 +1,519 @@ +``statemodify`` Quickstarter Notebook #3 : Using the RES Modification Function in the Upper Colorado River Basin +---------------------------------------------------------------------------------------------------------------- + +This notebook demonstrates the reservoir storage modification function +in the Upper Colorado River Basin. In this notebook, we seek to +understand how changes to reservoir storage can impact user shortages. +First we run a baseline simulation, which runs the StateMod simulation +assuming that the current infrastructure has existed through the whole +simulation period. We next extract shortages for a municipality. Recall +that the list of users and their water rights can be found in the +``.ddr`` file (located: ``data/cm2015_StateMod/StateMod/cm2015.ddr``) + +Step 1: Run a Historical Simulation in StateMod for the Uppper Colorado Subbasin +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: ipython3 + + import argparse + import logging + import os + import pickle + from string import Template + import subprocess + + import matplotlib.pyplot as plt + import numpy as np + import pandas as pd + import statemodify as stm + +.. container:: alert alert-block alert-info + + NOTE: Each simulation in this notebook is run for the length of the + historical period (from 1909-2013). If you want to reduce the length + of the simulation, navigate to the ``.ctl`` file and adjust the + ``iystr`` and ``iyend`` variables. For this notebook, this file is + located in: ``data/cm2015_StateMod/StateMod/cm2015.ctl`` + +As before, we set the directories and associated paths and also run +StateMod in a baseline simulation. + +.. code:: ipython3 + + # statemod directory + statemod_dir = "/usr/src/statemodify/statemod_upper_co" + + # root directory of statemod data for the target basin + root_dir = os.path.join(statemod_dir, "src", "main", "fortran") + + # home directory of notebook instance + home_dir = os.path.dirname(os.getcwd()) + + # path to the statemod executable + statemod_exe = os.path.join(root_dir, "statemod") + + # data directory and root name for the target basin + data_dir = os.path.join( + home_dir, + "data", + "cm2015_StateMod", + "StateMod" + ) + + # directory to the target basin input files with root name for the basin + basin_path = os.path.join(data_dir, "cm2015B") + + # scenarios output directory + scenarios_dir_res = os.path.join(data_dir, "scenarios_res") + + # parquet files output directory + parquet_dir_res = os.path.join(data_dir, "parquet_res") + + + # path to res template file + res_template_file = os.path.join( + home_dir, + "data", + "cm2015B_template_res.rsp" + ) + +.. container:: alert alert-block alert-info + + NOTE In order to expedite simulations for the Upper Colorado dataset, + make sure to turn off “Reoperation” mode. You can do so by opening + ``/home/jovyan/data/cm2015_StateMod/StateMod/cm2015.ctl``, navigating + to the ``ireopx`` entry and changing the value from “0” to “10”. + +.. code:: ipython3 + + # Change directories first + os.chdir(data_dir) #This is needed specific to the Upper Colorado model as the path name is too long for the model to accept + subprocess.call([statemod_exe, "cm2015B", "-simulate"]) + + + +.. parsed-literal:: + + Parse; Command line argument: + cm2015B -simulate + ________________________________________________________________________ + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 15.00.01 + Last revision date: 2015/10/28 + + ________________________________________________________________________ + + Opening log file cm2015B.log + + Subroutine Execut + Subroutine Datinp + + ... + + ________________________________________________________________________ + Execut; Successful Termination + Statem; See detailed messages in file: cm2015B.log + Stop 0 + + + +We isolate the shortages for one municipal user: the Town of Brekenridge +at the base of the Rocky Mountains’ Tenmile Range (ID: 3601008). If we +look up this user in the ``cm2015B.ddr`` file, we see that the user has +median water rights (47483.00000) and a smaller decree of 2.90 cfs. + +.. code:: ipython3 + + #Extract shortages using statemodify convert_xdd() function + + # create a directory to store the historic shortages + output_dir = os.path.join(data_dir, "historic_shortages") + + # create a directory to store the new files in if it does not exist + output_directory = os.path.join(data_dir, "historic_shortages") + if not os.path.exists(output_directory): + os.makedirs(output_directory) + + stm.xdd.convert_xdd( + # path to a directory where output .parquet files should be written + output_path=output_dir, + # whether to abort if .parquet files already exist at the output_path + allow_overwrite=True, + # path, glob, or a list of paths/globs to the .xdd files you want to convert + xdd_files=os.path.join(data_dir, "*.xdd"), + # if the output .parquet files should only contain a subset of structure ids, list them here; None for all + id_subset=['3601008'], + # how many .xdd files to convert in parallel; optimally you will want 2-4 CPUs per parallel process + parallel_jobs=2, + # convert to natural data types + preserve_string_dtype=False + + ) + + +.. parsed-literal:: + + 100%|██████████| 1/1 [00:00<00:00, 17.73it/s] + + +Next we plot the shortages for Breckenridge. + +.. code:: ipython3 + + data=pd.read_parquet(output_dir +'/cm2015B.parquet',engine='pyarrow') + + fig, ax = plt.subplots() + + for name, group in data.groupby('structure_id'): + ax.scatter( + group['year'], group['shortage_total'], label=name) + + plt.xlabel("Year") + plt.ylabel("Shortage (AF)") + plt.title("Baseline Shortages for Breckenridge") + plt.legend() + + + + +.. parsed-literal:: + + + + + + +.. image:: ../notebooks/output_12_1.png + + +We see that Breckenridge has experienced a variety of shortages +throughout the baseline simulation period. + +Step 2: Modify StateMod Input Files for Exploratory Analyses- Reservoir Function Example +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +If we look at the ``cm2015B.res`` file (learn more about the +```.res`` `__\ file), +we see that Breckenridge has an account in the Clinton Gulch Reservoir, +but a quick look in the +```.opr`` `__ +file also indicates that Breckenridge can receive water from the Dillon +reservoir. Let’s investigate what happens to these shortages when +storage at these two basins decreases using the ``modify_res()`` +function. As done in Hadjimichael et al. (2020), we sample losses using +a Latin hypercube sampling of up to 20% of the capacity of the +reservoirs (informed by Graf et al. (2010)) which may be due to erosion +and sedimentation of reservoirs in the UCRB, resulting in reduced +storage. The accounts associated with the reservoirs are also reduced +equally in order to accommodate the new storage level. For this example, +we want to change the reservoir storage for a specific set of reservoirs +by specifying the reservoir IDs for the ``target_structure_id_list``. +However, by setting ``target_structure_id_list=None`` we can decrease +storage at all reservoirs in the basin. + +.. code:: ipython3 + + output_directory = output_dir = os.path.join(data_dir, "input_files") + scenario = "1" + # basin name to process + basin_name = "Upper_Colorado" + + # seed value for reproducibility if so desired + seed_value = 1 + + # number of jobs to launch in parallel; -1 is all but 1 processor used + n_jobs = 2 + + # number of samples to generate + n_samples = 1 + + stm.modify_res(output_dir=output_directory, + scenario=scenario, + basin_name=basin_name, + target_structure_id_list=['3603575','3604512'], + seed_value=seed_value, + n_jobs=n_jobs, + n_samples=n_samples, + save_sample=True) + + +Since we are sampling only reductions in storage, we can investigate +behavior with a single sample. We can then load the saved sample to see +the percent reduction in reservoir storage volume that has been applied +to the different reservoirs. The sample indicates that we are reducing +the reservoir storage volume to 86% of the original storage. + +.. code:: ipython3 + + import numpy as np + sample_array = np.load(output_directory+'/res_1-samples_scenario-1.npy') + sample_array + + + + +.. parsed-literal:: + + array([0.86911215]) + + + +Step 3: Read in the New Input Files and Run StateMod : Reservoir Example +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Now that we have created the input files, the next step is to run +StateMod with the new input files. We create a template ``.rsp`` file +(``cm2015B_template_res.rsp``) and swap in the path to the alternative +``.res`` files that are created. Then we run StateMod for the two +scenarios and extract the shortages for Breckenridge. + +.. code:: ipython3 + + # set realization and sample + realization = 1 + sample = np.arange(0, 2, 1) + + # read RSP template + with open(res_template_file) as template_obj: + + # read in file + template_rsp = Template(template_obj.read()) + + for i in sample: + + # create scenario name + scenario = f"S{i}_{realization}" + + # dictionary holding search keys and replacement values to update the template file + d = {"RES": f"../../input_files/cm2015B_{scenario}.res"} + + # update the template + new_rsp = template_rsp.safe_substitute(d) + + # construct simulated scenario directory + simulated_scenario_dir = os.path.join(scenarios_dir_res, scenario) + if not os.path.exists(simulated_scenario_dir): + os.makedirs(simulated_scenario_dir) + + # target rsp file + rsp_file = os.path.join(simulated_scenario_dir, f"cm2015B_{scenario}.rsp") + + # write updated rsp file + with open(rsp_file, "w") as f1: + f1.write(new_rsp) + + # construct simulated basin path + simulated_basin_path = f"cm2015B_{scenario}" + + # run StateMod + print(f"Running: {scenario}") + os.chdir(simulated_scenario_dir) + + subprocess.call([statemod_exe, simulated_basin_path, "-simulate"]) + + #Save output to parquet files + print('creating parquet for ' + scenario) + + output_directory = os.path.join(parquet_dir_res+"/scenario/"+ scenario) + + if not os.path.exists(output_directory): + os.makedirs(output_directory) + + stm.xdd.convert_xdd( + output_path=output_directory, + allow_overwrite=True, + xdd_files=scenarios_dir_res + "/"+ scenario + "/cm2015B_"+scenario+".xdd", + id_subset=['3601008'], + parallel_jobs=2, + preserve_string_dtype=False + ) + + +.. parsed-literal:: + + Running: S0_1 + Parse; Command line argument: + cm2015B_S0_1 -simulate + ________________________________________________________________________ + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 15.00.01 + Last revision date: 2015/10/28 + + ________________________________________________________________________ + + Opening log file cm2015B_S0_1.log + + Subroutine Execut + Subroutine Datinp + + ... + + ________________________________________________________________________ + Execut; Successful Termination + Statem; See detailed messages in file: cm2015B_S0_1.log + Stop 0 + creating parquet for S0_1 + + +.. parsed-literal:: + + 100%|██████████| 1/1 [00:00<00:00, 586.29it/s] + + +.. parsed-literal:: + + Running: S1_1 + Parse; Command line argument: + cm2015B_S1_1 -simulate + ________________________________________________________________________ + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 15.00.01 + Last revision date: 2015/10/28 + + ________________________________________________________________________ + + Opening log file cm2015B_S1_1.log + + Subroutine Execut + Subroutine Datinp + + ________________________________________________________________________ + Datinp; Control File (*.ctl) + + ________________________________________________________________________ + Datinp; River Network File (*.rin) + + ________________________________________________________________________ + Datinp; Reservoir Station File (*.res) + + ________________________________________________________________________ + GetFile; Stopped in GetFile, see the log file (*.log) + Stop 1 + creating parquet for S1_1 + + +.. parsed-literal:: + + 100%|██████████| 1/1 [00:00<00:00, 589.42it/s] + + +Here, we extract the shortages from the Parquet files for the baseline +and alternative states of the world and plot the resulting shortages. + +.. code:: ipython3 + + baseline=pd.read_parquet(data_dir+'/'+'historic_shortages/cm2015B.parquet',engine='pyarrow') + SOW_1=pd.read_parquet(parquet_dir_res+'/scenario/S0_1/cm2015B_S0_1.parquet',engine='pyarrow') + + +.. code:: ipython3 + + baseline["shortage_total"] + + + + +.. parsed-literal:: + + 283920 0. + 283921 0. + 283922 0. + 283923 220. + 283924 201. + ... + 285280 0. + 285281 0. + 285282 0. + 285283 0. + 285284 0. + Name: shortage_total, Length: 1365, dtype: object + + + +.. code:: ipython3 + + baseline=pd.read_parquet(data_dir+'/'+'historic_shortages/cm2015B.parquet',engine='pyarrow') + SOW_1=pd.read_parquet(parquet_dir_res+'/scenario/S0_1/cm2015B_S0_1.parquet',engine='pyarrow') + + #Subtract shortages with respect to the baseline + subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total']],axis=1) + subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1'], axis=1) + subset_df['SOW_1_diff']=subset_df['SOW_1']-subset_df['Baseline'] + + #Plot shortages + fig, ax = plt.subplots() + + ax.scatter(subset_df['Year'], subset_df['SOW_1_diff']) + + plt.xlabel("Year") + plt.ylabel("Shortage (AF)") + plt.title("Change in Breckenridge Shortages from the Baseline") + plt.legend() + + +.. parsed-literal:: + + No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. + + + + +.. parsed-literal:: + + + + + + +.. image:: ../notebooks/output_25_2.png + + +When we plot the shortages to Breckenridge under the alternative SOW +where reservoir storage is reduced across the two reservoirs, we can see +that there are now instances in which Breckenridge experiences larger +shortages than in the baseline case. Given that the town utilizes both +direct diversions and reservoir storage for water supply, this result +suggests that they have less of a bank of water to pull from in the two +reservoirs which increases shortages. However, there are even some cases +where the town experiences surpluses and many cases where the shortages +do not change, demonstrating that there is inherent complexity in the +mapping of reservoir level to shortages, especially when the user has +multiple reservoir accounts. + +Now continue on to Quickstarter Notebook #4 to learn about running +StateMod with new streamflow scenarios across the West Slope basins. + +Notebook References +~~~~~~~~~~~~~~~~~~~ + +Graf, W. L., Wohl, E., Sinha, T., & Sabo, J. L. (2010). Sedimentation +and sustainability of western American reservoirs. Water Resources +Research, 46, W12535. https://doi.org/10.1029/2009WR008836 + +Hadjimichael, A., Quinn, J., Wilson, E., Reed, P., Basdekas, L., Yates, +D., & Garrison, M. (2020). Defining robustness, vulnerabilities, and +consequential scenarios for diverse stakeholder interests in +institutionally complex river basins. Earth’s Future, 8(7), +e2020EF001503. + +.. container:: alert alert-block alert-warning + + Tip: If you are interested in understanding how to apply + ``statemodify`` functions to your own model, take a look at the + source code found in the repository here: + + .. container:: + + :: + + 1. modify_res() + diff --git a/docs/source/notebooks/N4_Streamflow_File_Modification.rst b/docs/source/notebooks/N4_Streamflow_File_Modification.rst new file mode 100644 index 0000000..bbee9ba --- /dev/null +++ b/docs/source/notebooks/N4_Streamflow_File_Modification.rst @@ -0,0 +1,450 @@ +``statemodify`` Quickstarter Notebook #4: Using External Methods to Generate Synthetic Streamflow Traces +-------------------------------------------------------------------------------------------------------- + +In this notebook, we will demonstrate Option 2 that is provided by +``statemodify``, which is the ability to create future states of the +world through external methods rather than manipulating the baseline +time series as we do in Option 1. Here we offer a method to generate +alternative streamflow time series for the five West Slope basins using +the ``modify_xbm_iwr()`` function. + +The Colorado River Basin (CRB) is experiencing a shift to a more arid +climate which will likely be characterized by droughts that are longer +and more severe than what has been experienced historically. The +historic streamflow record is now not a sufficient representation of +future hydroclimate. Thus, we need methods of creating plausible future +streamflow scenarios for the region that can plausibly expand this +historic record. + +In this notebook, we demonstrate how to use the multi-site Hidden Markov +model (HMM)-based synthetic streamflow generator in ``statemodify`` to +create ensembles of plausible future regional streamflow traces for our +five West Slope basins. The HMM is fit to log annual historical +streamflow across the outlet gauges of five key basins on the West Slope +of the state of Colorado that drain into the Colorado River. The model +can then be used to generate streamflow scenarios that envelope the +historical record and expand the representation of natural variability +in the hydroclimate while still preserving the inter-site correlations +across the outlet gauges and at sites within each of the five basins. +The HMM-based model is particularly useful for generating sequences of +streamflow that exhibit long persistence, including decadal to +multidecadal hydrological drought conditions that are scarcely +represented within the historical records. + +By fitting the HMM on the historical time series of the basins, we +create synthetic traces that are stationary and thus give an indication +of the extent of natural variability that characterizes the region. The +HMM can be extended to create non-stationary traces, as shown in +Hadjimichael et al. (2020), but that version of the model is not +currently included in ``statemodify``. + +.. container:: alert alert-block alert-info + + NOTE: Each simulation in this notebook is run for the length of the + historical period (from 1909-2013). If you want to reduce the length + of the simulation, navigate to the ``.ctl`` file and adjust the + ``iystr`` and ``iyend`` variables. For this notebook, this file is + located in: ``data/cm2015_StateMod/StateMod/cm2015.ctl`` + +Step 1: Fit Multi-Site HMM +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: ipython3 + + import argparse + import logging + import os + import pickle + from string import Template + import subprocess + + import matplotlib.pyplot as plt + import numpy as np + import pandas as pd + import statemodify as stm + +First we define directories and associated paths. + +.. code:: ipython3 + + # statemod directory + statemod_dir = "/usr/src/statemodify/statemod_upper_co" + + # root directory of statemod data for the target basin + root_dir = os.path.join(statemod_dir, "src", "main", "fortran") + + # home directory of notebook instance + home_dir = os.path.dirname(os.getcwd()) + + # path to the statemod executable + statemod_exe = os.path.join(root_dir, "statemod") + + # data directory and root name for the target basin + data_dir = os.path.join( + home_dir, + "data", + "cm2015_StateMod", + "StateMod" + ) + + # directory to the target basin input files with root name for the basin + basin_path = os.path.join(data_dir, "cm2015B") + + # scenarios output directory + scenarios_dir = os.path.join(data_dir, "scenarios") + + # path to iwr/xbm file + xbm_iwr_template_file = os.path.join( + home_dir, + "data", + "cm2015B_template_xbm_iwr.rsp" + ) + +We create a function ``hmm_multisite_fit()`` that houses the Python code +to fit the multi-site HMM to annual flow at the outlet gauges of the +five basins. In this function, you can specify the output directory to +store the HMM parameters. + +.. code:: ipython3 + + #Make directory to store HMM parameters + + output_dir = os.path.join(data_dir, "HMM_parameters") + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + n_basins = 5 + + # choice to save parameters to NumPy array files + save_parameters = True + + fit_array_dict = stm.hmm_multisite_fit(n_basins=n_basins, + save_parameters=save_parameters, + output_directory=output_dir) + + # unpack output dictionary + unconditional_dry = fit_array_dict["unconditional_dry"] + unconditional_wet = fit_array_dict["unconditional_wet"] + logAnnualQ_h = fit_array_dict["logAnnualQ_h"] + transition_matrix = fit_array_dict["transition_matrix"] + covariance_matrix_wet = fit_array_dict["covariance_matrix_wet"] + covariance_matrix_dry = fit_array_dict["covariance_matrix_dry"] + wet_state_means = fit_array_dict["wet_state_means"] + dry_state_means = fit_array_dict["dry_state_means"] + + +Step 2: Sample from multi-site HMM +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We then use the ``hmm_multisite_sample()`` function to sample from the +HMM 100 times to develop 100 alternative, 105-year traces of streamflow +at the outlet gauge of each basin and we save each trace in a .csv file +in the ``HMM_Runs`` folder. + +.. code:: ipython3 + + #Create a folder to store the runs + + output_dir = os.path.join(data_dir, "HMM_Runs") + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + # using the outputs of the fit function above; this function write output sample files to the output directory + stm.hmm_multisite_sample(logAnnualQ_h, + transition_matrix, + unconditional_dry, + dry_state_means, + wet_state_means, + covariance_matrix_dry, + covariance_matrix_wet, + n_basins=n_basins, + output_directory=output_dir) + +Then one can plot flow duration curves (FDCs) of the annual +synthetically generated flow at each basin compared to the the +historical record. We see that the HMM is enveloping the historical +record and also expanding it around it, particularly around the tails of +the distribution, which will lead to more instances of extreme flood and +drought events. + +.. code:: ipython3 + + stm.plot_flow_duration_curves(flow_realizations_directory=output_dir, + save_figure=True,output_directory=output_dir, + figure_name= 'FDC', + dpi= 300) + + + +.. image:: ../notebooks/output_15_0.png + + +Step 3: Modify StateMod Input Files for Exploratory Analyses- Streamflow Example +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In order for the HMM to be used in conjunction with StateMod, we utilize +a statistical disaggregation technique to disaggregate the synthetically +generated outlet gauge flow to the upstream nodes and also from an +annual to monthly time scale. The synthetic log-space annual flows are +converted to real space and temporally downscaled to monthly flows using +a modification of the proportional scaling method used by Nowak et +al. (2010). First, a historical year is probabilistically selected based +on its “nearness” to the synthetic flow at the last node in terms of +annual total. The shifted monthly flows at the last node are then +downscaled to all other nodes using the same ratios of monthly flows at +the upstream nodes to the last node as in the historically selected +year.Though not demonstrated in this notebook, the irrigation demands +(in the +```.iwr`` `__ +file) are also inherently tied to the generation of the streamflow, as +irrigation demands will increase in dry years. Thus, a regression is fit +across historical irrigation anomalies and historical annual flow +anomalies and the appropriate irrigation anomaly is determined from this +regression for every synthetically generated flow anomaly. More +information on this method can be found in Hadjimichael et al., 2020. +All of this functionality is embedded in the ``modify_xbm_iwr()`` +function. + +.. code:: ipython3 + + #Make directory to store input files + + output_dir = os.path.join(data_dir, "input_files") + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + + flow_realizations_directory = os.path.join(data_dir, "HMM_Runs") + + scenario = "1" + + # basin name to process + basin_name = "Upper_Colorado" + + # seed value for reproducibility if so desired + seed_value = 123 + + # number of jobs to launch in parallel; -1 is all but 1 processor used + n_jobs = 2 + + # number of samples to generate (how many new xbm and iwr files); produces an IWR multiplier + n_samples = 1 + + # generate a batch of files using generated LHS + stm.modify_xbm_iwr(output_dir=output_dir, + flow_realizations_directory=flow_realizations_directory, + scenario=scenario, + basin_name=basin_name, + seed_value=seed_value, + n_jobs=n_jobs, + n_samples=n_samples, + save_sample=True, + randomly_select_flow_sample=True) + +Step 4: Read in the New Input Files and Run StateMod : Streamflow Example +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Now that we have created the new files, the next step is to run them +through StateMod. We create a template ``.rsp`` file +(``cm2015B_template_xbm_iwr.rsp``) and swap in the path to the +alternative +```.xbm`` `__ +and ``.iwr`` files that are created. Then we run StateMod for the single +scenario and one can then go on and extract shortages or reservoir +levels. + +.. container:: alert alert-block alert-info + + NOTE In order to expedite simulations for the Upper Colorado dataset, + make sure to turn off “Reoperation” mode. You can do so by opening + ``/home/jovyan/data/cm2015_StateMod/StateMod/cm2015.ctl``, navigating + to the ``ireopx`` entry and changing the value from “0” to “10”. + +.. code:: ipython3 + + # set realization and sample + realization = 1 + sample = np.arange(0, 1, 1) + + # read RSP template + with open(xbm_iwr_template_file) as template_obj: + + # read in file + template_rsp = Template(template_obj.read()) + + for i in sample: + + # create scenario name + scenario = f"S{i}_{realization}" + + # dictionary holding search keys and replacement values to update the template file + d = {"XBM": f"../../input_files/cm2015B_{scenario}.xbm","IWR": f"../../input_files/cm2015B_{scenario}.iwr"} + + # update the template + new_rsp = template_rsp.safe_substitute(d) + + # construct simulated scenario directory + simulated_scenario_dir = os.path.join(scenarios_dir, scenario) + if not os.path.exists(simulated_scenario_dir): + os.makedirs(simulated_scenario_dir) + + # target rsp file + rsp_file = os.path.join(simulated_scenario_dir, f"cm2015B_{scenario}.rsp") + + # write updated rsp file + with open(rsp_file, "w") as f1: + f1.write(new_rsp) + + # construct simulated basin path + simulated_basin_path = f"cm2015B_{scenario}" + + # run StateMod + print(f"Running: {scenario}") + os.chdir(simulated_scenario_dir) + + subprocess.call([statemod_exe, simulated_basin_path, "-simulate"]) + + + +.. parsed-literal:: + + Running: S0_1 + Parse; Command line argument: + cm2015B_S0_1 -simulate + ________________________________________________________________________ + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 15.00.01 + Last revision date: 2015/10/28 + + ________________________________________________________________________ + + Opening log file cm2015B_S0_1.log + + Subroutine Execut + Subroutine Datinp + + ... + + ________________________________________________________________________ + Execut; Successful Termination + Statem; See detailed messages in file: cm2015B_S0_1.log + Stop 0 + + +It’s easiest to see the value of generating multiple streamflow +scenarios if we run 100-1000 scenarios through StateMod. However, this +container does not have the resources to support exploratory modeling at +this scale. So we run these simulations externally and below, we read in +the ``.xre`` files with the ``read_xre()`` helper function and show the +distribution of the reservoir levels that are observed across Lake +Granby in the Upper Colorado under the 100 simulated scenarios versus +the historical 105-year period. + +.. code:: ipython3 + + # Example with Granby Lake + zip_file_path = os.path.join(home_dir, 'data', 'Granby_Dataset.zip') + final_directory = os.path.join(home_dir, 'data/') + + !unzip $zip_file_path -d $final_directory + granby_hmm, granby_hist, granby_hist_mean, granby_hist_1p = stm.read_xre(os.path.join(home_dir,"data/Upper_Colorado/"), 'Granby') + + # Plot quantiles + stm.plot_res_quantiles(granby_hmm, granby_hist_mean, 'Lake Granby') + + + +.. image:: ../notebooks/output_24_0.png + + +Here, we plot the monthly reservoir storage quantiles across each month +of the year. The shading corresponds to the larger 100-member sample +from the HMM and the dotted black line corresponds to the monthly +average storage across the historical 105-year record. Importantly, the +HMM is expanding the distribution of reservoir storages, particularly +creating both larger and smaller storages, meaning that we are capturing +reservoir levels under a broader range of wetter and drier conditions +that could have implications for shortages for users. + +We can also plot the range of monthly storages from the HMM and +historical period as box plots for an alternative comparison using the +``plot_reservoir_boxes()`` helper function. + +.. code:: ipython3 + + # using the output of the above `read_xre` function as inputs + stm.plot_reservoir_boxes(granby_hmm, granby_hist, 'Lake Granby') + + + +.. image:: ../notebooks/output_27_0.png + + +Here, the blue box plots correspond to the HMM-generated reservoir +storages and the orange box plots correspond to the historical monthly +dataset. The black circles represent outliers. As illustrated in the +quantile plot above as well, for all months, the HMM is creating a wider +distribution of reservoir storages, and tends to be able to encompass +even historical outliers. Remember that the HMM has only been fit on the +historical dataset. Thus, the HMM can provide an estimate of the expanse +of reservoir storages that can be expected just within the range of +natural variability, which is quite large! Particularly, the HMM is +creating many more instances of drier scenarios and lower reservoir +levels which can be very useful for informing drought vulnerability +assessments. + +.. container:: alert alert-block alert-info + + NOTE: If you are curious to learn more about HMM-based synthetic + streamflow generation, including model fitting, validation, and + applications, please refer to the following resources: + + .. container:: + + :: + + 1. Fitting Hidden Markov Models: Background and Methods + + .. container:: + + :: + + 2. Fitting Hidden Markov Models: Sample Scripts + + .. container:: + + :: + + 3. A Hidden-Markov Modeling Approach to Creating Synthetic Streamflow Scenarios Tutorial + +Notebook Specific References +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Nowak, K., Prairie, J., Rajagopalan, B., & Lall, U. (2010). A +nonparametric stochastic approach for multisite disaggregation of annual +to daily streamflow. Water resources research, 46(8). + +Hadjimichael, A., Quinn, J., Wilson, E., Reed, P., Basdekas, L., Yates, +D., & Garrison, M. (2020). Defining robustness, vulnerabilities, and +consequential scenarios for diverse stakeholder interests in +institutionally complex river basins. Earth’s Future, 8(7), +e2020EF001503. + +.. container:: alert alert-block alert-warning + + Tip: If you are interested in understanding how to apply + ``statemodify`` functions to your own model, take a look at the + source code found in the repository here: + + .. container:: + + :: + + 1. modify_xbm_iwr() + diff --git a/docs/source/notebooks/N5_Batch_Modification.rst b/docs/source/notebooks/N5_Batch_Modification.rst new file mode 100644 index 0000000..7389d78 --- /dev/null +++ b/docs/source/notebooks/N5_Batch_Modification.rst @@ -0,0 +1,273 @@ +``statemodify`` Quickstarter Notebook #5 : Sampling Multiple Uncertainties +-------------------------------------------------------------------------- + +In the prior notebooks, we sampled just one type of uncertainty at a +time to demonstrate how a single adjustment in the selected input file +leads to a verifiable change in output so that we can demonstrate that +``statemodify`` is working as expected. It is much harder to make sense +of the relative importance of uncertain drivers unless we conduct a +formal sensitivity analysis, which lies outside of the bounds of this +tutorial. However, it is very likely that many of these uncertainties +will be present and of interest in any given future for the region. +Thus, this notebook is used to demonstrate how to do a Latin hypercube +sample simultaneously across multiple uncertainties in a given basin +using the ``modify_batch()`` function. + +.. code:: ipython3 + + import argparse + import logging + import os + import pickle + from string import Template + import subprocess + + import matplotlib.pyplot as plt + import numpy as np + import pandas as pd + import statemodify as stm + +.. container:: alert alert-block alert-info + + NOTE: Each simulation in this notebook is run for the length of the + historical period (from 1909-2013). If you want to reduce the length + of the simulation, navigate to the ``.ctl`` file and adjust the + ``iystr`` and ``iyend`` variables. For this notebook, this file is + located in: ``data/cm2015_StateMod/StateMod/cm2015.ctl`` + +.. code:: ipython3 + + # statemod directory + statemod_dir = "/usr/src/statemodify/statemod_upper_co" + + # root directory of statemod data for the target basin + root_dir = os.path.join(statemod_dir, "src", "main", "fortran") + + # home directory of notebook instance + home_dir = os.path.dirname(os.getcwd()) + + # path to the statemod executable + statemod_exe = os.path.join(root_dir, "statemod") + + # data directory and root name for the target basin + data_dir = os.path.join( + home_dir, + "data", + "cm2015_StateMod", + "StateMod" + ) + + # directory to the target basin input files with root name for the basin + basin_path = os.path.join(data_dir, "cm2015B") + + # scenarios output directory + scenarios_dir = os.path.join(data_dir, "scenarios") + + #parquet output directory + parquet_dir=os.path.join(data_dir, "parquet") + + + # path to template file + multi_template_file = os.path.join( + home_dir, + "data", + "cm2015B_template_multi.rsp" + ) + +Step 1: Creating a Sample Across Multiple Uncertainties +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In this example, we create a global Latin hypercube sample across 3 +example uncertainties that we are interested in for the Upper Colorado: +evaporation at reservoirs, modification of water rights, and demands. +The form of the ``modify_batch()`` function is similiar to those +presented in the last notebooks; however, now, a problem dictionary +stores the names of the respective ``statemodify`` functions +(``modify_eva``, ``modify_ddr``, ``modify_ddm``) that will now be +applied simultaneously. The Latin hypercube sample is conducted with +respect to the ``bounds`` listed and the resulting multipliers or +additives are applied to the target IDs listed. + +.. container:: alert alert-block alert-info + + NOTE: If you are interested in creating an alternative ``.ddr`` file + that does not sample decrees, only adjusts the water rights, then you + will need to write ``None`` in the “bounds” parameter as we do below. + If you include bounds, then decrees as well as water rights will be + adjusted simultaneously. + +.. code:: ipython3 + + import statemodify as stm + + # variables that apply to multiple functions + output_dir = os.path.join(data_dir, "input_files") + basin_name = "Upper_Colorado" + scenario = "1" + seed_value = 77 + + # problem dictionary + problem_dict = { + "n_samples": 1, + 'num_vars': 3, + 'names': ['modify_eva', 'modify_ddr', 'modify_ddm'], + 'bounds': [ + [-0.5, 1.0], + None, + [0.5, 1.0] + ], + # additional settings for each function + "modify_eva": { + "seed_value": seed_value, + "output_dir": output_dir, + "scenario": scenario, + "basin_name": basin_name, + "query_field": "id", + "ids": ["10008", "10009"] + }, + "modify_ddr": { + "seed_value": seed_value, + "output_dir": output_dir, + "scenario": scenario, + "basin_name": basin_name, + "query_field": "id", + "ids": ["3600507.01", "3600507.02"], + "admin": [1, None], + "on_off": [1, 1] + }, + "modify_ddm": { + "seed_value": seed_value, + "output_dir": output_dir, + "scenario": scenario, + "basin_name": basin_name, + "query_field": "id", + "ids": ["3600507", "3600603"] + } + } + + # run in batch + fn_parameter_dict = stm.modify_batch(problem_dict=problem_dict) + + +.. parsed-literal:: + + Running modify_eva + Running modify_ddr + Running modify_ddm + + +Step 2: Running a Simulation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Now that we have developed the samples, we need to adjust our template +file to take in the additional uncertainties and then we can run our +simulation! Note that in contrast to the other notebooks, we are +changing the “EVA”, “DDM”, and “DDR” entries in the ``.rsp`` file at the +same time, running the simulation, and then extracting the shortages for +a specific user (ID: 3601008). + +.. container:: alert alert-block alert-info + + NOTE In order to expedite simulations for the Upper Colorado dataset, + make sure to turn off “Reoperation” mode. You can do so by opening + ``/home/jovyan/data/cm2015_StateMod/StateMod/cm2015.ctl``, navigating + to the ``ireopx`` entry and changing the value from “0” to “10”. + +.. code:: ipython3 + + # set realization and sample + realization = 1 + sample = np.arange(0, 1, 1) + + # read RSP template + with open(multi_template_file) as template_obj: + + # read in file + template_rsp = Template(template_obj.read()) + + for i in sample: + + # create scenario name + scenario = f"S{i}_{realization}" + + # dictionary holding search keys and replacement values to update the template file + d = {"EVA": f"../../input_files/cm2015B_{scenario}.eva","DDM": f"../../input_files/cm2015B_{scenario}.ddm","DDR": f"../../input_files/cm2015B_{scenario}.ddr"} + + # update the template + new_rsp = template_rsp.safe_substitute(d) + + # construct simulated scenario directory + simulated_scenario_dir = os.path.join(scenarios_dir, scenario) + if not os.path.exists(simulated_scenario_dir): + os.makedirs(simulated_scenario_dir) + + # target rsp file + rsp_file = os.path.join(simulated_scenario_dir, f"cm2015B_{scenario}.rsp") + + # write updated rsp file + with open(rsp_file, "w") as f1: + f1.write(new_rsp) + + # construct simulated basin path + simulated_basin_path = f"cm2015B_{scenario}" + + # run StateMod + print(f"Running: {scenario}") + os.chdir(simulated_scenario_dir) + + subprocess.call([statemod_exe, simulated_basin_path, "-simulate"]) + + #Save output to parquet files + print('creating parquet for ' + scenario) + + output_directory = os.path.join(parquet_dir+ "/scenario/"+ scenario) + + if not os.path.exists(output_directory): + os.makedirs(output_directory) + + stm.xdd.convert_xdd(output_path=output_directory,allow_overwrite=False,xdd_files=scenarios_dir + "/"+ scenario + "/cm2015B_"+scenario+".xdd",id_subset=['3601008'],parallel_jobs=2) + + + +.. parsed-literal:: + + Running: S0_1 + Parse; Command line argument: + cm2015B_S0_1 -simulate + ________________________________________________________________________ + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 15.00.01 + Last revision date: 2015/10/28 + + ________________________________________________________________________ + + Opening log file cm2015B_S0_1.log + + Subroutine Execut + Subroutine Datinp + + ... + + + +At the end of the simulation, the output is the file, +``cm2015B_S0_1.parquet``, which now contains the shortages for the +target ID for the length of the simulation. The user can then proceed to +do similiar analyses on water shortages that have been demonstrated in +the prior notebooks. + +.. container:: alert alert-block alert-warning + + Tip: If you are interested in understanding how to apply + ``statemodify`` functions to your own model, take a look at the + source code found in the repository here: + + .. container:: + + :: + + 1. modify_batch() + diff --git a/docs/source/notebooks/output_12_1.png b/docs/source/notebooks/output_12_1.png new file mode 100644 index 0000000000000000000000000000000000000000..950cb47b4e572388e7ecbd3c78c9e9fcb944891f GIT binary patch literal 24108 zcmdSBc|6wt`Y!qy6B0?L3{jLqRLEEoDrFv$nKBj05HdxIOqEnBna4~Kp^Sy1%u{5D zLWancG3Wa9UFWyn`k*`NhH#y6RJvD zBoes{i9}XQLydnq>kyrU|2yn*+`vWK@q)`$^DE~`r_Eh1UvzZ2XludicK(X9t)s&M z5h)SzeY`daV$K>>6+XrW} z&jx>=x4ZE3_SdqzuO+u0JGhl~TNK+7x8c=41Ck@s`@|^@e5@K@a{oB=)vLj}Vd-ha z$o{Frv8VZH{7HQHbzs*a7qTGynhUOEU}9p5K13~}q@-lk!8XCj#B}$pW$rQj^+MSH zfP*djF1EmjxLpcMu#5tOqoAEO{ 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+++++++------- .../N4_Streamflow_File_Modification.rst | 121 +++++----- .../notebooks/N5_Batch_Modification.rst | 85 ++++--- ...Demand_WaterRights_File_Modification.ipynb | 201 ++++++++-------- .../N2_Evaporation_File_Modification.ipynb | 192 ++++++++------- .../N3_Reservoir_File_Modification.ipynb | 132 +++++----- .../N4_Streamflow_File_Modification.ipynb | 124 +++++----- notebooks/N5_Batch_Modification.ipynb | 82 +++---- statemodify/xdd.py | 8 +- 11 files changed, 773 insertions(+), 765 deletions(-) diff --git a/docs/source/notebooks/N1_Demand_WaterRights_File_Modification.rst b/docs/source/notebooks/N1_Demand_WaterRights_File_Modification.rst index 6302a1d..43a7fee 100644 --- a/docs/source/notebooks/N1_Demand_WaterRights_File_Modification.rst +++ b/docs/source/notebooks/N1_Demand_WaterRights_File_Modification.rst @@ -58,10 +58,10 @@ conditions. import pickle from string import Template import subprocess - + import matplotlib.pyplot as plt import numpy as np - import pandas as pd + import pandas as pd import statemodify as stm .. container:: alert alert-block alert-info @@ -79,16 +79,16 @@ conditions. # statemod directory statemod_dir = "/usr/src/statemodify/statemod_gunnison_sjd" - + # root directory of statemod data for the target basin root_dir = os.path.join(statemod_dir, "src", "main", "fortran") - + # home directory of notebook instance home_dir = os.path.dirname(os.getcwd()) - + # path to the statemod executable statemod_exe = os.path.join(root_dir, "statemod-17.0.3-gfortran-lin-64bit-o3") - + # data directory and root name for the target basin data_dir = os.path.join( home_dir, @@ -97,25 +97,25 @@ conditions. "sj2015_StateMod_modified", "StateMod" ) - + # directory to the target basin input files with root name for the basin basin_path = os.path.join(data_dir, "sj2015B") - + # scenarios output directory scenarios_dir_ddm = os.path.join(data_dir, "scenarios_ddm") scenarios_dir_ddr = os.path.join(data_dir, "scenarios_ddr") - + # parquet files output directory parquet_dir_ddm = os.path.join(data_dir, "parquet_ddm") parquet_dir_ddr = os.path.join(data_dir, "parquet_ddr") - + # path to ddm and ddr template file ddm_template_file = os.path.join( home_dir, "data", "sj2015B_template_ddm.rsp" ) - + ddr_template_file = os.path.join( home_dir, "data", @@ -131,19 +131,19 @@ conditions. .. parsed-literal:: - Startup log file for messages to this point: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/sj2015B.rsp + Startup log file for messages to this point: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/sj2015B.rsp Closing startup log file: statem.log - Opening dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/sj2015B.log + Opening dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/sj2015B.log ________________________________________________________________________ - - StateMod - State of Colorado - Water Supply Planning Model - - Version: 17.0.3 + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 17.0.3 Last revision date: 2021/09/12 - + ________________________________________________________________________ - + Subroutine Execut Subroutine Datinp @@ -151,7 +151,7 @@ conditions. ________________________________________________________________________ Execut; Successful Termination - Statem; See detailed messages in dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/sj2015B.log + Statem; See detailed messages in dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/sj2015B.log Stop 0 Once StateMod has run successfully, we can now extract user shortages @@ -167,16 +167,16 @@ and saved. .. code:: ipython3 - #Extract shortages using statemodify convert_xdd() function - - # create a directory to store the historical shortages + #Extract shortages using statemodify convert_xdd() function + + # create a directory to store the historical shortages output_dir = os.path.join(data_dir, "historic_shortages") - + # create a directory to store the new files in if it does not exist output_directory = os.path.join(data_dir, "historic_shortages") if not os.path.exists(output_directory): os.makedirs(output_directory) - + stm.xdd.convert_xdd( # path to a directory where output .parquet files should be written output_path=output_dir, @@ -212,11 +212,11 @@ and saved. .dataframe tbody tr th:only-of-type { vertical-align: middle; } - + .dataframe tbody tr th { vertical-align: top; } - + .dataframe thead th { text-align: right; } @@ -529,11 +529,11 @@ We can then take these shortages and plot them for our list of users. .. code:: ipython3 fig, ax = plt.subplots() - + for name, group in data.groupby('structure_id'): ax.scatter( group['year'], group['shortage_total'], label=name) - + plt.xlabel("Year") plt.ylabel("Shortage (AF)") plt.legend() @@ -585,29 +585,29 @@ the ``input_files`` directory. "ids": ["2900501", "2900519","2900555"], "bounds": [0.5, 1.5] } - + output_directory = output_dir = os.path.join(data_dir, "input_files") - + scenario = "1" - + # the number of samples you wish to generate n_samples = 2 - + # seed value for reproducibility if so desired seed_value = 1 - + # number of rows to skip in file after comment skip_rows = 1 - + # name of field to query query_field = "id" - + # number of jobs to launch in parallel; -1 is all but 1 processor used n_jobs = -1 - + # basin to process basin_name = "San_Juan" - + # generate a batch of files using generated LHS stm.modify_ddm( modify_dict=setup_dict, @@ -669,53 +669,53 @@ scenario will take approximately 4 minutes. # set realization and sample realization = 1 sample = np.arange(0, 2, 1) - + # read RSP template with open(ddm_template_file) as template_obj: - + # read in file template_rsp = Template(template_obj.read()) - + for i in sample: - + # create scenario name scenario = f"S{i}_{realization}" - + # dictionary holding search keys and replacement values to update the template file d = {"DDM": f"../../input_files/sj2015B_{scenario}.ddm"} - + # update the template new_rsp = template_rsp.safe_substitute(d) - + # construct simulated scenario directory simulated_scenario_dir = os.path.join(scenarios_dir_ddm, scenario) if not os.path.exists(simulated_scenario_dir): os.makedirs(simulated_scenario_dir) - + # target rsp file rsp_file = os.path.join(simulated_scenario_dir, f"sj2015B_{scenario}.rsp") - + # write updated rsp file with open(rsp_file, "w") as f1: f1.write(new_rsp) - + # construct simulated basin path simulated_basin_path = os.path.join(simulated_scenario_dir, f"sj2015B_{scenario}") - + # run StateMod print(f"Running: {scenario}") os.chdir(simulated_scenario_dir) - + subprocess.call([statemod_exe, simulated_basin_path, "-simulate"]) - - #Save output to parquet files + + #Save output to parquet files print('creating parquet for ' + scenario) - + output_directory = os.path.join(parquet_dir_ddm+"/scenario/"+ scenario) - + if not os.path.exists(output_directory): os.makedirs(output_directory) - + stm.xdd.convert_xdd( output_path=output_directory, allow_overwrite=False, @@ -730,19 +730,19 @@ scenario will take approximately 4 minutes. .. parsed-literal:: Running: S0_1 - Startup log file for messages to this point: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddm/S0_1/sj2015B_S0_1.rsp + Startup log file for messages to this point: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddm/S0_1/sj2015B_S0_1.rsp Closing startup log file: statem.log - Opening dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddm/S0_1/sj2015B_S0_1.log + Opening dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddm/S0_1/sj2015B_S0_1.log ________________________________________________________________________ - - StateMod - State of Colorado - Water Supply Planning Model - - Version: 17.0.3 + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 17.0.3 Last revision date: 2021/09/12 - + ________________________________________________________________________ - + Subroutine Execut Subroutine Datinp @@ -750,7 +750,7 @@ scenario will take approximately 4 minutes. ________________________________________________________________________ Execut; Successful Termination - Statem; See detailed messages in dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddm/S0_1/sj2015B_S0_1.log + Statem; See detailed messages in dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddm/S0_1/sj2015B_S0_1.log Stop 0 creating parquet for S0_1 @@ -791,19 +791,19 @@ respect to the shortages received in the baseline case. baseline=pd.read_parquet(data_dir+'/historic_shortages/sj2015B.parquet',engine='pyarrow') SOW_1=pd.read_parquet(parquet_dir_ddm+'/scenario/S0_1/sj2015B_S0_1.parquet',engine='pyarrow') SOW_2=pd.read_parquet(parquet_dir_ddm+'/scenario/S1_1/sj2015B_S1_1.parquet',engine='pyarrow') - + # Subtract shortages with respect to the baseline subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total'],SOW_2['shortage_total']],axis=1) subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1','SOW_2'], axis=1) subset_df['SOW_1_diff'] = subset_df['SOW_1']-subset_df['Baseline'] subset_df['SOW_2_diff'] = subset_df['SOW_2']-subset_df['Baseline'] - + # Plot shortages fig, ax = plt.subplots() - + ax.scatter(subset_df['Year'], subset_df['SOW_1_diff'],label='Decreased Demand') ax.scatter(subset_df['Year'], subset_df['SOW_2_diff'],label='Increased Demand') - + plt.xlabel("Year") plt.ylabel("Shortage (AF)") plt.title("Change in Shortages from the Baseline") @@ -846,36 +846,36 @@ rank to 1. setup_dict = { # ids can either be 'struct' or 'id' values "ids": ["2900501"], - + # turn id on or off completely or for a given period # if 0 = off, 1 = on, YYYY = on for years >= YYYY, -YYYY = off for years > YYYY; see file header "on_off": [1], - + # apply rank of administrative order where 0 is lowest (senior) and n is highest (junior); None is no change "admin": [1], } - + output_directory = os.path.join(data_dir, "input_files") scenario = "1" - + # the number of samples you wish to generate n_samples = 1 - + # seed value for reproducibility if so desired seed_value = 1 - + # number of rows to skip in file after comment skip_rows = 0 - + # name of field to query query_field = "struct" - + # number of jobs to launch in parallel; -1 is all but 1 processor used n_jobs = -1 - + # basin to process basin_name = "San_Juan" - + # generate a batch of files using generated LHS stm.modify_ddr( modify_dict=setup_dict, @@ -907,53 +907,53 @@ using the .\ ``ddr`` template file. # set realization and sample realization = 1 sample = np.arange(0, 1, 1) - + # read RSP template with open(ddr_template_file) as template_obj: - + # read in file template_rsp = Template(template_obj.read()) - + for i in sample: - + # create scenario name scenario = f"S{i}_{realization}" - + # dictionary holding search keys and replacement values to update the template file d = {"DDR": f"../../input_files/sj2015B_{scenario}.ddr"} - + # update the template new_rsp = template_rsp.safe_substitute(d) - + # construct simulated scenario directory simulated_scenario_dir = os.path.join(scenarios_dir_ddr, scenario) if not os.path.exists(simulated_scenario_dir): os.makedirs(simulated_scenario_dir) - + # target rsp file rsp_file = os.path.join(simulated_scenario_dir, f"sj2015B_{scenario}.rsp") - + # write updated rsp file with open(rsp_file, "w") as f1: f1.write(new_rsp) - + # construct simulated basin path simulated_basin_path = os.path.join(simulated_scenario_dir, f"sj2015B_{scenario}") - + # run StateMod print(f"Running: {scenario}") os.chdir(simulated_scenario_dir) - + subprocess.call([statemod_exe, simulated_basin_path, "-simulate"]) - - #Save output to parquet files + + #Save output to parquet files print('creating parquet for ' + scenario) - + output_directory = os.path.join(parquet_dir_ddr+"/scenario/"+ scenario) - + if not os.path.exists(output_directory): os.makedirs(output_directory) - + stm.xdd.convert_xdd( output_path=output_directory, allow_overwrite=False, @@ -968,27 +968,27 @@ using the .\ ``ddr`` template file. .. parsed-literal:: Running: S0_1 - Startup log file for messages to this point: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddr/S0_1/sj2015B_S0_1.rsp + Startup log file for messages to this point: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddr/S0_1/sj2015B_S0_1.rsp Closing startup log file: statem.log - Opening dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddr/S0_1/sj2015B_S0_1.log + Opening dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddr/S0_1/sj2015B_S0_1.log ________________________________________________________________________ - - StateMod - State of Colorado - Water Supply Planning Model - - Version: 17.0.3 + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 17.0.3 Last revision date: 2021/09/12 - + ________________________________________________________________________ - + Subroutine Execut Subroutine Datinp - + ... ________________________________________________________________________ Execut; Successful Termination - Statem; See detailed messages in dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddr/S0_1/sj2015B_S0_1.log + Statem; See detailed messages in dataset log file: /home/jovyan/data/sj2015_StateMod_modified/sj2015_StateMod_modified/StateMod/scenarios_ddr/S0_1/sj2015B_S0_1.log Stop 0 creating parquet for S0_1 @@ -1001,17 +1001,17 @@ with respect to the baseline shortages. # Read in raw parquet files baseline=pd.read_parquet(data_dir+'/historic_shortages/sj2015B.parquet',engine='pyarrow') SOW_1=pd.read_parquet(parquet_dir_ddr+ '/scenario/S0_1/sj2015B_S0_1.parquet',engine='pyarrow') - + # Subtract shortages with respect to the baseline subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total']],axis=1) subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1'], axis=1) subset_df['diff']=subset_df['SOW_1']-subset_df['Baseline'] - + # Plot shortages fig, ax = plt.subplots() - + ax.scatter(subset_df['Year'], subset_df['diff']) - + plt.xlabel("Year") plt.ylabel("Shortage (AF)") plt.title("Change in Shortages from the Baseline") @@ -1052,4 +1052,3 @@ reservoir evaporation modification fuction. :: 2. modify_ddr() - diff --git a/docs/source/notebooks/N2_Evaporation_File_Modification.rst b/docs/source/notebooks/N2_Evaporation_File_Modification.rst index fc96c12..7f347df 100644 --- a/docs/source/notebooks/N2_Evaporation_File_Modification.rst +++ b/docs/source/notebooks/N2_Evaporation_File_Modification.rst @@ -25,10 +25,10 @@ dataset for the Gunnison. import pickle from string import Template import subprocess - + import matplotlib.pyplot as plt import numpy as np - import pandas as pd + import pandas as pd import statemodify as stm .. container:: alert alert-block alert-info @@ -44,16 +44,16 @@ dataset for the Gunnison. # statemod directory statemod_dir = "/usr/src/statemodify/statemod_gunnison_sjd" - + # root directory of statemod data for the target basin root_dir = os.path.join(statemod_dir, "src", "main", "fortran") - + # home directory of notebook instance home_dir = os.path.dirname(os.getcwd()) - + # path to the statemod executable statemod_exe = os.path.join(root_dir, "statemod-17.0.3-gfortran-lin-64bit-o3") - + # data directory and root name for the target basin data_dir = os.path.join( home_dir, @@ -62,13 +62,13 @@ dataset for the Gunnison. "gm2015_StateMod_modified", "StateMod" ) - + # directory to the target basin input files with root name for the basin basin_path = os.path.join(data_dir, "gm2015B") - + # scenarios output directory scenarios_dir = os.path.join(data_dir, "scenarios") - + # path to eva template file eva_template_file = os.path.join( home_dir, @@ -85,27 +85,27 @@ dataset for the Gunnison. .. parsed-literal:: - Startup log file for messages to this point: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015B.rsp + Startup log file for messages to this point: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015B.rsp Closing startup log file: statem.log - Opening dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015B.log + Opening dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015B.log ________________________________________________________________________ - - StateMod - State of Colorado - Water Supply Planning Model - - Version: 17.0.3 + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 17.0.3 Last revision date: 2021/09/12 - + ________________________________________________________________________ - + Subroutine Execut Subroutine Datinp - + ... ________________________________________________________________________ Execut; Successful Termination - Statem; See detailed messages in dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015B.log + Statem; See detailed messages in dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/gm2015B.log Stop 0 @@ -122,21 +122,21 @@ for supplying emergency water to Lake Powell. .. code:: ipython3 - # create a directory to store the historical reservoir levels at Blue Mesa + # create a directory to store the historical reservoir levels at Blue Mesa output_dir = os.path.join(data_dir, "historic_reservoir_levels") - + if not os.path.exists(output_dir): os.makedirs(output_dir) - + # path the the xre file xre_file = os.path.join(data_dir, "gm2015B.xre") - + # structure ID for reservoir of interest - structure_ID = '6203532' - + structure_ID = '6203532' + # name of the reservoir - structure_name = 'Blue_Mesa' - + structure_name = 'Blue_Mesa' + # extract the target info into a Pandas data frame df = stm.extract_xre_data(structure_name=structure_name, structure_id=structure_ID, @@ -153,16 +153,16 @@ reservoir storage. .. code:: ipython3 output_xre_file = os.path.join(output_dir, "Blue_Mesa_xre_data.csv") - + # read output data into a data frame df = pd.read_csv( - output_xre_file, + output_xre_file, usecols=['Year','Init. Storage'], - index_col=False) - + index_col=False) + # calculate the annual average df = df.groupby('Year').mean().reset_index() - + df @@ -176,11 +176,11 @@ reservoir storage. .dataframe tbody tr th:only-of-type { vertical-align: middle; } - + .dataframe tbody tr th { vertical-align: top; } - + .dataframe thead th { text-align: right; } @@ -264,9 +264,9 @@ the 1930s dustbowl and 1950s drought and the severe early 2002 drought). .. code:: ipython3 fig, ax = plt.subplots() - + plt.plot(df['Year'], df['Init. Storage']) - + plt.title("Blue Mesa Storage") plt.xlabel("Year") plt.ylabel("Reservoir Storage (AF)") @@ -304,38 +304,38 @@ create 2 alternative states of the world and store them in the .. code:: ipython3 - # a dictionary to describe what you want to modify and the bounds for the Latin hypercube sample. + # a dictionary to describe what you want to modify and the bounds for the Latin hypercube sample. setup_dict = { "ids": ['10011'], "bounds": [-0.5, 1.0] } - + # create a directory to store the new files in if it does not exist output_directory = os.path.join(data_dir, "input_files") if not os.path.exists(output_directory): os.makedirs(output_directory) - + # scenario name scenario = "1" - + # the number of samples you wish to generate n_samples = 2 - + # seed value for reproducibility if so desired seed_value = 1 - + # number of rows to skip in file after comment skip_rows = 1 - + # name of field to query query_field = "id" - + # number of jobs to launch in parallel; -1 is all but 1 processor used n_jobs = -1 - + # basin to process basin_name = "Gunnison" - + # generate a batch of files using generated LHS stm.modify_eva(modify_dict=setup_dict, query_field=query_field, @@ -364,10 +364,10 @@ termed SOW 1 and SOW 2 respectively. # path to the numpy file containing the samples eva_samples_file = os.path.join(output_directory, "eva_2-samples_scenario-1.npy") - - # load samples + + # load samples sample_array = np.load(eva_samples_file) - + sample_array @@ -395,43 +395,43 @@ scenarios and extract the reservoir levels for Blue Mesa. # set realization and sample realization = 1 sample = np.arange(0, 2, 1) - + # read RSP template with open(eva_template_file) as template_obj: - + # read in file template_rsp = Template(template_obj.read()) - + for i in sample: - + # create scenario name scenario = f"S{i}_{realization}" - + # dictionary holding search keys and replacement values to update the template file d = {"EVA": f"../../input_files/gm2015B_{scenario}.eva"} - + # update the template new_rsp = template_rsp.safe_substitute(d) - + # construct simulated scenario directory simulated_scenario_dir = os.path.join(scenarios_dir, scenario) if not os.path.exists(simulated_scenario_dir): os.makedirs(simulated_scenario_dir) - + # target rsp file rsp_file = os.path.join(simulated_scenario_dir, f"gm2015B_{scenario}.rsp") - + # write updated rsp file with open(rsp_file, "w") as f1: f1.write(new_rsp) - + # construct simulated basin path simulated_basin_path = os.path.join(simulated_scenario_dir, f"gm2015B_{scenario}") - + # run StateMod print(f"Running: {scenario}") os.chdir(simulated_scenario_dir) - + subprocess.call([statemod_exe, simulated_basin_path, "-simulate"]) @@ -439,27 +439,27 @@ scenarios and extract the reservoir levels for Blue Mesa. .. parsed-literal:: Running: S0_1 - Startup log file for messages to this point: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S0_1/gm2015B_S0_1.rsp + Startup log file for messages to this point: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S0_1/gm2015B_S0_1.rsp Closing startup log file: statem.log - Opening dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S0_1/gm2015B_S0_1.log + Opening dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S0_1/gm2015B_S0_1.log ________________________________________________________________________ - - StateMod - State of Colorado - Water Supply Planning Model - - Version: 17.0.3 + + StateMod + State of Colorado - Water Supply Planning Model + + Version: 17.0.3 Last revision date: 2021/09/12 - + ________________________________________________________________________ - + Subroutine Execut Subroutine Datinp - + ... ________________________________________________________________________ Execut; Successful Termination - Statem; See detailed messages in dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S1_1/gm2015B_S1_1.log + Statem; See detailed messages in dataset log file: /home/jovyan/data/gm2015_StateMod_modified/gm2015_StateMod_modified/StateMod/scenarios/S1_1/gm2015B_S1_1.log Stop 0 @@ -473,16 +473,16 @@ at Blue Mesa in the two new SOWs. # SOW 1 output_dir= os.path.join(scenarios_dir, "S0_1") - + # path the the xre file - xre_file = os.path.join(output_dir, "gm2015B_S0_1.xre") - + xre_file = os.path.join(output_dir, "gm2015B_S0_1.xre") + # structure ID for reservoir of interest - structure_ID = '6203532' - + structure_ID = '6203532' + # name of the reservoir - structure_name = 'Blue_Mesa' - + structure_name = 'Blue_Mesa' + # extract the target info into a Pandas data frame df = stm.extract_xre_data(structure_name=structure_name, structure_id=structure_ID, @@ -492,14 +492,14 @@ at Blue Mesa in the two new SOWs. write_csv=True, write_parquet=None ) - + # SOW 2 output_dir= os.path.join(scenarios_dir, "S1_1") - + # path the the xre file - xre_file = os.path.join(output_dir, "gm2015B_S1_1.xre") - - + xre_file = os.path.join(output_dir, "gm2015B_S1_1.xre") + + # extract the target info into a Pandas data frame df = stm.extract_xre_data(structure_name=structure_name, structure_id=structure_ID, @@ -519,32 +519,32 @@ world and alternative states of the world. # historic reservoir directory historic_res_dir = os.path.join(data_dir, "historic_reservoir_levels") blue_mesa_file = os.path.join(historic_res_dir, "Blue_Mesa_xre_data.csv") - - # Import baseline dataframe - baseline = pd.read_csv(blue_mesa_file, index_col=False, usecols=['Year','Init. Storage']) + + # Import baseline dataframe + baseline = pd.read_csv(blue_mesa_file, index_col=False, usecols=['Year','Init. Storage']) baseline = baseline.groupby('Year').mean().reset_index() - + # Import SOW1 s0_1_file = os.path.join(scenarios_dir, "S0_1", "Blue_Mesa_xre_data.csv") - SOW1 = pd.read_csv(s0_1_file, index_col=False, usecols=['Year','Init. Storage']) + SOW1 = pd.read_csv(s0_1_file, index_col=False, usecols=['Year','Init. Storage']) SOW1 = SOW1.groupby('Year').mean().reset_index() - + # Import SOW2 s1_1_file = os.path.join(scenarios_dir, "S1_1", "Blue_Mesa_xre_data.csv") - SOW2 = pd.read_csv(s1_1_file, index_col=False, usecols=['Year','Init. Storage']) + SOW2 = pd.read_csv(s1_1_file, index_col=False, usecols=['Year','Init. Storage']) SOW2 = SOW2.groupby('Year').mean().reset_index() - - # Plot reservoir levels + + # Plot reservoir levels fig, ax = plt.subplots() - + plt.plot(baseline['Year'], baseline['Init. Storage'],label='Baseline') plt.plot(SOW1['Year'], SOW1['Init. Storage'],label='Reduced Evaporation') plt.plot(SOW2['Year'], SOW2['Init. Storage'],label='Increased Evaporation') - + plt.title("Blue Mesa Storage") plt.xlabel("Year") plt.ylabel("Reservoir Storage (AF)") - + plt.legend() @@ -589,4 +589,3 @@ e2020EF001503. :: 1. modify_eva() - diff --git a/docs/source/notebooks/N3_Reservoir_File_Modification.rst b/docs/source/notebooks/N3_Reservoir_File_Modification.rst index 9495edd..f61f26b 100644 --- a/docs/source/notebooks/N3_Reservoir_File_Modification.rst +++ b/docs/source/notebooks/N3_Reservoir_File_Modification.rst @@ -21,10 +21,10 @@ Step 1: Run a Historical Simulation in StateMod for the Uppper Colorado Subbasin import pickle from string import Template import subprocess - + import matplotlib.pyplot as plt import numpy as np - import pandas as pd + import pandas as pd import statemodify as stm .. container:: alert alert-block alert-info @@ -42,16 +42,16 @@ StateMod in a baseline simulation. # statemod directory statemod_dir = "/usr/src/statemodify/statemod_upper_co" - + # root directory of statemod data for the target basin root_dir = os.path.join(statemod_dir, "src", "main", "fortran") - + # home directory of notebook instance home_dir = os.path.dirname(os.getcwd()) - + # path to the statemod executable statemod_exe = os.path.join(root_dir, "statemod") - + # data directory and root name for the target basin data_dir = os.path.join( home_dir, @@ -59,17 +59,17 @@ StateMod in a baseline simulation. "cm2015_StateMod", "StateMod" ) - + # directory to the target basin input files with root name for the basin basin_path = os.path.join(data_dir, "cm2015B") - + # scenarios output directory scenarios_dir_res = os.path.join(data_dir, "scenarios_res") - + # parquet files output directory parquet_dir_res = os.path.join(data_dir, "parquet_res") - - + + # path to res template file res_template_file = os.path.join( home_dir, @@ -86,7 +86,7 @@ StateMod in a baseline simulation. .. code:: ipython3 - # Change directories first + # Change directories first os.chdir(data_dir) #This is needed specific to the Upper Colorado model as the path name is too long for the model to accept subprocess.call([statemod_exe, "cm2015B", "-simulate"]) @@ -94,28 +94,28 @@ StateMod in a baseline simulation. .. parsed-literal:: - Parse; Command line argument: - cm2015B -simulate + Parse; Command line argument: + cm2015B -simulate ________________________________________________________________________ - - StateMod - State of Colorado - Water Supply Planning Model - + + StateMod + State of Colorado - Water Supply Planning Model + Version: 15.00.01 Last revision date: 2015/10/28 - + ________________________________________________________________________ - - Opening log file cm2015B.log - + + Opening log file cm2015B.log + Subroutine Execut Subroutine Datinp - + ... ________________________________________________________________________ Execut; Successful Termination - Statem; See detailed messages in file: cm2015B.log + Statem; See detailed messages in file: cm2015B.log Stop 0 @@ -127,16 +127,16 @@ median water rights (47483.00000) and a smaller decree of 2.90 cfs. .. code:: ipython3 - #Extract shortages using statemodify convert_xdd() function - - # create a directory to store the historic shortages + #Extract shortages using statemodify convert_xdd() function + + # create a directory to store the historic shortages output_dir = os.path.join(data_dir, "historic_shortages") - + # create a directory to store the new files in if it does not exist output_directory = os.path.join(data_dir, "historic_shortages") if not os.path.exists(output_directory): os.makedirs(output_directory) - + stm.xdd.convert_xdd( # path to a directory where output .parquet files should be written output_path=output_dir, @@ -150,7 +150,7 @@ median water rights (47483.00000) and a smaller decree of 2.90 cfs. parallel_jobs=2, # convert to natural data types preserve_string_dtype=False - + ) @@ -164,13 +164,13 @@ Next we plot the shortages for Breckenridge. .. code:: ipython3 data=pd.read_parquet(output_dir +'/cm2015B.parquet',engine='pyarrow') - + fig, ax = plt.subplots() - + for name, group in data.groupby('structure_id'): ax.scatter( group['year'], group['shortage_total'], label=name) - + plt.xlabel("Year") plt.ylabel("Shortage (AF)") plt.title("Baseline Shortages for Breckenridge") @@ -220,16 +220,16 @@ storage at all reservoirs in the basin. scenario = "1" # basin name to process basin_name = "Upper_Colorado" - + # seed value for reproducibility if so desired seed_value = 1 - + # number of jobs to launch in parallel; -1 is all but 1 processor used n_jobs = 2 - + # number of samples to generate n_samples = 1 - + stm.modify_res(output_dir=output_directory, scenario=scenario, basin_name=basin_name, @@ -275,53 +275,53 @@ scenarios and extract the shortages for Breckenridge. # set realization and sample realization = 1 sample = np.arange(0, 2, 1) - + # read RSP template with open(res_template_file) as template_obj: - + # read in file template_rsp = Template(template_obj.read()) - + for i in sample: - + # create scenario name scenario = f"S{i}_{realization}" - + # dictionary holding search keys and replacement values to update the template file d = {"RES": f"../../input_files/cm2015B_{scenario}.res"} - + # update the template new_rsp = template_rsp.safe_substitute(d) - + # construct simulated scenario directory simulated_scenario_dir = os.path.join(scenarios_dir_res, scenario) if not os.path.exists(simulated_scenario_dir): os.makedirs(simulated_scenario_dir) - + # target rsp file rsp_file = os.path.join(simulated_scenario_dir, f"cm2015B_{scenario}.rsp") - + # write updated rsp file with open(rsp_file, "w") as f1: f1.write(new_rsp) - + # construct simulated basin path simulated_basin_path = f"cm2015B_{scenario}" - + # run StateMod print(f"Running: {scenario}") os.chdir(simulated_scenario_dir) - + subprocess.call([statemod_exe, simulated_basin_path, "-simulate"]) - - #Save output to parquet files + + #Save output to parquet files print('creating parquet for ' + scenario) - + output_directory = os.path.join(parquet_dir_res+"/scenario/"+ scenario) - + if not os.path.exists(output_directory): os.makedirs(output_directory) - + stm.xdd.convert_xdd( output_path=output_directory, allow_overwrite=True, @@ -335,28 +335,28 @@ scenarios and extract the shortages for Breckenridge. .. parsed-literal:: Running: S0_1 - Parse; Command line argument: - cm2015B_S0_1 -simulate + Parse; Command line argument: + cm2015B_S0_1 -simulate ________________________________________________________________________ - - StateMod - State of Colorado - Water Supply Planning Model - + + StateMod + State of Colorado - Water Supply Planning Model + Version: 15.00.01 Last revision date: 2015/10/28 - + ________________________________________________________________________ - - Opening log file cm2015B_S0_1.log - + + Opening log file cm2015B_S0_1.log + Subroutine Execut Subroutine Datinp - + ... ________________________________________________________________________ Execut; Successful Termination - Statem; See detailed messages in file: cm2015B_S0_1.log + Statem; See detailed messages in file: cm2015B_S0_1.log Stop 0 creating parquet for S0_1 @@ -369,32 +369,32 @@ scenarios and extract the shortages for Breckenridge. .. parsed-literal:: Running: S1_1 - Parse; Command line argument: - cm2015B_S1_1 -simulate + Parse; Command line argument: + cm2015B_S1_1 -simulate ________________________________________________________________________ - - StateMod - State of Colorado - Water Supply Planning Model - + + StateMod + State of Colorado - Water Supply Planning Model + Version: 15.00.01 Last revision date: 2015/10/28 - + ________________________________________________________________________ - - Opening log file cm2015B_S1_1.log - + + Opening log file cm2015B_S1_1.log + Subroutine Execut Subroutine Datinp - + ________________________________________________________________________ - Datinp; Control File (*.ctl) - + Datinp; Control File (*.ctl) + ________________________________________________________________________ Datinp; River Network File (*.rin) - + ________________________________________________________________________ Datinp; Reservoir Station File (*.res) - + ________________________________________________________________________ GetFile; Stopped in GetFile, see the log file (*.log) Stop 1 @@ -429,7 +429,7 @@ and alternative states of the world and plot the resulting shortages. 283922 0. 283923 220. 283924 201. - ... + ... 285280 0. 285281 0. 285282 0. @@ -443,17 +443,17 @@ and alternative states of the world and plot the resulting shortages. baseline=pd.read_parquet(data_dir+'/'+'historic_shortages/cm2015B.parquet',engine='pyarrow') SOW_1=pd.read_parquet(parquet_dir_res+'/scenario/S0_1/cm2015B_S0_1.parquet',engine='pyarrow') - + #Subtract shortages with respect to the baseline subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total']],axis=1) subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1'], axis=1) subset_df['SOW_1_diff']=subset_df['SOW_1']-subset_df['Baseline'] - + #Plot shortages fig, ax = plt.subplots() - + ax.scatter(subset_df['Year'], subset_df['SOW_1_diff']) - + plt.xlabel("Year") plt.ylabel("Shortage (AF)") plt.title("Change in Breckenridge Shortages from the Baseline") @@ -516,4 +516,3 @@ e2020EF001503. :: 1. modify_res() - diff --git a/docs/source/notebooks/N4_Streamflow_File_Modification.rst b/docs/source/notebooks/N4_Streamflow_File_Modification.rst index bbee9ba..3f6ce84 100644 --- a/docs/source/notebooks/N4_Streamflow_File_Modification.rst +++ b/docs/source/notebooks/N4_Streamflow_File_Modification.rst @@ -57,10 +57,10 @@ Step 1: Fit Multi-Site HMM import pickle from string import Template import subprocess - + import matplotlib.pyplot as plt import numpy as np - import pandas as pd + import pandas as pd import statemodify as stm First we define directories and associated paths. @@ -69,16 +69,16 @@ First we define directories and associated paths. # statemod directory statemod_dir = "/usr/src/statemodify/statemod_upper_co" - + # root directory of statemod data for the target basin root_dir = os.path.join(statemod_dir, "src", "main", "fortran") - + # home directory of notebook instance home_dir = os.path.dirname(os.getcwd()) - + # path to the statemod executable statemod_exe = os.path.join(root_dir, "statemod") - + # data directory and root name for the target basin data_dir = os.path.join( home_dir, @@ -86,14 +86,14 @@ First we define directories and associated paths. "cm2015_StateMod", "StateMod" ) - + # directory to the target basin input files with root name for the basin basin_path = os.path.join(data_dir, "cm2015B") - + # scenarios output directory scenarios_dir = os.path.join(data_dir, "scenarios") - - # path to iwr/xbm file + + # path to iwr/xbm file xbm_iwr_template_file = os.path.join( home_dir, "data", @@ -108,21 +108,21 @@ store the HMM parameters. .. code:: ipython3 #Make directory to store HMM parameters - + output_dir = os.path.join(data_dir, "HMM_parameters") - + if not os.path.exists(output_dir): os.makedirs(output_dir) - + n_basins = 5 - + # choice to save parameters to NumPy array files save_parameters = True - + fit_array_dict = stm.hmm_multisite_fit(n_basins=n_basins, save_parameters=save_parameters, output_directory=output_dir) - + # unpack output dictionary unconditional_dry = fit_array_dict["unconditional_dry"] unconditional_wet = fit_array_dict["unconditional_wet"] @@ -145,12 +145,12 @@ in the ``HMM_Runs`` folder. .. code:: ipython3 #Create a folder to store the runs - + output_dir = os.path.join(data_dir, "HMM_Runs") - + if not os.path.exists(output_dir): os.makedirs(output_dir) - + # using the outputs of the fit function above; this function write output sample files to the output directory stm.hmm_multisite_sample(logAnnualQ_h, transition_matrix, @@ -209,30 +209,30 @@ function. .. code:: ipython3 - #Make directory to store input files - + #Make directory to store input files + output_dir = os.path.join(data_dir, "input_files") - + if not os.path.exists(output_dir): os.makedirs(output_dir) - - + + flow_realizations_directory = os.path.join(data_dir, "HMM_Runs") - + scenario = "1" - + # basin name to process basin_name = "Upper_Colorado" - + # seed value for reproducibility if so desired seed_value = 123 - + # number of jobs to launch in parallel; -1 is all but 1 processor used n_jobs = 2 - + # number of samples to generate (how many new xbm and iwr files); produces an IWR multiplier n_samples = 1 - + # generate a batch of files using generated LHS stm.modify_xbm_iwr(output_dir=output_dir, flow_realizations_directory=flow_realizations_directory, @@ -242,7 +242,7 @@ function. n_jobs=n_jobs, n_samples=n_samples, save_sample=True, - randomly_select_flow_sample=True) + randomly_select_flow_sample=True) Step 4: Read in the New Input Files and Run StateMod : Streamflow Example ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -268,43 +268,43 @@ levels. # set realization and sample realization = 1 sample = np.arange(0, 1, 1) - + # read RSP template with open(xbm_iwr_template_file) as template_obj: - + # read in file template_rsp = Template(template_obj.read()) - + for i in sample: - + # create scenario name scenario = f"S{i}_{realization}" - + # dictionary holding search keys and replacement values to update the template file d = {"XBM": f"../../input_files/cm2015B_{scenario}.xbm","IWR": f"../../input_files/cm2015B_{scenario}.iwr"} - + # update the template new_rsp = template_rsp.safe_substitute(d) - + # construct simulated scenario directory simulated_scenario_dir = os.path.join(scenarios_dir, scenario) if not os.path.exists(simulated_scenario_dir): os.makedirs(simulated_scenario_dir) - + # target rsp file rsp_file = os.path.join(simulated_scenario_dir, f"cm2015B_{scenario}.rsp") - + # write updated rsp file with open(rsp_file, "w") as f1: f1.write(new_rsp) - + # construct simulated basin path simulated_basin_path = f"cm2015B_{scenario}" - + # run StateMod print(f"Running: {scenario}") os.chdir(simulated_scenario_dir) - + subprocess.call([statemod_exe, simulated_basin_path, "-simulate"]) @@ -312,28 +312,28 @@ levels. .. parsed-literal:: Running: S0_1 - Parse; Command line argument: - cm2015B_S0_1 -simulate + Parse; Command line argument: + cm2015B_S0_1 -simulate ________________________________________________________________________ - - StateMod - State of Colorado - Water Supply Planning Model - + + StateMod + State of Colorado - Water Supply Planning Model + Version: 15.00.01 Last revision date: 2015/10/28 - + ________________________________________________________________________ - - Opening log file cm2015B_S0_1.log - + + Opening log file cm2015B_S0_1.log + Subroutine Execut Subroutine Datinp - + ... ________________________________________________________________________ Execut; Successful Termination - Statem; See detailed messages in file: cm2015B_S0_1.log + Statem; See detailed messages in file: cm2015B_S0_1.log Stop 0 @@ -351,10 +351,10 @@ the historical 105-year period. # Example with Granby Lake zip_file_path = os.path.join(home_dir, 'data', 'Granby_Dataset.zip') final_directory = os.path.join(home_dir, 'data/') - - !unzip $zip_file_path -d $final_directory + + !unzip $zip_file_path -d $final_directory granby_hmm, granby_hist, granby_hist_mean, granby_hist_1p = stm.read_xre(os.path.join(home_dir,"data/Upper_Colorado/"), 'Granby') - + # Plot quantiles stm.plot_res_quantiles(granby_hmm, granby_hist_mean, 'Lake Granby') @@ -415,13 +415,13 @@ assessments. :: - 2. Fitting Hidden Markov Models: Sample Scripts + 2. Fitting Hidden Markov Models: Sample Scripts .. container:: :: - 3. A Hidden-Markov Modeling Approach to Creating Synthetic Streamflow Scenarios Tutorial + 3. A Hidden-Markov Modeling Approach to Creating Synthetic Streamflow Scenarios Tutorial Notebook Specific References ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -447,4 +447,3 @@ e2020EF001503. :: 1. modify_xbm_iwr() - diff --git a/docs/source/notebooks/N5_Batch_Modification.rst b/docs/source/notebooks/N5_Batch_Modification.rst index 7389d78..46465e1 100644 --- a/docs/source/notebooks/N5_Batch_Modification.rst +++ b/docs/source/notebooks/N5_Batch_Modification.rst @@ -21,10 +21,10 @@ using the ``modify_batch()`` function. import pickle from string import Template import subprocess - + import matplotlib.pyplot as plt import numpy as np - import pandas as pd + import pandas as pd import statemodify as stm .. container:: alert alert-block alert-info @@ -39,16 +39,16 @@ using the ``modify_batch()`` function. # statemod directory statemod_dir = "/usr/src/statemodify/statemod_upper_co" - + # root directory of statemod data for the target basin root_dir = os.path.join(statemod_dir, "src", "main", "fortran") - + # home directory of notebook instance home_dir = os.path.dirname(os.getcwd()) - + # path to the statemod executable statemod_exe = os.path.join(root_dir, "statemod") - + # data directory and root name for the target basin data_dir = os.path.join( home_dir, @@ -56,17 +56,17 @@ using the ``modify_batch()`` function. "cm2015_StateMod", "StateMod" ) - + # directory to the target basin input files with root name for the basin basin_path = os.path.join(data_dir, "cm2015B") - + # scenarios output directory scenarios_dir = os.path.join(data_dir, "scenarios") - + #parquet output directory parquet_dir=os.path.join(data_dir, "parquet") - - + + # path to template file multi_template_file = os.path.join( home_dir, @@ -99,13 +99,13 @@ additives are applied to the target IDs listed. .. code:: ipython3 import statemodify as stm - + # variables that apply to multiple functions output_dir = os.path.join(data_dir, "input_files") basin_name = "Upper_Colorado" scenario = "1" seed_value = 77 - + # problem dictionary problem_dict = { "n_samples": 1, @@ -144,7 +144,7 @@ additives are applied to the target IDs listed. "ids": ["3600507", "3600603"] } } - + # run in batch fn_parameter_dict = stm.modify_batch(problem_dict=problem_dict) @@ -178,53 +178,53 @@ a specific user (ID: 3601008). # set realization and sample realization = 1 sample = np.arange(0, 1, 1) - + # read RSP template with open(multi_template_file) as template_obj: - + # read in file template_rsp = Template(template_obj.read()) - + for i in sample: - + # create scenario name scenario = f"S{i}_{realization}" - + # dictionary holding search keys and replacement values to update the template file d = {"EVA": f"../../input_files/cm2015B_{scenario}.eva","DDM": f"../../input_files/cm2015B_{scenario}.ddm","DDR": f"../../input_files/cm2015B_{scenario}.ddr"} - + # update the template new_rsp = template_rsp.safe_substitute(d) - + # construct simulated scenario directory simulated_scenario_dir = os.path.join(scenarios_dir, scenario) if not os.path.exists(simulated_scenario_dir): os.makedirs(simulated_scenario_dir) - + # target rsp file rsp_file = os.path.join(simulated_scenario_dir, f"cm2015B_{scenario}.rsp") - + # write updated rsp file with open(rsp_file, "w") as f1: f1.write(new_rsp) - + # construct simulated basin path simulated_basin_path = f"cm2015B_{scenario}" - + # run StateMod print(f"Running: {scenario}") os.chdir(simulated_scenario_dir) - + subprocess.call([statemod_exe, simulated_basin_path, "-simulate"]) - - #Save output to parquet files + + #Save output to parquet files print('creating parquet for ' + scenario) - + output_directory = os.path.join(parquet_dir+ "/scenario/"+ scenario) - + if not os.path.exists(output_directory): os.makedirs(output_directory) - + stm.xdd.convert_xdd(output_path=output_directory,allow_overwrite=False,xdd_files=scenarios_dir + "/"+ scenario + "/cm2015B_"+scenario+".xdd",id_subset=['3601008'],parallel_jobs=2) @@ -232,23 +232,23 @@ a specific user (ID: 3601008). .. parsed-literal:: Running: S0_1 - Parse; Command line argument: - cm2015B_S0_1 -simulate + Parse; Command line argument: + cm2015B_S0_1 -simulate ________________________________________________________________________ - - StateMod - State of Colorado - Water Supply Planning Model - + + StateMod + State of Colorado - Water Supply Planning Model + Version: 15.00.01 Last revision date: 2015/10/28 - + ________________________________________________________________________ - - Opening log file cm2015B_S0_1.log - + + Opening log file cm2015B_S0_1.log + Subroutine Execut Subroutine Datinp - + ... @@ -270,4 +270,3 @@ the prior notebooks. :: 1. modify_batch() - diff --git a/notebooks/N1_Demand_WaterRights_File_Modification.ipynb b/notebooks/N1_Demand_WaterRights_File_Modification.ipynb index ebed839..3cc40e0 100644 --- a/notebooks/N1_Demand_WaterRights_File_Modification.ipynb +++ b/notebooks/N1_Demand_WaterRights_File_Modification.ipynb @@ -77,12 +77,13 @@ "import logging\n", "import os\n", "import pickle\n", - "from string import Template\n", "import subprocess\n", + "from string import Template\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd \n", + "import pandas as pd\n", + "\n", "import statemodify as stm" ] }, @@ -122,11 +123,7 @@ "\n", "# data directory and root name for the target basin\n", "data_dir = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"sj2015_StateMod_modified\",\n", - " \"sj2015_StateMod_modified\",\n", - " \"StateMod\"\n", + " home_dir, \"data\", \"sj2015_StateMod_modified\", \"sj2015_StateMod_modified\", \"StateMod\"\n", ")\n", "\n", "# directory to the target basin input files with root name for the basin\n", @@ -141,17 +138,9 @@ "parquet_dir_ddr = os.path.join(data_dir, \"parquet_ddr\")\n", "\n", "# path to ddm and ddr template file\n", - "ddm_template_file = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"sj2015B_template_ddm.rsp\"\n", - ")\n", + "ddm_template_file = os.path.join(home_dir, \"data\", \"sj2015B_template_ddm.rsp\")\n", "\n", - "ddr_template_file = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"sj2015B_template_ddr.rsp\"\n", - ")" + "ddr_template_file = os.path.join(home_dir, \"data\", \"sj2015B_template_ddr.rsp\")" ] }, { @@ -1596,7 +1585,7 @@ ], "source": [ "# run statemod\n", - "subprocess.call([statemod_exe, basin_path, \"-simulate\"])\n" + "subprocess.call([statemod_exe, basin_path, \"-simulate\"])" ] }, { @@ -1624,9 +1613,9 @@ } ], "source": [ - "#Extract shortages using statemodify convert_xdd() function \n", + "# Extract shortages using statemodify convert_xdd() function\n", "\n", - "# create a directory to store the historical shortages \n", + "# create a directory to store the historical shortages\n", "output_dir = os.path.join(data_dir, \"historic_shortages\")\n", "\n", "# create a directory to store the new files in if it does not exist\n", @@ -1642,12 +1631,12 @@ " # path, glob, or a list of paths/globs to the .xdd files you want to convert\n", " xdd_files=os.path.join(data_dir, \"*.xdd\"),\n", " # if the output .parquet files should only contain a subset of structure ids, list them here; None for all\n", - " id_subset=['2900501','2900519','2900555'],\n", + " id_subset=[\"2900501\", \"2900519\", \"2900555\"],\n", " # how many .xdd files to convert in parallel; optimally you will want 2-4 CPUs per parallel process\n", " parallel_jobs=4,\n", " # convert to natural data types\n", - " preserve_string_dtype=False\n", - ")\n" + " preserve_string_dtype=False,\n", + ")" ] }, { @@ -2086,7 +2075,7 @@ } ], "source": [ - "data=pd.read_parquet(os.path.join(output_dir,'sj2015B.parquet'),engine='pyarrow')\n", + "data = pd.read_parquet(os.path.join(output_dir, \"sj2015B.parquet\"), engine=\"pyarrow\")\n", "data" ] }, @@ -2140,9 +2129,8 @@ "source": [ "fig, ax = plt.subplots()\n", "\n", - "for name, group in data.groupby('structure_id'):\n", - " ax.scatter(\n", - " group['year'], group['shortage_total'], label=name)\n", + "for name, group in data.groupby(\"structure_id\"):\n", + " ax.scatter(group[\"year\"], group[\"shortage_total\"], label=name)\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", @@ -2187,10 +2175,7 @@ "outputs": [], "source": [ "# a dictionary to describe what users you want to modify and the bounds for the LHS\n", - "setup_dict = {\n", - " \"ids\": [\"2900501\", \"2900519\",\"2900555\"],\n", - " \"bounds\": [0.5, 1.5]\n", - "}\n", + "setup_dict = {\"ids\": [\"2900501\", \"2900519\", \"2900555\"], \"bounds\": [0.5, 1.5]}\n", "\n", "output_directory = output_dir = os.path.join(data_dir, \"input_files\")\n", "\n", @@ -2231,8 +2216,8 @@ " data_specification_file=None,\n", " min_bound_value=-0.5,\n", " max_bound_value=1.5,\n", - " save_sample=True\n", - ")\n" + " save_sample=True,\n", + ")" ] }, { @@ -2264,7 +2249,7 @@ } ], "source": [ - "sample_array = np.load(output_directory+'/ddm_2-samples_scenario-1.npy')\n", + "sample_array = np.load(output_directory + \"/ddm_2-samples_scenario-1.npy\")\n", "sample_array" ] }, @@ -3740,58 +3725,63 @@ "\n", "# read RSP template\n", "with open(ddm_template_file) as template_obj:\n", - " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", - " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - " \n", + "\n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"DDM\": f\"../../input_files/sj2015B_{scenario}.ddm\"}\n", - " \n", + "\n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - " \n", + "\n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir_ddm, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - " \n", + "\n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}.rsp\")\n", - " \n", + "\n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - " \n", + "\n", " # construct simulated basin path\n", - " simulated_basin_path = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}\")\n", + " simulated_basin_path = os.path.join(\n", + " simulated_scenario_dir, f\"sj2015B_{scenario}\"\n", + " )\n", "\n", " # run StateMod\n", " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - " \n", - " #Save output to parquet files \n", - " print('creating parquet for ' + scenario)\n", - " \n", - " output_directory = os.path.join(parquet_dir_ddm+\"/scenario/\"+ scenario)\n", - " \n", + "\n", + " # Save output to parquet files\n", + " print(\"creating parquet for \" + scenario)\n", + "\n", + " output_directory = os.path.join(parquet_dir_ddm + \"/scenario/\" + scenario)\n", + "\n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - " \n", + "\n", " stm.xdd.convert_xdd(\n", " output_path=output_directory,\n", " allow_overwrite=False,\n", - " xdd_files=scenarios_dir_ddm + \"/\"+ scenario + \"/sj2015B_\"+scenario+\".xdd\",\n", - " id_subset=['2900501','2900519','2900555'],\n", + " xdd_files=scenarios_dir_ddm\n", + " + \"/\"\n", + " + scenario\n", + " + \"/sj2015B_\"\n", + " + scenario\n", + " + \".xdd\",\n", + " id_subset=[\"2900501\", \"2900519\", \"2900555\"],\n", " parallel_jobs=4,\n", - " preserve_string_dtype=False\n", - " )\n" + " preserve_string_dtype=False,\n", + " )" ] }, { @@ -3836,7 +3826,7 @@ ], "source": [ "output_directory = os.path.join(data_dir, \"input_files\")\n", - "sample_array = np.load(output_directory+'/ddm_2-samples_scenario-1.npy')\n", + "sample_array = np.load(output_directory + \"/ddm_2-samples_scenario-1.npy\")\n", "sample_array" ] }, @@ -3879,21 +3869,35 @@ ], "source": [ "# Read in raw parquet files\n", - "baseline=pd.read_parquet(data_dir+'/historic_shortages/sj2015B.parquet',engine='pyarrow')\n", - "SOW_1=pd.read_parquet(parquet_dir_ddm+'/scenario/S0_1/sj2015B_S0_1.parquet',engine='pyarrow')\n", - "SOW_2=pd.read_parquet(parquet_dir_ddm+'/scenario/S1_1/sj2015B_S1_1.parquet',engine='pyarrow')\n", + "baseline = pd.read_parquet(\n", + " data_dir + \"/historic_shortages/sj2015B.parquet\", engine=\"pyarrow\"\n", + ")\n", + "SOW_1 = pd.read_parquet(\n", + " parquet_dir_ddm + \"/scenario/S0_1/sj2015B_S0_1.parquet\", engine=\"pyarrow\"\n", + ")\n", + "SOW_2 = pd.read_parquet(\n", + " parquet_dir_ddm + \"/scenario/S1_1/sj2015B_S1_1.parquet\", engine=\"pyarrow\"\n", + ")\n", "\n", "# Subtract shortages with respect to the baseline\n", - "subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total'],SOW_2['shortage_total']],axis=1)\n", - "subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1','SOW_2'], axis=1)\n", - "subset_df['SOW_1_diff'] = subset_df['SOW_1']-subset_df['Baseline']\n", - "subset_df['SOW_2_diff'] = subset_df['SOW_2']-subset_df['Baseline']\n", + "subset_df = pd.concat(\n", + " [\n", + " baseline[\"year\"],\n", + " baseline[\"shortage_total\"],\n", + " SOW_1[\"shortage_total\"],\n", + " SOW_2[\"shortage_total\"],\n", + " ],\n", + " axis=1,\n", + ")\n", + "subset_df = subset_df.set_axis([\"Year\", \"Baseline\", \"SOW_1\", \"SOW_2\"], axis=1)\n", + "subset_df[\"SOW_1_diff\"] = subset_df[\"SOW_1\"] - subset_df[\"Baseline\"]\n", + "subset_df[\"SOW_2_diff\"] = subset_df[\"SOW_2\"] - subset_df[\"Baseline\"]\n", "\n", "# Plot shortages\n", "fig, ax = plt.subplots()\n", "\n", - "ax.scatter(subset_df['Year'], subset_df['SOW_1_diff'],label='Decreased Demand')\n", - "ax.scatter(subset_df['Year'], subset_df['SOW_2_diff'],label='Increased Demand')\n", + "ax.scatter(subset_df[\"Year\"], subset_df[\"SOW_1_diff\"], label=\"Decreased Demand\")\n", + "ax.scatter(subset_df[\"Year\"], subset_df[\"SOW_2_diff\"], label=\"Increased Demand\")\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", @@ -3938,11 +3942,9 @@ "setup_dict = {\n", " # ids can either be 'struct' or 'id' values\n", " \"ids\": [\"2900501\"],\n", - "\n", " # turn id on or off completely or for a given period\n", " # if 0 = off, 1 = on, YYYY = on for years >= YYYY, -YYYY = off for years > YYYY; see file header\n", " \"on_off\": [1],\n", - "\n", " # apply rank of administrative order where 0 is lowest (senior) and n is highest (junior); None is no change\n", " \"admin\": [1],\n", "}\n", @@ -3985,8 +3987,8 @@ " data_specification_file=None,\n", " min_bound_value=-0.5,\n", " max_bound_value=1.5,\n", - " save_sample=True\n", - ")\n" + " save_sample=True,\n", + ")" ] }, { @@ -5449,58 +5451,63 @@ "\n", "# read RSP template\n", "with open(ddr_template_file) as template_obj:\n", - " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", - " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - " \n", + "\n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"DDR\": f\"../../input_files/sj2015B_{scenario}.ddr\"}\n", - " \n", + "\n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - " \n", + "\n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir_ddr, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - " \n", + "\n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}.rsp\")\n", - " \n", + "\n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - " \n", + "\n", " # construct simulated basin path\n", - " simulated_basin_path = os.path.join(simulated_scenario_dir, f\"sj2015B_{scenario}\")\n", + " simulated_basin_path = os.path.join(\n", + " simulated_scenario_dir, f\"sj2015B_{scenario}\"\n", + " )\n", "\n", " # run StateMod\n", " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - " \n", - " #Save output to parquet files \n", - " print('creating parquet for ' + scenario)\n", - " \n", - " output_directory = os.path.join(parquet_dir_ddr+\"/scenario/\"+ scenario)\n", - " \n", + "\n", + " # Save output to parquet files\n", + " print(\"creating parquet for \" + scenario)\n", + "\n", + " output_directory = os.path.join(parquet_dir_ddr + \"/scenario/\" + scenario)\n", + "\n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - " \n", + "\n", " stm.xdd.convert_xdd(\n", " output_path=output_directory,\n", " allow_overwrite=False,\n", - " xdd_files=scenarios_dir_ddr + \"/\"+ scenario + \"/sj2015B_\"+scenario+\".xdd\",\n", - " id_subset=['2900501'],\n", + " xdd_files=scenarios_dir_ddr\n", + " + \"/\"\n", + " + scenario\n", + " + \"/sj2015B_\"\n", + " + scenario\n", + " + \".xdd\",\n", + " id_subset=[\"2900501\"],\n", " parallel_jobs=2,\n", - " preserve_string_dtype=False\n", - " )\n" + " preserve_string_dtype=False,\n", + " )" ] }, { @@ -5542,18 +5549,24 @@ ], "source": [ "# Read in raw parquet files\n", - "baseline=pd.read_parquet(data_dir+'/historic_shortages/sj2015B.parquet',engine='pyarrow')\n", - "SOW_1=pd.read_parquet(parquet_dir_ddr+ '/scenario/S0_1/sj2015B_S0_1.parquet',engine='pyarrow')\n", + "baseline = pd.read_parquet(\n", + " data_dir + \"/historic_shortages/sj2015B.parquet\", engine=\"pyarrow\"\n", + ")\n", + "SOW_1 = pd.read_parquet(\n", + " parquet_dir_ddr + \"/scenario/S0_1/sj2015B_S0_1.parquet\", engine=\"pyarrow\"\n", + ")\n", "\n", "# Subtract shortages with respect to the baseline\n", - "subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total']],axis=1)\n", - "subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1'], axis=1)\n", - "subset_df['diff']=subset_df['SOW_1']-subset_df['Baseline']\n", + "subset_df = pd.concat(\n", + " [baseline[\"year\"], baseline[\"shortage_total\"], SOW_1[\"shortage_total\"]], axis=1\n", + ")\n", + "subset_df = subset_df.set_axis([\"Year\", \"Baseline\", \"SOW_1\"], axis=1)\n", + "subset_df[\"diff\"] = subset_df[\"SOW_1\"] - subset_df[\"Baseline\"]\n", "\n", "# Plot shortages\n", "fig, ax = plt.subplots()\n", "\n", - "ax.scatter(subset_df['Year'], subset_df['diff'])\n", + "ax.scatter(subset_df[\"Year\"], subset_df[\"diff\"])\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", diff --git a/notebooks/N2_Evaporation_File_Modification.ipynb b/notebooks/N2_Evaporation_File_Modification.ipynb index 52d2e91..a951ea3 100644 --- a/notebooks/N2_Evaporation_File_Modification.ipynb +++ b/notebooks/N2_Evaporation_File_Modification.ipynb @@ -47,12 +47,13 @@ "import logging\n", "import os\n", "import pickle\n", - "from string import Template\n", "import subprocess\n", + "from string import Template\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd \n", + "import pandas as pd\n", + "\n", "import statemodify as stm" ] }, @@ -96,11 +97,7 @@ "\n", "# data directory and root name for the target basin\n", "data_dir = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"gm2015_StateMod_modified\",\n", - " \"gm2015_StateMod_modified\",\n", - " \"StateMod\"\n", + " home_dir, \"data\", \"gm2015_StateMod_modified\", \"gm2015_StateMod_modified\", \"StateMod\"\n", ")\n", "\n", "# directory to the target basin input files with root name for the basin\n", @@ -110,11 +107,7 @@ "scenarios_dir = os.path.join(data_dir, \"scenarios\")\n", "\n", "# path to eva template file\n", - "eva_template_file = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"gm2015B_template_eva.rsp\"\n", - ")" + "eva_template_file = os.path.join(home_dir, \"data\", \"gm2015B_template_eva.rsp\")" ] }, { @@ -1570,7 +1563,7 @@ ], "source": [ "# run statemod\n", - "subprocess.call([statemod_exe, basin_path, \"-simulate\"])\n" + "subprocess.call([statemod_exe, basin_path, \"-simulate\"])" ] }, { @@ -1590,7 +1583,7 @@ }, "outputs": [], "source": [ - "# create a directory to store the historical reservoir levels at Blue Mesa \n", + "# create a directory to store the historical reservoir levels at Blue Mesa\n", "output_dir = os.path.join(data_dir, \"historic_reservoir_levels\")\n", "\n", "if not os.path.exists(output_dir):\n", @@ -1600,19 +1593,20 @@ "xre_file = os.path.join(data_dir, \"gm2015B.xre\")\n", "\n", "# structure ID for reservoir of interest\n", - "structure_ID = '6203532' \n", + "structure_ID = \"6203532\"\n", "\n", "# name of the reservoir\n", - "structure_name = 'Blue_Mesa' \n", + "structure_name = \"Blue_Mesa\"\n", "\n", "# extract the target info into a Pandas data frame\n", - "df = stm.extract_xre_data(structure_name=structure_name,\n", - " structure_id=structure_ID,\n", - " input_file=xre_file,\n", - " basin_name=None,\n", - " output_directory=output_dir,\n", - " write_csv=True,\n", - " write_parquet=None\n", + "df = stm.extract_xre_data(\n", + " structure_name=structure_name,\n", + " structure_id=structure_ID,\n", + " input_file=xre_file,\n", + " basin_name=None,\n", + " output_directory=output_dir,\n", + " write_csv=True,\n", + " write_parquet=None,\n", ")" ] }, @@ -1744,15 +1738,12 @@ "output_xre_file = os.path.join(output_dir, \"Blue_Mesa_xre_data.csv\")\n", "\n", "# read output data into a data frame\n", - "df = pd.read_csv(\n", - " output_xre_file, \n", - " usecols=['Year','Init. Storage'],\n", - " index_col=False) \n", + "df = pd.read_csv(output_xre_file, usecols=[\"Year\", \"Init. Storage\"], index_col=False)\n", "\n", "# calculate the annual average\n", - "df = df.groupby('Year').mean().reset_index()\n", + "df = df.groupby(\"Year\").mean().reset_index()\n", "\n", - "df\n" + "df" ] }, { @@ -1795,11 +1786,11 @@ "source": [ "fig, ax = plt.subplots()\n", "\n", - "plt.plot(df['Year'], df['Init. Storage'])\n", + "plt.plot(df[\"Year\"], df[\"Init. Storage\"])\n", "\n", "plt.title(\"Blue Mesa Storage\")\n", "plt.xlabel(\"Year\")\n", - "plt.ylabel(\"Reservoir Storage (AF)\")\n" + "plt.ylabel(\"Reservoir Storage (AF)\")" ] }, { @@ -1827,11 +1818,8 @@ }, "outputs": [], "source": [ - "# a dictionary to describe what you want to modify and the bounds for the Latin hypercube sample. \n", - "setup_dict = {\n", - " \"ids\": ['10011'],\n", - " \"bounds\": [-0.5, 1.0]\n", - "}\n", + "# a dictionary to describe what you want to modify and the bounds for the Latin hypercube sample.\n", + "setup_dict = {\"ids\": [\"10011\"], \"bounds\": [-0.5, 1.0]}\n", "\n", "# create a directory to store the new files in if it does not exist\n", "output_directory = os.path.join(data_dir, \"input_files\")\n", @@ -1860,22 +1848,24 @@ "basin_name = \"Gunnison\"\n", "\n", "# generate a batch of files using generated LHS\n", - "stm.modify_eva(modify_dict=setup_dict,\n", - " query_field=query_field,\n", - " output_dir=output_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " sampling_method=\"LHS\",\n", - " n_samples=n_samples,\n", - " skip_rows=skip_rows,\n", - " n_jobs=n_jobs,\n", - " seed_value=seed_value,\n", - " template_file=None,\n", - " factor_method=\"add\",\n", - " data_specification_file=None,\n", - " min_bound_value=-0.5,\n", - " max_bound_value=1.0,\n", - " save_sample=True)\n" + "stm.modify_eva(\n", + " modify_dict=setup_dict,\n", + " query_field=query_field,\n", + " output_dir=output_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " sampling_method=\"LHS\",\n", + " n_samples=n_samples,\n", + " skip_rows=skip_rows,\n", + " n_jobs=n_jobs,\n", + " seed_value=seed_value,\n", + " template_file=None,\n", + " factor_method=\"add\",\n", + " data_specification_file=None,\n", + " min_bound_value=-0.5,\n", + " max_bound_value=1.0,\n", + " save_sample=True,\n", + ")" ] }, { @@ -1910,10 +1900,10 @@ "# path to the numpy file containing the samples\n", "eva_samples_file = os.path.join(output_directory, \"eva_2-samples_scenario-1.npy\")\n", "\n", - "# load samples \n", + "# load samples\n", "sample_array = np.load(eva_samples_file)\n", "\n", - "sample_array\n" + "sample_array" ] }, { @@ -4807,41 +4797,41 @@ "\n", "# read RSP template\n", "with open(eva_template_file) as template_obj:\n", - " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", - " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - " \n", + "\n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"EVA\": f\"../../input_files/gm2015B_{scenario}.eva\"}\n", - " \n", + "\n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - " \n", + "\n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - " \n", + "\n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"gm2015B_{scenario}.rsp\")\n", - " \n", + "\n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - " \n", + "\n", " # construct simulated basin path\n", - " simulated_basin_path = os.path.join(simulated_scenario_dir, f\"gm2015B_{scenario}\")\n", + " simulated_basin_path = os.path.join(\n", + " simulated_scenario_dir, f\"gm2015B_{scenario}\"\n", + " )\n", "\n", " # run StateMod\n", " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", - " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n" + " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])" ] }, { @@ -4870,43 +4860,45 @@ "outputs": [], "source": [ "# SOW 1\n", - "output_dir= os.path.join(scenarios_dir, \"S0_1\")\n", + "output_dir = os.path.join(scenarios_dir, \"S0_1\")\n", "\n", "# path the the xre file\n", - "xre_file = os.path.join(output_dir, \"gm2015B_S0_1.xre\") \n", + "xre_file = os.path.join(output_dir, \"gm2015B_S0_1.xre\")\n", "\n", "# structure ID for reservoir of interest\n", - "structure_ID = '6203532' \n", + "structure_ID = \"6203532\"\n", "\n", "# name of the reservoir\n", - "structure_name = 'Blue_Mesa' \n", + "structure_name = \"Blue_Mesa\"\n", "\n", "# extract the target info into a Pandas data frame\n", - "df = stm.extract_xre_data(structure_name=structure_name,\n", - " structure_id=structure_ID,\n", - " input_file=xre_file,\n", - " basin_name=None,\n", - " output_directory=output_dir,\n", - " write_csv=True,\n", - " write_parquet=None\n", + "df = stm.extract_xre_data(\n", + " structure_name=structure_name,\n", + " structure_id=structure_ID,\n", + " input_file=xre_file,\n", + " basin_name=None,\n", + " output_directory=output_dir,\n", + " write_csv=True,\n", + " write_parquet=None,\n", ")\n", "\n", "# SOW 2\n", - "output_dir= os.path.join(scenarios_dir, \"S1_1\")\n", + "output_dir = os.path.join(scenarios_dir, \"S1_1\")\n", "\n", "# path the the xre file\n", - "xre_file = os.path.join(output_dir, \"gm2015B_S1_1.xre\") \n", + "xre_file = os.path.join(output_dir, \"gm2015B_S1_1.xre\")\n", "\n", "\n", "# extract the target info into a Pandas data frame\n", - "df = stm.extract_xre_data(structure_name=structure_name,\n", - " structure_id=structure_ID,\n", - " input_file=xre_file,\n", - " basin_name=None,\n", - " output_directory=output_dir,\n", - " write_csv=True,\n", - " write_parquet=None\n", - ")\n" + "df = stm.extract_xre_data(\n", + " structure_name=structure_name,\n", + " structure_id=structure_ID,\n", + " input_file=xre_file,\n", + " basin_name=None,\n", + " output_directory=output_dir,\n", + " write_csv=True,\n", + " write_parquet=None,\n", + ")" ] }, { @@ -4951,32 +4943,34 @@ "historic_res_dir = os.path.join(data_dir, \"historic_reservoir_levels\")\n", "blue_mesa_file = os.path.join(historic_res_dir, \"Blue_Mesa_xre_data.csv\")\n", "\n", - "# Import baseline dataframe \n", - "baseline = pd.read_csv(blue_mesa_file, index_col=False, usecols=['Year','Init. Storage']) \n", - "baseline = baseline.groupby('Year').mean().reset_index()\n", + "# Import baseline dataframe\n", + "baseline = pd.read_csv(\n", + " blue_mesa_file, index_col=False, usecols=[\"Year\", \"Init. Storage\"]\n", + ")\n", + "baseline = baseline.groupby(\"Year\").mean().reset_index()\n", "\n", "# Import SOW1\n", "s0_1_file = os.path.join(scenarios_dir, \"S0_1\", \"Blue_Mesa_xre_data.csv\")\n", - "SOW1 = pd.read_csv(s0_1_file, index_col=False, usecols=['Year','Init. Storage']) \n", - "SOW1 = SOW1.groupby('Year').mean().reset_index()\n", - " \n", + "SOW1 = pd.read_csv(s0_1_file, index_col=False, usecols=[\"Year\", \"Init. Storage\"])\n", + "SOW1 = SOW1.groupby(\"Year\").mean().reset_index()\n", + "\n", "# Import SOW2\n", "s1_1_file = os.path.join(scenarios_dir, \"S1_1\", \"Blue_Mesa_xre_data.csv\")\n", - "SOW2 = pd.read_csv(s1_1_file, index_col=False, usecols=['Year','Init. Storage']) \n", - "SOW2 = SOW2.groupby('Year').mean().reset_index()\n", - " \n", - "# Plot reservoir levels \n", + "SOW2 = pd.read_csv(s1_1_file, index_col=False, usecols=[\"Year\", \"Init. Storage\"])\n", + "SOW2 = SOW2.groupby(\"Year\").mean().reset_index()\n", + "\n", + "# Plot reservoir levels\n", "fig, ax = plt.subplots()\n", "\n", - "plt.plot(baseline['Year'], baseline['Init. Storage'],label='Baseline')\n", - "plt.plot(SOW1['Year'], SOW1['Init. Storage'],label='Reduced Evaporation')\n", - "plt.plot(SOW2['Year'], SOW2['Init. Storage'],label='Increased Evaporation')\n", + "plt.plot(baseline[\"Year\"], baseline[\"Init. Storage\"], label=\"Baseline\")\n", + "plt.plot(SOW1[\"Year\"], SOW1[\"Init. Storage\"], label=\"Reduced Evaporation\")\n", + "plt.plot(SOW2[\"Year\"], SOW2[\"Init. Storage\"], label=\"Increased Evaporation\")\n", "\n", "plt.title(\"Blue Mesa Storage\")\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Reservoir Storage (AF)\")\n", "\n", - "plt.legend()\n" + "plt.legend()" ] }, { diff --git a/notebooks/N3_Reservoir_File_Modification.ipynb b/notebooks/N3_Reservoir_File_Modification.ipynb index f440f62..1ea925a 100644 --- a/notebooks/N3_Reservoir_File_Modification.ipynb +++ b/notebooks/N3_Reservoir_File_Modification.ipynb @@ -37,12 +37,13 @@ "import logging\n", "import os\n", "import pickle\n", - "from string import Template\n", "import subprocess\n", + "from string import Template\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd \n", + "import pandas as pd\n", + "\n", "import statemodify as stm" ] }, @@ -88,12 +89,7 @@ "statemod_exe = os.path.join(root_dir, \"statemod\")\n", "\n", "# data directory and root name for the target basin\n", - "data_dir = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"cm2015_StateMod\",\n", - " \"StateMod\"\n", - ")\n", + "data_dir = os.path.join(home_dir, \"data\", \"cm2015_StateMod\", \"StateMod\")\n", "\n", "# directory to the target basin input files with root name for the basin\n", "basin_path = os.path.join(data_dir, \"cm2015B\")\n", @@ -106,11 +102,7 @@ "\n", "\n", "# path to res template file\n", - "res_template_file = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"cm2015B_template_res.rsp\"\n", - ")" + "res_template_file = os.path.join(home_dir, \"data\", \"cm2015B_template_res.rsp\")" ] }, { @@ -2911,9 +2903,11 @@ } ], "source": [ - "# Change directories first \n", - "os.chdir(data_dir) #This is needed specific to the Upper Colorado model as the path name is too long for the model to accept\n", - "subprocess.call([statemod_exe, \"cm2015B\", \"-simulate\"])\n" + "# Change directories first\n", + "os.chdir(\n", + " data_dir\n", + ") # This is needed specific to the Upper Colorado model as the path name is too long for the model to accept\n", + "subprocess.call([statemod_exe, \"cm2015B\", \"-simulate\"])" ] }, { @@ -2943,9 +2937,9 @@ } ], "source": [ - "#Extract shortages using statemodify convert_xdd() function \n", + "# Extract shortages using statemodify convert_xdd() function\n", "\n", - "# create a directory to store the historic shortages \n", + "# create a directory to store the historic shortages\n", "output_dir = os.path.join(data_dir, \"historic_shortages\")\n", "\n", "# create a directory to store the new files in if it does not exist\n", @@ -2961,12 +2955,11 @@ " # path, glob, or a list of paths/globs to the .xdd files you want to convert\n", " xdd_files=os.path.join(data_dir, \"*.xdd\"),\n", " # if the output .parquet files should only contain a subset of structure ids, list them here; None for all\n", - " id_subset=['3601008'],\n", + " id_subset=[\"3601008\"],\n", " # how many .xdd files to convert in parallel; optimally you will want 2-4 CPUs per parallel process\n", " parallel_jobs=2,\n", " # convert to natural data types\n", - " preserve_string_dtype=False\n", - " \n", + " preserve_string_dtype=False,\n", ")" ] }, @@ -3008,13 +3001,12 @@ } ], "source": [ - "data=pd.read_parquet(output_dir +'/cm2015B.parquet',engine='pyarrow')\n", + "data = pd.read_parquet(output_dir + \"/cm2015B.parquet\", engine=\"pyarrow\")\n", "\n", "fig, ax = plt.subplots()\n", "\n", - "for name, group in data.groupby('structure_id'):\n", - " ax.scatter(\n", - " group['year'], group['shortage_total'], label=name)\n", + "for name, group in data.groupby(\"structure_id\"):\n", + " ax.scatter(group[\"year\"], group[\"shortage_total\"], label=name)\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", @@ -3069,14 +3061,16 @@ "# number of samples to generate\n", "n_samples = 1\n", "\n", - "stm.modify_res(output_dir=output_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " target_structure_id_list=['3603575','3604512'],\n", - " seed_value=seed_value,\n", - " n_jobs=n_jobs,\n", - " n_samples=n_samples,\n", - " save_sample=True)\n" + "stm.modify_res(\n", + " output_dir=output_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " target_structure_id_list=[\"3603575\", \"3604512\"],\n", + " seed_value=seed_value,\n", + " n_jobs=n_jobs,\n", + " n_samples=n_samples,\n", + " save_sample=True,\n", + ")" ] }, { @@ -3108,7 +3102,8 @@ ], "source": [ "import numpy as np\n", - "sample_array = np.load(output_directory+'/res_1-samples_scenario-1.npy')\n", + "\n", + "sample_array = np.load(output_directory + \"/res_1-samples_scenario-1.npy\")\n", "sample_array" ] }, @@ -5963,33 +5958,31 @@ "\n", "# read RSP template\n", "with open(res_template_file) as template_obj:\n", - " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", - " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - " \n", + "\n", " # dictionary holding search keys and replacement values to update the template file\n", " d = {\"RES\": f\"../../input_files/cm2015B_{scenario}.res\"}\n", - " \n", + "\n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - " \n", + "\n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir_res, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - " \n", + "\n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"cm2015B_{scenario}.rsp\")\n", - " \n", + "\n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - " \n", + "\n", " # construct simulated basin path\n", " simulated_basin_path = f\"cm2015B_{scenario}\"\n", "\n", @@ -5998,22 +5991,27 @@ " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - " \n", - " #Save output to parquet files \n", - " print('creating parquet for ' + scenario)\n", - " \n", - " output_directory = os.path.join(parquet_dir_res+\"/scenario/\"+ scenario)\n", - " \n", + "\n", + " # Save output to parquet files\n", + " print(\"creating parquet for \" + scenario)\n", + "\n", + " output_directory = os.path.join(parquet_dir_res + \"/scenario/\" + scenario)\n", + "\n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - " \n", + "\n", " stm.xdd.convert_xdd(\n", " output_path=output_directory,\n", " allow_overwrite=True,\n", - " xdd_files=scenarios_dir_res + \"/\"+ scenario + \"/cm2015B_\"+scenario+\".xdd\",\n", - " id_subset=['3601008'],\n", + " xdd_files=scenarios_dir_res\n", + " + \"/\"\n", + " + scenario\n", + " + \"/cm2015B_\"\n", + " + scenario\n", + " + \".xdd\",\n", + " id_subset=[\"3601008\"],\n", " parallel_jobs=2,\n", - " preserve_string_dtype=False\n", + " preserve_string_dtype=False,\n", " )" ] }, @@ -6034,8 +6032,12 @@ }, "outputs": [], "source": [ - "baseline=pd.read_parquet(data_dir+'/'+'historic_shortages/cm2015B.parquet',engine='pyarrow')\n", - "SOW_1=pd.read_parquet(parquet_dir_res+'/scenario/S0_1/cm2015B_S0_1.parquet',engine='pyarrow')\n" + "baseline = pd.read_parquet(\n", + " data_dir + \"/\" + \"historic_shortages/cm2015B.parquet\", engine=\"pyarrow\"\n", + ")\n", + "SOW_1 = pd.read_parquet(\n", + " parquet_dir_res + \"/scenario/S0_1/cm2015B_S0_1.parquet\", engine=\"pyarrow\"\n", + ")" ] }, { @@ -6109,18 +6111,24 @@ } ], "source": [ - "baseline=pd.read_parquet(data_dir+'/'+'historic_shortages/cm2015B.parquet',engine='pyarrow')\n", - "SOW_1=pd.read_parquet(parquet_dir_res+'/scenario/S0_1/cm2015B_S0_1.parquet',engine='pyarrow')\n", + "baseline = pd.read_parquet(\n", + " data_dir + \"/\" + \"historic_shortages/cm2015B.parquet\", engine=\"pyarrow\"\n", + ")\n", + "SOW_1 = pd.read_parquet(\n", + " parquet_dir_res + \"/scenario/S0_1/cm2015B_S0_1.parquet\", engine=\"pyarrow\"\n", + ")\n", "\n", - "#Subtract shortages with respect to the baseline\n", - "subset_df=pd.concat([baseline['year'],baseline['shortage_total'],SOW_1['shortage_total']],axis=1)\n", - "subset_df = subset_df.set_axis(['Year', 'Baseline', 'SOW_1'], axis=1)\n", - "subset_df['SOW_1_diff']=subset_df['SOW_1']-subset_df['Baseline']\n", + "# Subtract shortages with respect to the baseline\n", + "subset_df = pd.concat(\n", + " [baseline[\"year\"], baseline[\"shortage_total\"], SOW_1[\"shortage_total\"]], axis=1\n", + ")\n", + "subset_df = subset_df.set_axis([\"Year\", \"Baseline\", \"SOW_1\"], axis=1)\n", + "subset_df[\"SOW_1_diff\"] = subset_df[\"SOW_1\"] - subset_df[\"Baseline\"]\n", "\n", - "#Plot shortages\n", + "# Plot shortages\n", "fig, ax = plt.subplots()\n", "\n", - "ax.scatter(subset_df['Year'], subset_df['SOW_1_diff'])\n", + "ax.scatter(subset_df[\"Year\"], subset_df[\"SOW_1_diff\"])\n", "\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Shortage (AF)\")\n", diff --git a/notebooks/N4_Streamflow_File_Modification.ipynb b/notebooks/N4_Streamflow_File_Modification.ipynb index 0d34272..4aaaf4e 100644 --- a/notebooks/N4_Streamflow_File_Modification.ipynb +++ b/notebooks/N4_Streamflow_File_Modification.ipynb @@ -69,12 +69,13 @@ "import logging\n", "import os\n", "import pickle\n", - "from string import Template\n", "import subprocess\n", + "from string import Template\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd \n", + "import pandas as pd\n", + "\n", "import statemodify as stm" ] }, @@ -108,12 +109,7 @@ "statemod_exe = os.path.join(root_dir, \"statemod\")\n", "\n", "# data directory and root name for the target basin\n", - "data_dir = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"cm2015_StateMod\",\n", - " \"StateMod\"\n", - ")\n", + "data_dir = os.path.join(home_dir, \"data\", \"cm2015_StateMod\", \"StateMod\")\n", "\n", "# directory to the target basin input files with root name for the basin\n", "basin_path = os.path.join(data_dir, \"cm2015B\")\n", @@ -121,12 +117,8 @@ "# scenarios output directory\n", "scenarios_dir = os.path.join(data_dir, \"scenarios\")\n", "\n", - "# path to iwr/xbm file \n", - "xbm_iwr_template_file = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"cm2015B_template_xbm_iwr.rsp\"\n", - ")" + "# path to iwr/xbm file\n", + "xbm_iwr_template_file = os.path.join(home_dir, \"data\", \"cm2015B_template_xbm_iwr.rsp\")" ] }, { @@ -146,7 +138,7 @@ }, "outputs": [], "source": [ - "#Make directory to store HMM parameters\n", + "# Make directory to store HMM parameters\n", "\n", "output_dir = os.path.join(data_dir, \"HMM_parameters\")\n", "\n", @@ -158,9 +150,9 @@ "# choice to save parameters to NumPy array files\n", "save_parameters = True\n", "\n", - "fit_array_dict = stm.hmm_multisite_fit(n_basins=n_basins,\n", - " save_parameters=save_parameters,\n", - " output_directory=output_dir)\n", + "fit_array_dict = stm.hmm_multisite_fit(\n", + " n_basins=n_basins, save_parameters=save_parameters, output_directory=output_dir\n", + ")\n", "\n", "# unpack output dictionary\n", "unconditional_dry = fit_array_dict[\"unconditional_dry\"]\n", @@ -170,7 +162,7 @@ "covariance_matrix_wet = fit_array_dict[\"covariance_matrix_wet\"]\n", "covariance_matrix_dry = fit_array_dict[\"covariance_matrix_dry\"]\n", "wet_state_means = fit_array_dict[\"wet_state_means\"]\n", - "dry_state_means = fit_array_dict[\"dry_state_means\"]\n" + "dry_state_means = fit_array_dict[\"dry_state_means\"]" ] }, { @@ -198,7 +190,7 @@ }, "outputs": [], "source": [ - "#Create a folder to store the runs\n", + "# Create a folder to store the runs\n", "\n", "output_dir = os.path.join(data_dir, \"HMM_Runs\")\n", "\n", @@ -206,15 +198,17 @@ " os.makedirs(output_dir)\n", "\n", "# using the outputs of the fit function above; this function write output sample files to the output directory\n", - "stm.hmm_multisite_sample(logAnnualQ_h,\n", - " transition_matrix,\n", - " unconditional_dry,\n", - " dry_state_means,\n", - " wet_state_means,\n", - " covariance_matrix_dry,\n", - " covariance_matrix_wet,\n", - " n_basins=n_basins,\n", - " output_directory=output_dir)" + "stm.hmm_multisite_sample(\n", + " logAnnualQ_h,\n", + " transition_matrix,\n", + " unconditional_dry,\n", + " dry_state_means,\n", + " wet_state_means,\n", + " covariance_matrix_dry,\n", + " covariance_matrix_wet,\n", + " n_basins=n_basins,\n", + " output_directory=output_dir,\n", + ")" ] }, { @@ -245,10 +239,13 @@ } ], "source": [ - "stm.plot_flow_duration_curves(flow_realizations_directory=output_dir,\n", - " save_figure=True,output_directory=output_dir,\n", - " figure_name= 'FDC',\n", - " dpi= 300)" + "stm.plot_flow_duration_curves(\n", + " flow_realizations_directory=output_dir,\n", + " save_figure=True,\n", + " output_directory=output_dir,\n", + " figure_name=\"FDC\",\n", + " dpi=300,\n", + ")" ] }, { @@ -278,16 +275,16 @@ }, "outputs": [], "source": [ - "#Make directory to store input files \n", + "# Make directory to store input files\n", "\n", "output_dir = os.path.join(data_dir, \"input_files\")\n", "\n", "if not os.path.exists(output_dir):\n", " os.makedirs(output_dir)\n", - " \n", + "\n", "\n", "flow_realizations_directory = os.path.join(data_dir, \"HMM_Runs\")\n", - " \n", + "\n", "scenario = \"1\"\n", "\n", "# basin name to process\n", @@ -303,15 +300,17 @@ "n_samples = 1\n", "\n", "# generate a batch of files using generated LHS\n", - "stm.modify_xbm_iwr(output_dir=output_dir,\n", - " flow_realizations_directory=flow_realizations_directory,\n", - " scenario=scenario,\n", - " basin_name=basin_name,\n", - " seed_value=seed_value,\n", - " n_jobs=n_jobs,\n", - " n_samples=n_samples,\n", - " save_sample=True,\n", - " randomly_select_flow_sample=True) " + "stm.modify_xbm_iwr(\n", + " output_dir=output_dir,\n", + " flow_realizations_directory=flow_realizations_directory,\n", + " scenario=scenario,\n", + " basin_name=basin_name,\n", + " seed_value=seed_value,\n", + " n_jobs=n_jobs,\n", + " n_samples=n_samples,\n", + " save_sample=True,\n", + " randomly_select_flow_sample=True,\n", + ")" ] }, { @@ -3128,33 +3127,34 @@ "\n", "# read RSP template\n", "with open(xbm_iwr_template_file) as template_obj:\n", - " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", - " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - " \n", + "\n", " # dictionary holding search keys and replacement values to update the template file\n", - " d = {\"XBM\": f\"../../input_files/cm2015B_{scenario}.xbm\",\"IWR\": f\"../../input_files/cm2015B_{scenario}.iwr\"}\n", - " \n", + " d = {\n", + " \"XBM\": f\"../../input_files/cm2015B_{scenario}.xbm\",\n", + " \"IWR\": f\"../../input_files/cm2015B_{scenario}.iwr\",\n", + " }\n", + "\n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - " \n", + "\n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - " \n", + "\n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"cm2015B_{scenario}.rsp\")\n", - " \n", + "\n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - " \n", + "\n", " # construct simulated basin path\n", " simulated_basin_path = f\"cm2015B_{scenario}\"\n", "\n", @@ -3162,7 +3162,7 @@ " print(f\"Running: {scenario}\")\n", " os.chdir(simulated_scenario_dir)\n", "\n", - " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n" + " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])" ] }, { @@ -3194,14 +3194,16 @@ ], "source": [ "# Example with Granby Lake\n", - "zip_file_path = os.path.join(home_dir, 'data', 'Granby_Dataset.zip')\n", - "final_directory = os.path.join(home_dir, 'data/')\n", + "zip_file_path = os.path.join(home_dir, \"data\", \"Granby_Dataset.zip\")\n", + "final_directory = os.path.join(home_dir, \"data/\")\n", "\n", - "!unzip $zip_file_path -d $final_directory \n", - "granby_hmm, granby_hist, granby_hist_mean, granby_hist_1p = stm.read_xre(os.path.join(home_dir,\"data/Upper_Colorado/\"), 'Granby')\n", + "!unzip $zip_file_path -d $final_directory\n", + "granby_hmm, granby_hist, granby_hist_mean, granby_hist_1p = stm.read_xre(\n", + " os.path.join(home_dir, \"data/Upper_Colorado/\"), \"Granby\"\n", + ")\n", "\n", "# Plot quantiles\n", - "stm.plot_res_quantiles(granby_hmm, granby_hist_mean, 'Lake Granby')" + "stm.plot_res_quantiles(granby_hmm, granby_hist_mean, \"Lake Granby\")" ] }, { @@ -3241,7 +3243,7 @@ ], "source": [ "# using the output of the above `read_xre` function as inputs\n", - "stm.plot_reservoir_boxes(granby_hmm, granby_hist, 'Lake Granby')" + "stm.plot_reservoir_boxes(granby_hmm, granby_hist, \"Lake Granby\")" ] }, { diff --git a/notebooks/N5_Batch_Modification.ipynb b/notebooks/N5_Batch_Modification.ipynb index 6ef477a..5ed2e07 100644 --- a/notebooks/N5_Batch_Modification.ipynb +++ b/notebooks/N5_Batch_Modification.ipynb @@ -33,12 +33,13 @@ "import logging\n", "import os\n", "import pickle\n", - "from string import Template\n", "import subprocess\n", + "from string import Template\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd \n", + "import pandas as pd\n", + "\n", "import statemodify as stm" ] }, @@ -80,12 +81,7 @@ "statemod_exe = os.path.join(root_dir, \"statemod\")\n", "\n", "# data directory and root name for the target basin\n", - "data_dir = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"cm2015_StateMod\",\n", - " \"StateMod\"\n", - ")\n", + "data_dir = os.path.join(home_dir, \"data\", \"cm2015_StateMod\", \"StateMod\")\n", "\n", "# directory to the target basin input files with root name for the basin\n", "basin_path = os.path.join(data_dir, \"cm2015B\")\n", @@ -93,16 +89,12 @@ "# scenarios output directory\n", "scenarios_dir = os.path.join(data_dir, \"scenarios\")\n", "\n", - "#parquet output directory\n", - "parquet_dir=os.path.join(data_dir, \"parquet\")\n", + "# parquet output directory\n", + "parquet_dir = os.path.join(data_dir, \"parquet\")\n", "\n", "\n", "# path to template file\n", - "multi_template_file = os.path.join(\n", - " home_dir,\n", - " \"data\",\n", - " \"cm2015B_template_multi.rsp\"\n", - ")" + "multi_template_file = os.path.join(home_dir, \"data\", \"cm2015B_template_multi.rsp\")" ] }, { @@ -167,13 +159,9 @@ "# problem dictionary\n", "problem_dict = {\n", " \"n_samples\": 1,\n", - " 'num_vars': 3,\n", - " 'names': ['modify_eva', 'modify_ddr', 'modify_ddm'],\n", - " 'bounds': [\n", - " [-0.5, 1.0],\n", - " None,\n", - " [0.5, 1.0]\n", - " ],\n", + " \"num_vars\": 3,\n", + " \"names\": [\"modify_eva\", \"modify_ddr\", \"modify_ddm\"],\n", + " \"bounds\": [[-0.5, 1.0], None, [0.5, 1.0]],\n", " # additional settings for each function\n", " \"modify_eva\": {\n", " \"seed_value\": seed_value,\n", @@ -181,7 +169,7 @@ " \"scenario\": scenario,\n", " \"basin_name\": basin_name,\n", " \"query_field\": \"id\",\n", - " \"ids\": [\"10008\", \"10009\"]\n", + " \"ids\": [\"10008\", \"10009\"],\n", " },\n", " \"modify_ddr\": {\n", " \"seed_value\": seed_value,\n", @@ -191,7 +179,7 @@ " \"query_field\": \"id\",\n", " \"ids\": [\"3600507.01\", \"3600507.02\"],\n", " \"admin\": [1, None],\n", - " \"on_off\": [1, 1]\n", + " \"on_off\": [1, 1],\n", " },\n", " \"modify_ddm\": {\n", " \"seed_value\": seed_value,\n", @@ -199,8 +187,8 @@ " \"scenario\": scenario,\n", " \"basin_name\": basin_name,\n", " \"query_field\": \"id\",\n", - " \"ids\": [\"3600507\", \"3600603\"]\n", - " }\n", + " \"ids\": [\"3600507\", \"3600603\"],\n", + " },\n", "}\n", "\n", "# run in batch\n", @@ -511,33 +499,35 @@ "\n", "# read RSP template\n", "with open(multi_template_file) as template_obj:\n", - " \n", " # read in file\n", " template_rsp = Template(template_obj.read())\n", "\n", " for i in sample:\n", - " \n", " # create scenario name\n", " scenario = f\"S{i}_{realization}\"\n", - " \n", + "\n", " # dictionary holding search keys and replacement values to update the template file\n", - " d = {\"EVA\": f\"../../input_files/cm2015B_{scenario}.eva\",\"DDM\": f\"../../input_files/cm2015B_{scenario}.ddm\",\"DDR\": f\"../../input_files/cm2015B_{scenario}.ddr\"}\n", - " \n", + " d = {\n", + " \"EVA\": f\"../../input_files/cm2015B_{scenario}.eva\",\n", + " \"DDM\": f\"../../input_files/cm2015B_{scenario}.ddm\",\n", + " \"DDR\": f\"../../input_files/cm2015B_{scenario}.ddr\",\n", + " }\n", + "\n", " # update the template\n", " new_rsp = template_rsp.safe_substitute(d)\n", - " \n", + "\n", " # construct simulated scenario directory\n", " simulated_scenario_dir = os.path.join(scenarios_dir, scenario)\n", " if not os.path.exists(simulated_scenario_dir):\n", " os.makedirs(simulated_scenario_dir)\n", - " \n", + "\n", " # target rsp file\n", " rsp_file = os.path.join(simulated_scenario_dir, f\"cm2015B_{scenario}.rsp\")\n", - " \n", + "\n", " # write updated rsp file\n", " with open(rsp_file, \"w\") as f1:\n", " f1.write(new_rsp)\n", - " \n", + "\n", " # construct simulated basin path\n", " simulated_basin_path = f\"cm2015B_{scenario}\"\n", "\n", @@ -546,16 +536,22 @@ " os.chdir(simulated_scenario_dir)\n", "\n", " subprocess.call([statemod_exe, simulated_basin_path, \"-simulate\"])\n", - " \n", - " #Save output to parquet files \n", - " print('creating parquet for ' + scenario)\n", - " \n", - " output_directory = os.path.join(parquet_dir+ \"/scenario/\"+ scenario)\n", - " \n", + "\n", + " # Save output to parquet files\n", + " print(\"creating parquet for \" + scenario)\n", + "\n", + " output_directory = os.path.join(parquet_dir + \"/scenario/\" + scenario)\n", + "\n", " if not os.path.exists(output_directory):\n", " os.makedirs(output_directory)\n", - " \n", - " stm.xdd.convert_xdd(output_path=output_directory,allow_overwrite=False,xdd_files=scenarios_dir + \"/\"+ scenario + \"/cm2015B_\"+scenario+\".xdd\",id_subset=['3601008'],parallel_jobs=2)\n" + "\n", + " stm.xdd.convert_xdd(\n", + " output_path=output_directory,\n", + " allow_overwrite=False,\n", + " xdd_files=scenarios_dir + \"/\" + scenario + \"/cm2015B_\" + scenario + \".xdd\",\n", + " id_subset=[\"3601008\"],\n", + " parallel_jobs=2,\n", + " )" ] }, { diff --git a/statemodify/xdd.py b/statemodify/xdd.py index 4355581..e59a3cb 100644 --- a/statemodify/xdd.py +++ b/statemodify/xdd.py @@ -21,7 +21,7 @@ def __init__( xdd_files: Union[str, Path, list[Union[str, Path]]] = "**/*.xdd", id_subset: Union[None, list[str]] = None, parallel_jobs: int = 4, - preserve_string_dtype: bool = True + preserve_string_dtype: bool = True, ): """Convert object for transforming StateMod output .xdd files into compressed, columnar .parquet files. @@ -272,7 +272,7 @@ def _parse_file( output_path: str, id_subset: Union[None, list[str]], preserve_string_dtype: bool, - field_dtypes: dict + field_dtypes: dict, ): data = [] with open(file) as f: @@ -297,7 +297,7 @@ def _parse_file( if preserve_string_dtype is False: df = df.astype(field_dtypes) - df.to_parquet(f"{output_path}/{Path(file).stem}.parquet") + df.to_parquet(f"{output_path}/{Path(file).stem}.parquet") def convert_xdd( @@ -307,7 +307,7 @@ def convert_xdd( xdd_files: Union[str, Path, list[Union[str, Path]]] = "**/*.xdd", id_subset: Union[None, list[str]] = None, parallel_jobs: int = 4, - preserve_string_dtype: bool = True + preserve_string_dtype: bool = True, ): """Convert StateMod output .xdd files to compressed, columnar .parquet files.