Generation of spe11x_performance_time_series_detailed.csv

examples/finner_grids/spe11b.txt

@@ -52,6 +52,4 @@ PERM7    1e-5 PORO7 1e-6 THCONR7 2.00
 1000 1000 0.1 1     0 10 1     0 10
   25    5 0.1 1 0.035 10 1     0 10
   25    5 0.1 1 0.035 10 1 0.035 10
-  50   25 0.1 1     0 10 1     0 10
- 400   50 0.1 1     0 10 1     0 10
- 500  100 0.1 1     0 10 1     0 10
+ 950    5 0.1 1     0 10 1     0 10

src/pyopmspe11/core/pyopmspe11.py

@@ -68,11 +68,11 @@ def pyopmspe11():
         # Write used opm related files
         opm_files(dic)
 
-    if dic["mode"] in ["all", "flow", "deck_flow", "deck_flow_data"]:
+    if dic["mode"] in ["all", "flow", "deck_flow", "flow_data", "deck_flow_data"]:
         # Run the simulations
         simulations(dic, dic["fol"].upper(), "flow")
 
-    if dic["mode"] in ["all", "data", "deck_flow_data", "data_plot"]:
+    if dic["mode"] in ["all", "data", "flow_data", "deck_flow_data", "data_plot"]:
         # Write the data
         if not os.path.exists(f"{dic['exe']}/{dic['fol']}/data"):
             os.system(f"mkdir {dic['exe']}/{dic['fol']}/data")
@@ -103,8 +103,8 @@ def load_parser():
         help="Run the whole framework ('all'), only create decks ('deck'), "
         "only run flow ('flow'), only write benchmark data ('data'), "
         "only create plots ('plot'), deck and run ('deck_flow'), "
-        "data and plot (data_plot), or deck, run, and data "
-        "(deck_flow_data) ('deck_flow' by default).",
+        "data and plot ('data_plot'), run and data ('flow_data'),  or deck, "
+        "run, and data ('deck_flow_data') ('deck_flow' by default).",
     )
     parser.add_argument(
         "-c",

src/pyopmspe11/visualization/data.py

@@ -212,7 +212,7 @@ def opm_files(dig):
 
 
 def performance(dig):
-    """Write the performance within the benchmark format SECONDS_IN_YEAR"""
+    """Write the performance within the benchmark format"""
     dil = {"infosteps": []}
     dil["times_data"] = np.linspace(
         0, dig["times"][-1], round(dig["times"][-1] / dig["sparse_t"]) + 1
@@ -248,6 +248,7 @@ def performance(dig):
     dil["tsteps"] = np.array(
         [86400 * infostep[1] * infostep[11] for infostep in dil["infosteps"]]
     )
+    dil["alltsteps"] = np.array([86400 * infostep[1] for infostep in dil["infosteps"]])
     if dig["use"] == "opm":
         fgip = dig["smspec"]["FGIP"]
         times = 86400.0 * dig["smspec"]["TIME"] - dig["time_initial"]
@@ -293,6 +294,30 @@ def performance(dig):
         encoding="utf8",
     ) as file:
         file.write("\n".join(dil["text"]))
+    dil["text"] = []
+    dil["text"].append(
+        "# t [s], tstep [s], fsteps [-], mass [kg], dof [-], nliter [-], "
+        + "nres [-], liniter [-], runtime [s], tlinsol [s]"
+    )
+    for j, time in enumerate(infotimes):
+        dil["text"].append(
+            f"{time:.3e}, "
+            + f"{dil['alltsteps'][j]:.3e}, "
+            + f"{dil['fsteps'][j]:.3e}, "
+            + f"{interp_fgip(time):.3e}, "
+            + f"{dig['dof'] * dig['nocellsa']:.3e}, "
+            + f"{dil['nliters'][j]:.3e}, "
+            + f"{dil['nress'][j]:.3e}, "
+            + f"{dil['liniters'][j]:.3e}, "
+            + f"{dil['runtimes'][j]:.3e}, "
+            + f"{dil['tlinsols'][j]:.3e}"
+        )
+    with open(
+        f"{dig['where']}/{dig['case']}_performance_time_series_detailed.csv",
+        "w",
+        encoding="utf8",
+    ) as file:
+        file.write("\n".join(dil["text"]))
 
 
 def create_from_summary(dig, dil):
@@ -708,14 +733,24 @@ def generate_arrays(dig, dil, names, t_n):
     dil["sgas_array"][dig["actind"]] = sgas
     dil["gden_array"][dig["actind"]] = rhog * (sgas > 0.0)
     dil["wden_array"][dig["actind"]] = rhow
-    dil["xco2_array"][dig["actind"]] = np.divide(co2_d, co2_d + h2o_l)
     dil["tco2_array"][dig["actind"]] = co2_d + co2_g
+    dil = compute_xco2(dig, dil, co2_d, h2o_l)
     if dig["case"] != "spe11a":
         h2o_v = rvv * rhog * sgas * dig["porva"] * WAT_DEN_REF / GAS_DEN_REF
         dil = compute_xh20(dig, dil, h2o_v, co2_g)
     return dil
 
 
+def compute_xco2(dig, dil, co2_d, h2o_l):
+    """Compute the mass fraction of CO2 in liquid"""
+    mliq = co2_d + h2o_l
+    xco2 = 0.0 * co2_d
+    inds = mliq > 0.0
+    xco2[inds] = np.divide(co2_d[inds], mliq[inds])
+    dil["xco2_array"][dig["actind"]] = xco2
+    return dil
+
+
 def compute_xh20(dig, dil, h2o_v, co2_g):
     """Compute the mass fraction of water in vapour"""
     mgas = h2o_v + co2_g

src/pyopmspe11/visualization/plotting.py

@@ -144,60 +144,55 @@ def plot_results(dic):
 
 def performance(dic):
     """time solver plots"""
-    csv = np.genfromtxt(
-        f"{dic['exe']}/{dic['folders'][0]}/data/{dic['case']}_performance_time_series.csv",
-        delimiter=",",
-        skip_header=1,
-    )
-    times = [csv[i][0] for i in range(csv.shape[0])]
-    dic["fig"] = plt.figure(figsize=(40, 75))
-    plots = [
-        "tstep",
-        "fsteps",
-        "mass",
-        "dof",
-        "nliter",
-        "nres",
-        "liniter",
-        "runtime",
-        "tlinsol",
-    ]
-    ylabels = ["s", "\\#", "kg", "\\#", "\\#", "\\#", "\\#", "s", "s"]
-    for k, (plot, ylabel) in enumerate(zip(plots, ylabels)):
-        axis = dic["fig"].add_subplot(9, 5, k + 1)
-        for nfol, fol in enumerate(dic["folders"]):
-            csv = np.genfromtxt(
-                f"{dic['exe']}/{fol}/data/{dic['case']}_performance_time_series.csv",
-                delimiter=",",
-                skip_header=1,
-            )
-            labels = [
-                f"sum={sum((csv[i][1] for i in range(csv.shape[0]))):.3e}",
-                f"sum={sum((csv[i][2] for i in range(csv.shape[0]))):.3e}",
-                f"max={max((csv[i][3] for i in range(csv.shape[0]))):.3e}",
-                f"max={csv[-1][4]:.3e}",
-                f"sum={sum((csv[i][5] for i in range(csv.shape[0]))):.3e}",
-                f"sum={sum((csv[i][6] for i in range(csv.shape[0]))):.3e}",
-                f"sum={sum((csv[i][7] for i in range(csv.shape[0]))):.3e}",
-                f"sum={sum((csv[i][8] for i in range(csv.shape[0]))):.3e}",
-                f"sum={sum((csv[i][9] for i in range(csv.shape[0]))):.3e}",
-            ]
-            times = [csv[i][0] / dic["tscale"] for i in range(csv.shape[0])]
-            labels[k] += f" ({fol})"
-            axis.step(
-                times,
-                [csv[i][k + 1] for i in range(csv.shape[0])],
-                lw=2,
-                color=dic["colors"][nfol],
-                label=labels[k],
-            )
-            axis.set_title(plot + f", {dic['case']}")
-            axis.set_ylabel(ylabel)
-            axis.set_xlabel(f"Time [{dic['tlabel']}]")
-            axis.legend()
-    dic["fig"].savefig(
-        f"{dic['where']}/{dic['case']}_performance.png", bbox_inches="tight"
-    )
+    for kind in ["", "_detailed"]:
+        dic["fig"] = plt.figure(figsize=(40, 75))
+        plots = [
+            "tstep",
+            "fsteps",
+            "mass",
+            "dof",
+            "nliter",
+            "nres",
+            "liniter",
+            "runtime",
+            "tlinsol",
+        ]
+        ylabels = ["s", "\\#", "kg", "\\#", "\\#", "\\#", "\\#", "s", "s"]
+        for k, (plot, ylabel) in enumerate(zip(plots, ylabels)):
+            axis = dic["fig"].add_subplot(9, 5, k + 1)
+            for nfol, fol in enumerate(dic["folders"]):
+                csv = np.genfromtxt(
+                    f"{dic['exe']}/{fol}/data/{dic['case']}_performance_time_series{kind}.csv",
+                    delimiter=",",
+                    skip_header=1,
+                )
+                labels = [
+                    f"sum={sum((csv[i][1] for i in range(csv.shape[0]))):.3e}",
+                    f"sum={sum((csv[i][2] for i in range(csv.shape[0]))):.3e}",
+                    f"max={max((csv[i][3] for i in range(csv.shape[0]))):.3e}",
+                    f"max={csv[-1][4]:.3e}",
+                    f"sum={sum((csv[i][5] for i in range(csv.shape[0]))):.3e}",
+                    f"sum={sum((csv[i][6] for i in range(csv.shape[0]))):.3e}",
+                    f"sum={sum((csv[i][7] for i in range(csv.shape[0]))):.3e}",
+                    f"sum={sum((csv[i][8] for i in range(csv.shape[0]))):.3e}",
+                    f"sum={sum((csv[i][9] for i in range(csv.shape[0]))):.3e}",
+                ]
+                times = [csv[i][0] / dic["tscale"] for i in range(csv.shape[0])]
+                labels[k] += f" ({fol})"
+                axis.step(
+                    times,
+                    [csv[i][k + 1] for i in range(csv.shape[0])],
+                    lw=2,
+                    color=dic["colors"][nfol],
+                    label=labels[k],
+                )
+                axis.set_title(plot + f", {dic['case']}")
+                axis.set_ylabel(ylabel)
+                axis.set_xlabel(f"Time [{dic['tlabel']}]")
+                axis.legend()
+        dic["fig"].savefig(
+            f"{dic['where']}/{dic['case']}_performance{kind}.png", bbox_inches="tight"
+        )
 
 
 def sparse_data(dic):