diff --git a/docs/Users_Guide/appendixF.rst b/docs/Users_Guide/appendixF.rst index bc051f5a14..17628c7ee5 100644 --- a/docs/Users_Guide/appendixF.rst +++ b/docs/Users_Guide/appendixF.rst @@ -355,7 +355,7 @@ The first argument for the Plot-Data-Plane tool is the gridded data file to be r 'level': 'Surface', 'units': 'None', 'init': '20050807_000000', 'valid': '20050807_120000', 'lead': '120000', 'accum': '120000' - 'grid': {...} } + 'grid': { ... } } DEBUG 1: Creating postscript file: fcst.ps Special Case for Ensemble-Stat, Series-Analysis, and MTD diff --git a/docs/Users_Guide/config_options.rst b/docs/Users_Guide/config_options.rst index c9a2ba01f6..25515fc524 100644 --- a/docs/Users_Guide/config_options.rst +++ b/docs/Users_Guide/config_options.rst @@ -21,42 +21,42 @@ which are dictionaries themselves. The configuration file language supports the following data types: * Dictionary: - + * Grouping of one or more entries enclosed by curly braces {}. * Array: - + * List of one or more entries enclosed by square braces []. - + * Array elements are separated by commas. * String: - + * A character string enclosed by double quotation marks "". - + * Integer: - + * A numeric integer value. - + * Float: - + * A numeric float value. - + * Boolean: - + * A boolean value (TRUE or FALSE). - + * Threshold: - + * A threshold type (<, <=, ==, !-, >=, or >) followed by a numeric value. - + * The threshold type may also be specified using two letter abbreviations (lt, le, eq, ne, ge, gt). - + * Multiple thresholds may be combined by specifying the logic type of AND (&&) or OR (||). For example, ">=5&&<=10" defines the numbers between 5 and 10 and "==1||==2" defines numbers exactly equal to 1 or 2. - + * Percentile Thresholds: * A threshold type (<, <=, ==, !=, >=, or >), followed by a percentile @@ -65,34 +65,34 @@ The configuration file language supports the following data types: * Note that the two letter threshold type abbreviations (lt, le, eq, ne, ge, gt) are not supported for percentile thresholds. - + * Thresholds may be defined as percentiles of the data being processed in several places: - + * In Point-Stat and Grid-Stat when setting "cat_thresh", "wind_thresh" and "cnt_thresh". - + * In Wavelet-Stat when setting "cat_thresh". - + * In MODE when setting "conv_thresh" and "merge_thresh". - + * In Ensemble-Stat when setting "obs_thresh". - + * When using the "censor_thresh" config option. - + * In the Stat-Analysis "-out_fcst_thresh" and "-out_obs_thresh" job command options. - + * In the Gen-Vx-Mask "-thresh" command line option. - + * The following percentile threshold types are supported: - + * SFP for a percentile of the sample forecast values. e.g. ">SFP33.3" means greater than the 33.3-rd forecast percentile. - + * SOP for a percentile of the sample observation values. e.g. ">SOP75" means greater than the 75-th observation percentile. - + * SFCP for a percentile of the sample forecast climatology values. e.g. ">SFCP90" means greater than the 90-th forecast climatology percentile. @@ -101,11 +101,11 @@ The configuration file language supports the following data types: e.g. ">SOCP90" means greater than the 90-th observation climatology percentile. For backward compatibility, the "SCP" threshold type is processed the same as "SOCP". - + * USP for a user-specified percentile threshold. e.g. "5.0 threshold to the observations and then chooses a forecast threshold which results in a frequency bias of 1. The frequency bias can be any float value > 0.0. - + * FCDP for forecast climatological distribution percentile thresholds. These thresholds require that the forecast climatological mean and standard deviation be defined using the "climo_mean" and "climo_stdev" @@ -130,7 +130,7 @@ The configuration file language supports the following data types: However these thresholds are defined using the observation climatological mean and standard deviation rather than the forecast climatological data. For backward compatibility, the "CDP" threshold type is processed the - same as "OCDP". + same as "OCDP". * When percentile thresholds of type SFP, SOP, SFCP, SOCP, FCDP, or OCDP are requested for continuous filtering thresholds (cnt_thresh), wind speed @@ -140,13 +140,13 @@ The configuration file language supports the following data types: bins which span the values from 0 to 100. For example, ==OCDP25 is automatically expanded to 4 percentile bins: >=OCDP0&&=OCDP25&&=OCDP50&&=OCDP75&&<=OCDP100 - + * When sample percentile thresholds of type SFP, SOP, SFCP, SOCP, or FBIAS are requested, MET recomputes the actual percentile that the threshold represents. If the requested percentile and actual percentile differ by more than 5%, a warning message is printed. This may occur when the sample size is small or the data values are not truly continuous. - + * When percentile thresholds of type SFP, SOP, SFCP, SOCP, or USP are used, the actual threshold value is appended to the FCST_THRESH and OBS_THRESH output columns. For example, if the 90-th percentile of the current set @@ -167,20 +167,20 @@ The configuration file language supports the following data types: Users are encouraged to replace the deprecated SCP and CDP threshold types with the updated SOCP and OCDP types, respectively. - + * Piecewise-Linear Function (currently used only by MODE): - + * A list of (x, y) points enclosed in parenthesis (). - + * The (x, y) points are *NOT* separated by commas. - + * User-defined function of a single variable: - + * Left side is a function name followed by variable name in parenthesis. - + * Right side is an equation which includes basic math functions (+,-,*,/), built-in functions (listed below), or other user-defined functions. - + * Built-in functions include: sin, cos, tan, sind, cosd, tand, asin, acos, atan, asind, acosd, atand, atan2, atan2d, arg, argd, log, exp, log10, exp10, sqrt, abs, min, max, @@ -401,7 +401,7 @@ References: | `Office Note 388 GRIB1 `_ | `A Guide to the Code Form FM 92-IX Ext. GRIB Edition 1 `_ -| +| GRIB2 table files begin with "grib2" prefix and end with a ".txt" suffix. The first line of the file must contain GRIB2. @@ -418,7 +418,7 @@ The following lines consist of 8 integers followed by 3 strings. | Column 9: variable name | Column 10: variable description | Column 11: units -| +| References: @@ -502,7 +502,7 @@ parallelization: * :code:`grid_ens_prod` * :code:`mode` -**Thread Binding** +**Thread Binding** It is normally beneficial to bind threads to particular cores, sometimes called *affinitization*. There are a few reasons for this, but at the very least it @@ -618,7 +618,7 @@ writing of NetCDF files within MET significantly. output_precision ---------------- - + The "output_precision" entry in ConfigConstants defines the precision (number of significant decimal places) to be written to the ASCII output files. Setting this option in the config file of one of the tools will @@ -632,7 +632,7 @@ override the default value set in ConfigConstants. tmp_dir ------- - + The "tmp_dir" entry in ConfigConstants defines the directory for the temporary files. The directory must exist and be writable. The environment variable MET_TMP_DIR overrides the default value at the configuration file. @@ -669,7 +669,7 @@ used. message_type_map ---------------- - + The "message_type_map" entry is an array of dictionaries, each containing a "key" string and "val" string. This defines a mapping of input strings to output message types. This mapping is applied in ASCII2NC when @@ -693,7 +693,7 @@ types. model ----- - + The "model" entry specifies a name for the model being verified. This name is written to the MODEL column of the ASCII output generated. If you're verifying multiple models, you should choose descriptive model names (no @@ -706,7 +706,7 @@ e.g. model = "GFS"; desc ---- - + The "desc" entry specifies a user-specified description for each verification task. This string is written to the DESC column of the ASCII output generated. It may be set separately in each "obs.field" verification task @@ -736,10 +736,10 @@ the configuration file obtype value is written. obtype = "ANALYS"; .. _regrid: - + regrid ------ - + The "regrid" entry is a dictionary containing information about how to handle input gridded data files. The "regrid" entry specifies regridding logic using the following entries: @@ -747,17 +747,17 @@ using the following entries: * The "to_grid" entry may be set to NONE, FCST, OBS, a named grid, the path to a gridded data file defining the grid, or an explicit grid specification string. - + * to_grid = NONE; To disable regridding. - + * to_grid = FCST; To regrid observations to the forecast grid. - + * to_grid = OBS; To regrid forecasts to the observation grid. - + * to_grid = "G218"; To regrid both to a named grid. - + * to_grid = "path"; To regrid both to a grid defined by a file. - + * to_grid = "spec"; To define a grid specification string, as described in :ref:`appendixB`. @@ -768,29 +768,29 @@ using the following entries: write bad data for the current point. * The "method" entry defines the regridding method to be used. - + * Valid regridding methods: - + * MIN for the minimum value - + * MAX for the maximum value - + * MEDIAN for the median value - + * UW_MEAN for the unweighted average value - + * DW_MEAN for the distance-weighted average value (weight = distance^-2) - + * AW_MEAN for an area-weighted mean when regridding from high to low resolution grids (width = 1) - + * LS_FIT for a least-squares fit - + * BILIN for bilinear interpolation (width = 2) - + * NEAREST for the nearest grid point (width = 1) - + * BUDGET for the mass-conserving budget interpolation * The budget interpolation method is often used for precipitation @@ -806,15 +806,15 @@ using the following entries: * FORCE to compare gridded data directly with no interpolation as long as the grid x and y dimensions match. - + * UPPER_LEFT for the upper left grid point (width = 1) - + * UPPER_RIGHT for the upper right grid point (width = 1) - + * LOWER_RIGHT for the lower right grid point (width = 1) - + * LOWER_LEFT for the lower left grid point (width = 1) - + * MAXGAUSS to compute the maximum value in the neighborhood and apply a Gaussian smoother to the result @@ -842,7 +842,7 @@ using the following entries: regridding step. The conversion operation is applied first, followed by the censoring operation. Note that these operations are limited in scope. They are only applied if defined within the regrid dictionary itself. - Settings defined at higher levels of config file context are not applied. + Settings defined at higher levels of config file context are not applied. .. code-block:: none @@ -861,7 +861,7 @@ using the following entries: fcst ---- - + The "fcst" entry is a dictionary containing information about the field(s) to be verified. This dictionary may include the following entries: @@ -1046,7 +1046,7 @@ to be verified. This dictionary may include the following entries: the analysis. For example, the following settings exclude matched pairs where the observation value differs from the forecast or climatological mean values by more than 10: - + .. code-block:: none mpr_column = [ "ABS(OBS-FCST)", "ABS(OBS-CLIMO_MEAN)" ]; @@ -1144,70 +1144,70 @@ File-format specific settings for the "field" entry: * `GRIB1 Product Definition Section `_ * `GRIB2 Product Definition Section `_ - + * The "level" entry specifies a level type and value: - + * ANNN for accumulation interval NNN - + * ZNNN for vertical level NNN - + * ZNNN-NNN for a range of vertical levels - + * PNNN for pressure level NNN in hPa - + * PNNN-NNN for a range of pressure levels in hPa - + * LNNN for a generic level type - + * RNNN for a specific GRIB record number - + * The "GRIB_lvl_typ" entry is an integer specifying the level type. - + * The "GRIB_lvl_val1" and "GRIB_lvl_val2" entries are floats specifying the first and second level values. - + * The "GRIB_ens" entry is a string specifying NCEP's usage of the extended PDS for ensembles. Set to "hi_res_ctl", "low_res_ctl", "+n", or "-n", for the n-th ensemble member. - + * The GRIB1_ptv entry is an integer specifying the GRIB1 parameter table version number. - + * The GRIB1_code entry is an integer specifying the GRIB1 code (wgrib kpds5 value). - + * The GRIB1_center is an integer specifying the originating center. - + * The GRIB1_subcenter is an integer specifying the originating subcenter. - + * The GRIB1_tri is an integer specifying the time range indicator. - + * The GRIB2_mtab is an integer specifying the master table number. - + * The GRIB2_ltab is an integer specifying the local table number. - + * The GRIB2_disc is an integer specifying the GRIB2 discipline code. - + * The GRIB2_parm_cat is an integer specifying the parameter category code. - + * The GRIB2_parm is an integer specifying the parameter code. - + * The GRIB2_pdt is an integer specifying the product definition template (Table 4.0). - + * The GRIB2_process is an integer specifying the generating process (Table 4.3). - + * The GRIB2_cntr is an integer specifying the originating center. - + * The GRIB2_ens_type is an integer specifying the ensemble type (Table 4.6). - + * The GRIB2_der_type is an integer specifying the derived product type (Table 4.7). - + * The GRIB2_stat_type is an integer specifying the statistical processing type (Table 4.10). @@ -1234,13 +1234,13 @@ File-format specific settings for the "field" entry: template values are 1 and 2, respectively: GRIB2_ipdtmpl_index=[8, 26]; GRIB2_ipdtmpl_val=[1, 2]; - + * NetCDF (from MET tools, CF-compliant, p_interp, and wrf_interp): - + * The "name" entry specifies the NetCDF variable name. - + * The "level" entry specifies the dimensions to be used: - + * (i,...,j,*,*) for a single field, where i,...,j specifies fixed dimension values and *,* specifies the two dimensions for the gridded field. @ specifies the vertical level value or time value @@ -1271,10 +1271,10 @@ File-format specific settings for the "field" entry: ]; * Python (using PYTHON_NUMPY or PYTHON_XARRAY): - + * The Python interface for MET is described in Appendix F of the MET User's Guide. - + * Two methods for specifying the Python command and input file name are supported. For tools which read a single gridded forecast and/or observation file, both options work. However, only the second option @@ -1282,13 +1282,13 @@ File-format specific settings for the "field" entry: as Ensemble-Stat, Series-Analysis, and MTD. Option 1: - + * On the command line, replace the path to the input gridded data file with the constant string PYTHON_NUMPY or PYTHON_XARRAY. - + * Specify the configuration "name" entry as the Python command to be executed to read the data. - + * The "level" entry is not required for Python. For example: @@ -1303,14 +1303,14 @@ File-format specific settings for the "field" entry: * On the command line, leave the path to the input gridded data as is. - + * Set the configuration "file_type" entry to the constant PYTHON_NUMPY or PYTHON_XARRAY. - + * Specify the configuration "name" entry as the Python command to be executed to read the data, but replace the input gridded data file with the constant MET_PYTHON_INPUT_ARG. - + * The "level" entry is not required for Python. For example: @@ -1337,7 +1337,7 @@ File-format specific settings for the "field" entry: init_time = "20120619_12"; valid_time = "20120620_00"; lead_time = "12"; - + field = [ { name = "APCP"; @@ -1453,16 +1453,16 @@ or that filtering by station ID may also be accomplished using the "mask.sid" option. However, when using the "sid_inc" option, statistics are reported separately for each masking region. - + * The "sid_exc" entry is an array of station ID groups indicating which station ID's should be excluded from the verification task. - + * Each element in the "sid_inc" and "sid_exc" arrays is either the name of a single station ID or the full path to a station ID group file name. A station ID group file consists of a name for the group followed by a list of station ID's. All of the station ID's indicated will be concatenated into one long list of station ID's to be included or excluded. - + * As with "message_type" above, the "sid_inc" and "sid_exc" settings can be placed in the in the "field" array element to control which station ID's are included or excluded for each verification task. @@ -1473,7 +1473,7 @@ or climo_mean ---------- - + The "climo_mean" dictionary specifies climatology mean data to be read by the Grid-Stat, Point-Stat, Ensemble-Stat, and Series-Analysis tools. It can be set inside the "fcst" and "obs" dictionaries to specify separate forecast and @@ -1496,7 +1496,7 @@ the climatology file names and fields to be used. * The "time_interp_method" entry specifies how the climatology data should be interpolated in time to the forecast valid time: - + * NEAREST for data closest in time * UW_MEAN for average of data before and after * DW_MEAN for linear interpolation in time of data before and after @@ -1519,16 +1519,16 @@ the climatology file names and fields to be used. .. code-block:: none climo_mean = { - + file_name = [ "/path/to/climatological/mean/files" ]; field = []; - + regrid = { method = NEAREST; width = 1; vld_thresh = 0.5; } - + time_interp_method = DW_MEAN; day_interval = 31; hour_interval = 6; @@ -1536,7 +1536,7 @@ the climatology file names and fields to be used. climo_stdev ----------- - + The "climo_stdev" dictionary specifies climatology standard deviation data to be read by the Grid-Stat, Point-Stat, Ensemble-Stat, and Series-Analysis tools. It can be set inside the "fcst" and "obs" dictionaries to specify @@ -1591,7 +1591,7 @@ dictionaries, as shown below. climo_cdf --------- - + The "climo_cdf" dictionary specifies how the the observation climatological mean ("climo_mean") and standard deviation ("climo_stdev") data are used to evaluate model performance relative to where the observation value falls @@ -1723,11 +1723,11 @@ The "mask_missing_flag" entry specifies how missing data should be handled in the Wavelet-Stat and MODE tools: * NONE to perform no masking of missing data - + * FCST to mask the forecast field with missing observation data - + * OBS to mask the observation field with missing forecast data - + * BOTH to mask both fields with missing data from the other .. code-block:: none @@ -1770,7 +1770,7 @@ in the following ways: three digit grid number. Supplying a value of "FULL" indicates that the verification should be performed over the entire grid on which the data resides. - See: `ON388 - TABLE B, GRID IDENTIFICATION (PDS Octet 7), MASTER LIST OF NCEP STORAGE GRIDS, GRIB Edition 1 (FM92) `_. + See: `ON388 - TABLE B, GRID IDENTIFICATION (PDS Octet 7), MASTER LIST OF NCEP STORAGE GRIDS, GRIB Edition 1 (FM92) `_. The "grid" entry can be the gridded data file defining grid. * The "poly" entry contains a comma-separated list of files that define @@ -1850,7 +1850,7 @@ in the following ways: * The "sid" entry is an array of strings which define groups of observation station ID's over which to compute statistics. Each entry in the array is either a filename of a comma-separated list. - + * For a filename, the strings are whitespace-separated. The first string is the mask "name" and the remaining strings are the station ID's to be used. @@ -1929,10 +1929,10 @@ bootstrap confidence intervals. The interval variable indicates what method should be used for computing bootstrap confidence intervals: * The "interval" entry specifies the confidence interval method: - + * BCA for the BCa (bias-corrected percentile) interval method is highly accurate but computationally intensive. - + * PCTILE uses the percentile method which is somewhat less accurate but more efficient. @@ -1965,7 +1965,7 @@ should be used for computing bootstrap confidence intervals: documentation of the `GNU Scientific Library `_ for a listing of the random number generators available for use. - + * The "seed" entry may be set to a specific value to make the computation of bootstrap confidence intervals fully repeatable. When left empty the random number generator seed is chosen automatically which will lead @@ -1994,11 +1994,11 @@ This dictionary may include the following entries: * The "field" entry specifies to which field(s) the interpolation method should be applied. This does not apply when doing point verification with the Point-Stat or Ensemble-Stat tools: - + * FCST to interpolate/smooth the forecast field. - + * OBS to interpolate/smooth the observation field. - + * BOTH to interpolate/smooth both the forecast and the observation. * The "vld_thresh" entry specifies a number between 0 and 1. When @@ -2033,38 +2033,38 @@ This dictionary may include the following entries: * The "method" entry is an array of interpolation procedures to be applied to the points in the box: - + * MIN for the minimum value - + * MAX for the maximum value - + * MEDIAN for the median value - + * UW_MEAN for the unweighted average value - + * DW_MEAN for the distance-weighted average value where weight = distance^-2 * LS_FIT for a least-squares fit - + * BILIN for bilinear interpolation (width = 2) - + * NEAREST for the nearest grid point (width = 1) - + * BEST for the value closest to the observation - + * UPPER_LEFT for the upper left grid point (width = 1) * UPPER_RIGHT for the upper right grid point (width = 1) - + * LOWER_RIGHT for the lower right grid point (width = 1) - + * LOWER_LEFT for the lower left grid point (width = 1) * GAUSSIAN for the Gaussian kernel * MAXGAUSS for the maximum value followed by a Gaussian smoother - + * GEOG_MATCH for the nearest grid point where the land/sea mask and geography criteria are satisfied @@ -2096,7 +2096,7 @@ This dictionary may include the following entries: land_mask --------- - + The "land_mask" dictionary defines the land/sea mask field used when verifying at the surface. The "flag" entry enables/disables this logic. When enabled, the "message_type_group_map" dictionary must contain entries @@ -2124,7 +2124,7 @@ The "land_mask.flag" entry may be set separately in each "obs.field" entry. topo_mask --------- - + The "topo_mask" dictionary defines the model topography field used when verifying at the surface. The flag entry enables/disables this logic. When enabled, the "message_type_group_map" dictionary must contain an entry @@ -2154,7 +2154,7 @@ The "topo_mask.flag" entry may be set separately in each "obs.field" entry. hira ---- - + The "hira" entry is a dictionary that is very similar to the "interp" and "nbrhd" entries. It specifies information for applying the High Resolution Assessment (HiRA) verification logic in Point-Stat. HiRA is analogous to @@ -2207,15 +2207,15 @@ This dictionary may include the following entries: output_flag ----------- - + The "output_flag" entry is a dictionary that specifies what verification methods should be applied to the input data. Options exist for each output line type from the MET tools. Each line type may be set to one of: * NONE to skip the corresponding verification method - + * STAT to write the verification output only to the ".stat" output file - + * BOTH to write to the ".stat" output file as well the optional "_type.txt" file, a more readable ASCII file sorted by line type. @@ -2289,7 +2289,7 @@ netcdf output will be generated. nc_pairs_var_name ----------------- - + The "nc_pairs_var_name" entry specifies a string for each verification task in Grid-Stat. This string is parsed from each "obs.field" dictionary entry and is used to construct variable names for the NetCDF matched pairs output @@ -2302,14 +2302,14 @@ For example: | nc_pairs_var_name = "TMP"; | - + .. code-block:: none nc_pairs_var_name = ""; nc_pairs_var_suffix ------------------- - + The "nc_pairs_var_suffix" entry is similar to the "nc_pairs_var_name" entry described above. It is also parsed from each "obs.field" dictionary entry. However, it defines a suffix to be appended to the output variable name. @@ -2334,7 +2334,7 @@ For example: ps_plot_flag ------------ - + The "ps_plot_flag" entry is a boolean value for Wavelet-Stat and MODE indicating whether a PostScript plot should be generated summarizing the verification. @@ -2345,23 +2345,47 @@ the verification. grid_weight_flag ---------------- - + The "grid_weight_flag" specifies how grid weighting should be applied -during the computation of continuous statistics and partial sums. It is -meant to account for grid box area distortion and is often applied to global -Lat/Lon grids. It is only applied for grid-to-grid verification in Grid-Stat -and Ensemble-Stat and is not applied for grid-to-point verification. +during the computation of contingency tables (CTC, MCTC, PCT, and +NBRCTC), partial sums (SL1L2, SAL1L2, VL1L2, and VAL1L2), and statistics +(CNT, CTS, MCTS, PSTD, PRC, PJC, ECLV, NBRCNT, and NBRCTS). +It is meant to account for grid box area distortion and is often applied +to global Lat/Lon grids. It is only applied for grid-to-grid verification +in Grid-Stat and Ensemble-Stat and is not applied for grid-to-point +verification. It can only be defined once at the highest level of config +file context and applies to all verification tasks for that run. + Three grid weighting options are currently supported: -* NONE to disable grid weighting using a constant weight (default). - +* NONE to disable grid weighting using a constant weight of 1.0 (default). + * COS_LAT to define the weight as the cosine of the grid point latitude. This an approximation for grid box area used by NCEP and WMO. - + * AREA to define the weight as the true area of the grid box (km^2). -The weights are ultimately computed as the weight at each grid point divided -by the sum of the weights for the current masking region. +If requested in the config file, the raw grid weights can be written to +the NetCDF output from Grid-Stat and Ensemble-Stat. + +When computing partial sums and continuous statistics, the weights are +first normalized by dividing by the sum of the weights for the current +masking region. When computing contingency tables and deriving statistics, +each contingency table cell contains the sum of the weights of the matching +grid points rather than the integer count of those grid points. Statistics +are derived using these sums of weights rather than the raw counts. + +When no grid weighting is requested (**NONE**), contingency tables are +populated using a default constant weight of 1.0 and the corresponding cells +are written to the output as integer counts for consistency with earlier +versions of MET. + +.. note:: + + The FHO line type is not compatible with grid weighting. If requested + with grid weighting enabled, Grid-Stat prints a warning message and + automatically disables the FHO line type. Users are advised to request the + CTC line type instead. .. code-block:: none @@ -2404,7 +2428,7 @@ The "duplicate_flag" entry specifies how to handle duplicate point observations in Point-Stat and Ensemble-Stat: * NONE to use all point observations (legacy behavior) - + * UNIQUE only use a single observation if two or more observations match. Matching observations are determined if they contain identical latitude, longitude, level, elevation, and time information. @@ -2428,21 +2452,21 @@ in Point-Stat and Ensemble-Stat. Eight techniques are currently supported: * NONE to use all point observations (legacy behavior) - + * NEAREST use only the observation that has the valid time closest to the forecast valid time - + * MIN use only the observation that has the lowest value - + * MAX use only the observation that has the highest value - + * UW_MEAN compute an unweighted mean of the observations - + * DW_MEAN compute a weighted mean of the observations based on the time of the observation - + * MEDIAN use the median observation - + * PERC use the Nth percentile observation where N = obs_perc_value The reporting mechanism for this feature can be activated by specifying @@ -2457,14 +2481,14 @@ in those cases. obs_perc_value -------------- - + Percentile value to use when obs_summary = PERC .. code-block:: none obs_perc_value = 50; - + obs_quality_inc --------------- @@ -2480,7 +2504,7 @@ Note "obs_quality_inc" replaces the older option "obs_quality". obs_quality_inc = [ "1", "2", "3", "9" ]; - + obs_quality_exc --------------- @@ -2495,7 +2519,7 @@ an array of strings, even if the values themselves are numeric. obs_quality_exc = [ "1", "2", "3", "9" ]; - + met_data_dir ------------ @@ -2685,7 +2709,7 @@ entries. This dictionary may include the following entries: censor_val = []; ens_thresh = 1.0; vld_thresh = 1.0; - + field = [ { name = "APCP"; @@ -2746,37 +2770,37 @@ combination of the categorical threshold (cat_thresh), neighborhood width ensemble_flag ^^^^^^^^^^^^^ - + The "ensemble_flag" entry is a dictionary of boolean value indicating which ensemble products should be generated: * "latlon" for a grid of the Latitude and Longitude fields * "mean" for the simple ensemble mean - + * "stdev" for the ensemble standard deviation - + * "minus" for the mean minus one standard deviation - + * "plus" for the mean plus one standard deviation - + * "min" for the ensemble minimum - + * "max" for the ensemble maximum - + * "range" for the range of ensemble values - + * "vld_count" for the number of valid ensemble members - + * "frequency" for the ensemble relative frequency meeting a threshold - + * "nep" for the neighborhood ensemble probability - + * "nmep" for the neighborhood maximum ensemble probability - + * "rank" to write the rank for the gridded observation field to separate NetCDF output file. - + * "weight" to write the grid weights specified in grid_weight_flag to the rank NetCDF output file. @@ -2798,7 +2822,7 @@ which ensemble products should be generated: rank = TRUE; weight = FALSE; } - + EnsembleStatConfig_default -------------------------- @@ -2831,7 +2855,7 @@ data is provided, the climo_cdf thresholds will be used instead. ens_ssvar_bin_size = 1; ens_phist_bin_size = 0.05; prob_cat_thresh = []; - + field = [ { name = "APCP"; @@ -2916,7 +2940,7 @@ CHISQUARED distributions are defined by a single parameter. The GAMMA, UNIFORM, and BETA distributions are defined by two parameters. See the `GNU Scientific Library Reference Manual `_ for more information on these distributions. - + The inst_bias_scale and inst_bias_offset entries specify bias scale and offset values that should be applied to observation values prior to @@ -3211,85 +3235,85 @@ MET User's Guide for a description of these attributes. // centroid_x_min = 0.0; // centroid_x_max = 0.0; - + // centroid_y_min = 0.0; // centroid_y_max = 0.0; - + // centroid_lat_min = 0.0; // centroid_lat_max = 0.0; - + // centroid_lon_min = 0.0; // centroid_lon_max = 0.0; - + // axis_ang_min = 0.0; // axis_ang_max = 0.0; - + // length_min = 0.0; // length_max = 0.0; - + // width_min = 0.0; // width_max = 0.0; - + // aspect_ratio_min = 0.0; // aspect_ratio_max = 0.0; - + // curvature_min = 0.0; // curvature_max = 0.0; - + // curvature_x_min = 0.0; // curvature_x_max = 0.0; - + // curvature_y_min = 0.0; // curvature_y_max = 0.0; - + // complexity_min = 0.0; // complexity_max = 0.0; - + // intensity_10_min = 0.0; // intensity_10_max = 0.0; - + // intensity_25_min = 0.0; // intensity_25_max = 0.0; // intensity_50_min = 0.0; // intensity_50_max = 0.0; - + // intensity_75_min = 0.0; // intensity_75_max = 0.0; - + // intensity_90_min = 0.0; // intensity_90_max = 0.0; - + // intensity_user_min = 0.0; // intensity_user_max = 0.0; - + // intensity_sum_min = 0.0; // intensity_sum_max = 0.0; - + // centroid_dist_min = 0.0; // centroid_dist_max = 0.0; - + // boundary_dist_min = 0.0; // boundary_dist_max = 0.0; - + // convex_hull_dist_min = 0.0; // convex_hull_dist_max = 0.0; - + // angle_diff_min = 0.0; // angle_diff_max = 0.0; - + // area_ratio_min = 0.0; // area_ratio_max = 0.0; - + // intersection_over_area_min = 0.0; // intersection_over_area_max = 0.0; - + // complexity_ratio_min = 0.0; // complexity_ratio_max = 0.0; - + // percentile_intensity_ratio_min = 0.0; // percentile_intensity_ratio_max = 0.0; - + // interest_min = 0.0; // interest_max = 0.0; @@ -3370,14 +3394,14 @@ The object definition settings for MODE are contained within the "fcst" and merge_thresh = [ >=1.0, >=2.0, >=3.0 ]; * The "merge_flag" entry specifies the merging methods to be applied: - + * NONE for no merging - + * THRESH for the double-threshold merging method. Merge objects that would be part of the same object at the lower threshold. - + * ENGINE for the fuzzy logic approach comparing the field to itself - + * BOTH for both the double-threshold and engine merging methods .. code-block:: none @@ -3387,7 +3411,7 @@ The object definition settings for MODE are contained within the "fcst" and name = "APCP"; level = "A03"; } - + censor_thresh = []; censor_val = []; conv_radius = 60.0/grid_res; in grid squares @@ -3418,13 +3442,13 @@ match_flag The "match_flag" entry specifies the matching method to be applied: * NONE for no matching between forecast and observation objects - + * MERGE_BOTH for matching allowing additional merging in both fields. If two objects in one field match the same object in the other field, those two objects are merged. - + * MERGE_FCST for matching allowing only additional forecast merging - + * NO_MERGE for matching with no additional merging in either field .. code-block:: none @@ -3445,7 +3469,7 @@ skip unreasonable object comparisons. weight ^^^^^^ - + The weight variables control how much weight is assigned to each pairwise attribute when computing a total interest value for object pairs. The weights need not sum to any particular value but must be non-negative. When the @@ -3479,23 +3503,23 @@ mathematical functions. .. code-block:: none interest_function = { - + centroid_dist = ( ( 0.0, 1.0 ) ( 60.0/grid_res, 1.0 ) ( 600.0/grid_res, 0.0 ) ); - + boundary_dist = ( ( 0.0, 1.0 ) ( 400.0/grid_res, 0.0 ) ); - + convex_hull_dist = ( ( 0.0, 1.0 ) ( 400.0/grid_res, 0.0 ) ); - + angle_diff = ( ( 0.0, 1.0 ) ( 30.0, 1.0 ) @@ -3508,24 +3532,24 @@ mathematical functions. ( corner, 1.0 ) ( 1.0, 1.0 ) ); - + area_ratio = ratio_if; - + int_area_ratio = ( ( 0.00, 0.00 ) ( 0.10, 0.50 ) ( 0.25, 1.00 ) ( 1.00, 1.00 ) ); - + complexity_ratio = ratio_if; - + inten_perc_ratio = ratio_if; } total_interest_thresh ^^^^^^^^^^^^^^^^^^^^^ - + The total_interest_thresh variable should be set between 0 and 1. This threshold is applied to the total interest values computed for each pair of objects and is used in determining matches. @@ -3574,7 +3598,7 @@ lines in the grid. ct_stats_flag ^^^^^^^^^^^^^ - + The ct_stats_flag can be set to TRUE or FALSE to produce additional output, in the form of contingency table counts and statistics. @@ -3604,16 +3628,16 @@ The PB2NC tool filters out observations from PREPBUFR or BUFR files using the following criteria: (1) by message type: supply a list of PREPBUFR message types to retain - + (2) by station id: supply a list of observation stations to retain - + (3) by valid time: supply the beginning and ending time offset values in the obs_window entry described above. (4) by location: use the "mask" entry described below to supply either an NCEP masking grid, a masking lat/lon polygon or a file to a mask lat/lon polygon - + (5) by elevation: supply min/max elevation values (6) by report type: supply a list of report types to retain using @@ -3621,15 +3645,15 @@ following criteria: (7) by instrument type: supply a list of instrument type to retain - + (8) by vertical level: supply beg/end vertical levels using the level_range entry described below - + (9) by variable type: supply a list of observation variable types to retain using the obs_bufr_var entry described below - + (10) by quality mark: supply a quality mark threshold - + (11) Flag to retain values for all quality marks, or just the first quality mark (highest): use the event_stack_flag described below @@ -3637,24 +3661,24 @@ following criteria: retain. 0 - Surface level (mass reports only) - + 1 - Mandatory level (upper-air profile reports) - + 2 - Significant temperature level (upper-air profile reports) - + 2 - Significant temperature and winds-by-pressure level (future combined mass and wind upper-air reports) - + 3 - Winds-by-pressure level (upper-air profile reports) - + 4 - Winds-by-height level (upper-air profile reports) - + 5 - Tropopause level (upper-air profile reports) - + 6 - Reports on a single level (e.g., aircraft, satellite-wind, surface wind, precipitable water retrievals, etc.) - + 7 - Auxiliary levels generated via interpolation from spanning levels (upper-air profile reports) @@ -3665,14 +3689,14 @@ In the PB2NC tool, the "message_type" entry is an array of message types to be retained. An empty list indicates that all should be retained. | List of valid message types: -| “ADPUPA”, “AIRCAR”, “AIRCFT”, “ADPSFC”, “ERS1DA”, “GOESND”, “GPSIPW”, -| “MSONET”, “PROFLR”, “QKSWND”, “RASSDA”, “SATEMP”, +| “ADPUPA”, “AIRCAR”, “AIRCFT”, “ADPSFC”, “ERS1DA”, “GOESND”, “GPSIPW”, +| “MSONET”, “PROFLR”, “QKSWND”, “RASSDA”, “SATEMP”, | “SATWND”, “SFCBOG”, “SFCSHP”, “SPSSMI”, “SYNDAT”, “VADWND” For example: | message_type[] = [ "ADPUPA", "AIRCAR" ]; -| +| `Current Table A Entries in PREPBUFR mnemonic table `_ @@ -3782,12 +3806,12 @@ categories should be retained: | 1 = Mandatory level (upper-air profile reports) -| 2 = Significant temperature level (upper-air profile reports) +| 2 = Significant temperature level (upper-air profile reports) | 2 = Significant temperature and winds-by-pressure level (future combined mass -| and wind upper-air reports) +| and wind upper-air reports) -| 3 = Winds-by-pressure level (upper-air profile reports) +| 3 = Winds-by-pressure level (upper-air profile reports) | 4 = Winds-by-height level (upper-air profile reports) @@ -3799,7 +3823,7 @@ categories should be retained: | 7 = Auxiliary levels generated via interpolation from spanning levels | (upper-air profile reports) -| +| An empty list indicates that all should be retained. @@ -3870,7 +3894,7 @@ abbreviations to the output. quality_mark_thresh ^^^^^^^^^^^^^^^^^^^ - + The "quality_mark_thresh" entry specifies the maximum quality mark value to be retained. Observations with a quality mark LESS THAN OR EQUAL TO this threshold will be retained, while observations with a quality mark @@ -3959,12 +3983,12 @@ job to be performed. The format for an analysis job is as follows: | -job job_name | OPTIONAL ARGS -| +| Where "job_name" is set to one of the following: * "filter" - + To filter out the STAT lines matching the job filtering criteria specified below and using the optional arguments below. The output STAT lines are written to the file specified using the @@ -3983,7 +4007,7 @@ Where "job_name" is set to one of the following: | * "summary" - + To compute summary information for a set of statistics. The summary output includes the mean, standard deviation, percentiles (0th, 10th, 25th, 50th, 75th, 90th, and 100th), range, @@ -3993,10 +4017,10 @@ Where "job_name" is set to one of the following: logic: * simple arithmetic mean (default) - + * square root of the mean of the statistic squared (applied to columns listed in "wmo_sqrt_stats") - + * apply fisher transform (applied to columns listed in "wmo_fisher_stats") @@ -4004,9 +4028,9 @@ Where "job_name" is set to one of the following: The columns of data to be summarized are specified in one of two ways: - + * Specify the -line_type option once and specify one or more column names. - + * Format the -column option as LINE_TYPE:COLUMN. | @@ -4020,7 +4044,7 @@ Where "job_name" is set to one of the following: processing them separately. For TCStat, the "-column" argument may be set to: - + * "TRACK" for track, along-track, and cross-track errors. * "WIND" for all wind radius errors. * "TI" for track and maximum wind intensity errors. @@ -4046,7 +4070,7 @@ Where "job_name" is set to one of the following: To summarize multiple columns. * "aggregate" - + To aggregate the STAT data for the STAT line type specified using the "-line_type" argument. The output of the job will be in the same format as the input line type specified. The following line @@ -4058,13 +4082,13 @@ Where "job_name" is set to one of the following: SL1L2, SAL1L2, VL1L2, VAL1L2, PCT, NBRCNT, NBRCTC, GRAD, ISC, ECNT, RPS, RHIST, PHIST, RELP, SSVAR - + Required Args: -line_type | * "aggregate_stat" - + To aggregate the STAT data for the STAT line type specified using the "-line_type" argument. The output of the job will be the line type specified using the "-out_line_type" argument. The valid @@ -4156,11 +4180,11 @@ Where "job_name" is set to one of the following: Optionally, specify other filters for each term, -fcst_thresh. * "go_index" - + The GO Index is a special case of the skill score index consisting of a predefined set of variables, levels, lead times, statistics, and weights. - + For lead times of 12, 24, 36, and 48 hours, it contains RMSE for: .. code-block:: none @@ -4178,7 +4202,7 @@ Where "job_name" is set to one of the following: | * "ramp" - + The ramp job operates on a time-series of forecast and observed values and is analogous to the RIRW (Rapid Intensification and Weakening) job supported by the tc_stat tool. The amount of change @@ -4486,17 +4510,17 @@ wavelet decomposition should be performed: See: `Discrete Wavelet Transforms (DWT) initialization `_ * Valid combinations of the two are listed below: - + * HAAR for Haar wavelet (member = 2) - + * HAAR_CNTR for Centered-Haar wavelet (member = 2) * DAUB for Daubechies wavelet (member = 4, 6, 8, 10, 12, 14, 16, 18, 20) - + * DAUB_CNTR for Centered-Daubechies wavelet (member = 4, 6, 8, 10, 12, 14, 16, 18, 20) - + * BSPLINE for Bspline wavelet (member = 103, 105, 202, 204, 206, 208, 301, 303, 305, 307, 309) diff --git a/docs/Users_Guide/ensemble-stat.rst b/docs/Users_Guide/ensemble-stat.rst index 73e0b799be..8a2502c525 100644 --- a/docs/Users_Guide/ensemble-stat.rst +++ b/docs/Users_Guide/ensemble-stat.rst @@ -160,29 +160,30 @@ ____________________ .. code-block:: none - model = "FCST"; - desc = "NA"; - obtype = "ANALYS"; - regrid = { ... } - climo_mean = { ... } - climo_stdev = { ... } - climo_cdf = { ... } - obs_window = { beg = -5400; end = 5400; } - mask = { grid = [ "FULL" ]; poly = []; sid = []; } - ci_alpha = [ 0.05 ]; - interp = { field = BOTH; vld_thresh = 1.0; shape = SQUARE; - type = [ { method = NEAREST; width = 1; } ]; } - eclv_points = []; - sid_inc = []; - sid_exc = []; - duplicate_flag = NONE; + model = "FCST"; + desc = "NA"; + obtype = "ANALYS"; + regrid = { ... } + climo_mean = { ... } + climo_stdev = { ... } + climo_cdf = { ... } + obs_window = { beg = -5400; end = 5400; } + mask = { grid = [ "FULL" ]; poly = []; sid = []; } + ci_alpha = [ 0.05 ]; + interp = { field = BOTH; vld_thresh = 1.0; shape = SQUARE; + type = [ { method = NEAREST; width = 1; } ]; } + eclv_points = []; + sid_inc = []; + sid_exc = []; + duplicate_flag = NONE; obs_quality_inc = []; obs_quality_exc = []; - obs_summary = NONE; - obs_perc_value = 50; + obs_summary = NONE; + obs_perc_value = 50; message_type_group_map = [...]; - output_prefix = ""; - version = "VN.N"; + grid_weight_flag = NONE; + output_prefix = ""; + version = "VN.N"; The configuration options listed above are common to many MET tools and are described in :numref:`config_options`. diff --git a/docs/Users_Guide/grid-stat.rst b/docs/Users_Guide/grid-stat.rst index b10b1b3431..631afbdaf2 100644 --- a/docs/Users_Guide/grid-stat.rst +++ b/docs/Users_Guide/grid-stat.rst @@ -241,31 +241,32 @@ __________________________ .. code-block:: none - model = "FCST"; - desc = "NA"; - obtype = "ANALYS"; - fcst = { ... } - obs = { ... } - regrid = { ... } - climo_mean = { ... } - climo_stdev = { ... } - climo_cdf = { ... } - mask = { grid = [ "FULL" ]; poly = []; } - ci_alpha = [ 0.05 ]; - boot = { interval = PCTILE; rep_prop = 1.0; n_rep = 1000; - rng = "mt19937"; seed = ""; } - interp = { field = BOTH; vld_thresh = 1.0; shape = SQUARE; - type = [ { method = NEAREST; width = 1; } ]; } - censor_thresh = []; - censor_val = []; - mpr_column = []; - mpr_thresh = []; - eclv_points = 0.05; - hss_ec_value = NA; - rank_corr_flag = TRUE; - tmp_dir = "/tmp"; - output_prefix = ""; - version = "VN.N"; + model = "FCST"; + desc = "NA"; + obtype = "ANALYS"; + fcst = { ... } + obs = { ... } + regrid = { ... } + climo_mean = { ... } + climo_stdev = { ... } + climo_cdf = { ... } + mask = { grid = [ "FULL" ]; poly = []; } + ci_alpha = [ 0.05 ]; + boot = { interval = PCTILE; rep_prop = 1.0; n_rep = 1000; + rng = "mt19937"; seed = ""; } + interp = { field = BOTH; vld_thresh = 1.0; shape = SQUARE; + type = [ { method = NEAREST; width = 1; } ]; } + censor_thresh = []; + censor_val = []; + mpr_column = []; + mpr_thresh = []; + eclv_points = 0.05; + hss_ec_value = NA; + rank_corr_flag = TRUE; + grid_weight_flag = NONE; + tmp_dir = "/tmp"; + output_prefix = ""; + version = "VN.N"; The configuration options listed above are common to multiple MET tools and are described in :numref:`config_options`. diff --git a/internal/test_unit/config/EnsembleStatConfig_grid_weight b/internal/test_unit/config/EnsembleStatConfig_grid_weight index 9915c3fa37..12994a3a5b 100644 --- a/internal/test_unit/config/EnsembleStatConfig_grid_weight +++ b/internal/test_unit/config/EnsembleStatConfig_grid_weight @@ -15,7 +15,7 @@ model = "FCST"; // Output description to be written // May be set separately in each "obs.field" entry // -desc = "NA"; +desc = "${DESC}"; // // Output observation type to be written @@ -62,7 +62,7 @@ prob_pct_thresh = [ ==0.25 ]; nc_var_str = ""; eclv_points = 0.05; -tmp_field = [ { name = "TMP"; level = [ "Z2" ]; } ]; +tmp_field = [ { name = "TMP"; level = [ "Z2" ]; prob_cat_thresh = [ <=273, >273 ]; } ]; // // Forecast and observation fields to be verified @@ -139,6 +139,11 @@ climo_mean = { hour_interval = 6; } +climo_stdev = climo_mean; +climo_stdev = { + file_name = [ "${CLIMO_STDEV_FILE}" ]; +} + //////////////////////////////////////////////////////////////////////////////// // @@ -200,11 +205,11 @@ output_flag = { orank = NONE; ssvar = STAT; relp = STAT; - pct = NONE; - pstd = NONE; - pjc = NONE; - prc = NONE; - eclv = NONE; + pct = STAT; + pstd = STAT; + pjc = STAT; + prc = STAT; + eclv = STAT; } //////////////////////////////////////////////////////////////////////////////// diff --git a/internal/test_unit/config/GridStatConfig_grid_weight b/internal/test_unit/config/GridStatConfig_grid_weight index 1efce2f152..27b266dfa8 100644 --- a/internal/test_unit/config/GridStatConfig_grid_weight +++ b/internal/test_unit/config/GridStatConfig_grid_weight @@ -15,7 +15,7 @@ model = "GFS"; // Output description to be written // May be set separately in each "obs.field" entry // -desc = "NA"; +desc = "${DESC}"; // // Output observation type to be written @@ -54,7 +54,7 @@ nc_pairs_var_suffix = ""; hss_ec_value = NA; rank_corr_flag = FALSE; -tmp_field = [ { name = "TMP"; level = [ "P500" ]; } ]; +tmp_field = [ { name = "TMP"; level = [ "P500" ]; cat_thresh = [ >245, >255 ]; } ]; // // Forecast and observation fields to be verified @@ -179,11 +179,11 @@ distance_map = { // Statistical output types // output_flag = { - fho = NONE; - ctc = NONE; - cts = NONE; - mctc = NONE; - mcts = NONE; + fho = NONE; + ctc = STAT; + cts = STAT; + mctc = STAT; + mcts = STAT; cnt = STAT; sl1l2 = STAT; sal1l2 = STAT; diff --git a/internal/test_unit/t b/internal/test_unit/t new file mode 100755 index 0000000000..8df021c329 --- /dev/null +++ b/internal/test_unit/t @@ -0,0 +1,114 @@ +export 'CLIMO_MEAN_FILE=${MET_TEST_INPUT}/climatology_data/NCEP_1.0deg/cmean_1d.19790410' +export 'DESC=NO_WEIGHT' +export 'GRID_WEIGHT=NONE' +export 'OUTPUT_PREFIX=NO_WEIGHT' +/d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../share/met/../../bin/grid_stat \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib2/gfs/gfs_2012040900_F024.grib2 \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib2/gfsanl/gfsanl_4_20120410_0000_000.grb2 \ + /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/config/GridStatConfig_grid_weight \ + -outdir /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../test_output/grid_weight -v 1 +unset CLIMO_MEAN_FILE +unset DESC +unset GRID_WEIGHT +unset OUTPUT_PREFIX + + +export 'CLIMO_MEAN_FILE=${MET_TEST_INPUT}/climatology_data/NCEP_1.0deg/cmean_1d.19790410' +export 'DESC=COS_LAT_WEIGHT' +export 'GRID_WEIGHT=COS_LAT' +export 'OUTPUT_PREFIX=COS_LAT_WEIGHT' +/d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../share/met/../../bin/grid_stat \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib2/gfs/gfs_2012040900_F024.grib2 \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib2/gfsanl/gfsanl_4_20120410_0000_000.grb2 \ + /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/config/GridStatConfig_grid_weight \ + -outdir /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../test_output/grid_weight -v 1 +unset CLIMO_MEAN_FILE +unset DESC +unset GRID_WEIGHT +unset OUTPUT_PREFIX + + +export 'CLIMO_MEAN_FILE=${MET_TEST_INPUT}/climatology_data/NCEP_1.0deg/cmean_1d.19790410' +export 'DESC=AREA_WEIGHT' +export 'GRID_WEIGHT=AREA' +export 'OUTPUT_PREFIX=AREA_WEIGHT' +/d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../share/met/../../bin/grid_stat \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib2/gfs/gfs_2012040900_F024.grib2 \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib2/gfsanl/gfsanl_4_20120410_0000_000.grb2 \ + /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/config/GridStatConfig_grid_weight \ + -outdir /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../test_output/grid_weight -v 1 +unset CLIMO_MEAN_FILE +unset DESC +unset GRID_WEIGHT +unset OUTPUT_PREFIX + + +export 'CLIMO_MEAN_FILE=${MET_TEST_INPUT}/climatology_data/NCEP_1.0deg/cmean_1d.19790410' +export 'CLIMO_STDEV_FILE=${MET_TEST_INPUT}/climatology_data/NCEP_1.0deg/cstdv_1d.19790410' +export 'DESC=NO_WEIGHT' +export 'GRID_WEIGHT=NONE' +export 'OUTPUT_PREFIX=NO_WEIGHT' +/d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../share/met/../../bin/ensemble_stat \ + 6 \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-fer-gep1/arw-fer-gep1_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-fer-gep5/arw-fer-gep5_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-sch-gep2/arw-sch-gep2_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-sch-gep6/arw-sch-gep6_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-tom-gep3/arw-tom-gep3_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-tom-gep7/arw-tom-gep7_2012040912_F024.grib \ + /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/config/EnsembleStatConfig_grid_weight \ + -grid_obs /d1/projects/MET/MET_test_data/unit_test/obs_data/laps/laps_2012041012_F000.grib \ + -outdir /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../test_output/grid_weight -v 1 +unset CLIMO_MEAN_FILE +unset CLIMO_STDEV_FILE +unset DESC +unset GRID_WEIGHT +unset OUTPUT_PREFIX + + +export 'CLIMO_MEAN_FILE=${MET_TEST_INPUT}/climatology_data/NCEP_1.0deg/cmean_1d.19790410' +export 'CLIMO_STDEV_FILE=${MET_TEST_INPUT}/climatology_data/NCEP_1.0deg/cstdv_1d.19790410' +export 'DESC=COS_LAT_WEIGHT' +export 'GRID_WEIGHT=COS_LAT' +export 'OUTPUT_PREFIX=COS_LAT_WEIGHT' +/d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../share/met/../../bin/ensemble_stat \ + 6 \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-fer-gep1/arw-fer-gep1_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-fer-gep5/arw-fer-gep5_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-sch-gep2/arw-sch-gep2_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-sch-gep6/arw-sch-gep6_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-tom-gep3/arw-tom-gep3_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-tom-gep7/arw-tom-gep7_2012040912_F024.grib \ + /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/config/EnsembleStatConfig_grid_weight \ + -grid_obs /d1/projects/MET/MET_test_data/unit_test/obs_data/laps/laps_2012041012_F000.grib \ + -outdir /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../test_output/grid_weight -v 1 +unset CLIMO_MEAN_FILE +unset CLIMO_STDEV_FILE +unset DESC +unset GRID_WEIGHT +unset OUTPUT_PREFIX + + +export 'CLIMO_MEAN_FILE=${MET_TEST_INPUT}/climatology_data/NCEP_1.0deg/cmean_1d.19790410' +export 'CLIMO_STDEV_FILE=${MET_TEST_INPUT}/climatology_data/NCEP_1.0deg/cstdv_1d.19790410' +export 'DESC=AREA_WEIGHT' +export 'GRID_WEIGHT=AREA' +export 'OUTPUT_PREFIX=AREA_WEIGHT' +/d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../share/met/../../bin/ensemble_stat \ + 6 \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-fer-gep1/arw-fer-gep1_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-fer-gep5/arw-fer-gep5_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-sch-gep2/arw-sch-gep2_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-sch-gep6/arw-sch-gep6_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-tom-gep3/arw-tom-gep3_2012040912_F024.grib \ + /d1/projects/MET/MET_test_data/unit_test/model_data/grib1/arw-tom-gep7/arw-tom-gep7_2012040912_F024.grib \ + /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/config/EnsembleStatConfig_grid_weight \ + -grid_obs /d1/projects/MET/MET_test_data/unit_test/obs_data/laps/laps_2012041012_F000.grib \ + -outdir /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../test_output/grid_weight -v 1 +unset CLIMO_MEAN_FILE +unset CLIMO_STDEV_FILE +unset DESC +unset GRID_WEIGHT +unset OUTPUT_PREFIX + + diff --git a/internal/test_unit/unit_test.log b/internal/test_unit/unit_test.log new file mode 100644 index 0000000000..ef1c7b19b5 --- /dev/null +++ b/internal/test_unit/unit_test.log @@ -0,0 +1,1225 @@ +export MET_BASE=/d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../share/met +export MET_BUILD_BASE=/d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../.. +export MET_TEST_BASE=/d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit +export MET_TEST_INPUT=/d1/projects/MET/MET_test_data/unit_test +export MET_TEST_OUTPUT=/d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../test_output + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_ascii2nc.xml + +TEST: ascii2nc_TRMM_3hr + - pass - 17.767 sec +TEST: ascii2nc_GAGE_24hr + - pass - 1.165 sec +TEST: ascii2nc_GAGE_24hr_badfile + - pass - 0.53 sec +TEST: ascii2nc_duplicates + - pass - 0.532 sec +TEST: ascii2nc_SURFRAD1 + - pass - 0.975 sec +TEST: ascii2nc_insitu_turb + - pass - 3.119 sec +TEST: ascii2nc_by_var_name_PB + - pass - 146.208 sec +TEST: ascii2nc_rain_01H_sum + - pass - 0.582 sec +TEST: ascii2nc_airnow_daily_v2 + - pass - 0.799 sec +TEST: ascii2nc_airnow_hourly_aqobs + - pass - 0.886 sec +TEST: ascii2nc_airnow_hourly + - pass - 3.847 sec +TEST: ascii2nc_ndbc + - pass - 8.243 sec +TEST: ascii2nc_ismn_SNOTEL + - pass - 14.834 sec +TEST: ascii2nc_iabp + - pass - 0.542 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_ascii2nc_indy.xml + +TEST: ascii2nc_TRMM_12hr + - pass - 20.969 sec +TEST: ascii2nc_LITTLE_R + - pass - 0.605 sec +TEST: ascii2nc_LITTLE_R_BAD_RECORD + - pass - 0.551 sec +TEST: ascii2nc_SURFRAD + - pass - 0.976 sec +TEST: ascii2nc_SURFRAD_summary1 + - pass - 2.007 sec +TEST: ascii2nc_SURFRAD_summary2 + - pass - 1.483 sec +TEST: ascii2nc_SURFRAD_summary3 + - pass - 1.224 sec +TEST: ascii2nc_SURFRAD_summary4 + - pass - 1.205 sec +TEST: ascii2nc_insitu_turb_mask_sid + - pass - 1.241 sec +TEST: ascii2nc_insitu_turb_mask_grid_data + - pass - 2.994 sec +TEST: ascii2nc_insitu_turb_mask_named_grid + - pass - 2.963 sec +TEST: ascii2nc_MASK_GRID + - pass - 3.539 sec +TEST: ascii2nc_MASK_POLY + - pass - 1.298 sec +TEST: ascii2nc_WWSIS_clear_pvwatts_one_min + - pass - 18.383 sec +TEST: ascii2nc_WWSIS_clear_pvwatts_five_min + - pass - 2.641 sec +TEST: ascii2nc_WWSIS_clear_pvwatts_ten_min + - pass - 1.509 sec +TEST: ascii2nc_WWSIS_clear_pvwatts_sixty_min + - pass - 0.694 sec +TEST: ascii2nc_WWSIS_HA_pvwatts_sixty_min + - pass - 0.735 sec +TEST: ascii2nc_WWSIS_pvwatts_one_min + - pass - 18.646 sec +TEST: ascii2nc_WWSIS_pvwatts_sixty_min + - pass - 0.7 sec +TEST: ascii2nc_by_var_name + - pass - 0.534 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_madis2nc.xml + +TEST: madis2nc_METAR + - pass - 12.385 sec +TEST: madis2nc_METAR_time_summary + - pass - 16.391 sec +TEST: madis2nc_METAR_mask_sid + - pass - 0.608 sec +TEST: madis2nc_METAR_mask_grid + - pass - 1.052 sec +TEST: madis2nc_RAOB + - pass - 3.295 sec +TEST: madis2nc_PROFILER_MASK_POLY + - pass - 0.57 sec +TEST: madis2nc_MARITIME + - pass - 0.811 sec +TEST: madis2nc_MESONET_MASK_GRID + - pass - 6.653 sec +TEST: madis2nc_MESONET_optional_vars + - pass - 4.856 sec +TEST: madis2nc_ACARS_PROFILES + - pass - 2.095 sec +TEST: madis2nc_buf_handle + - pass - 2.626 sec +TEST: madis2nc_multiple_inputs + - pass - 2.167 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_trmm2nc.xml + +TEST: trmm2nc_3hr + - pass - 0.334 sec +TEST: trmm2nc_12hr + - pass - 0.331 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_pb2nc.xml + +TEST: pb2nc_GDAS_mask_grid_G212 + - pass - 8.247 sec +TEST: pb2nc_NDAS_no_mask + - pass - 9.265 sec +TEST: pb2nc_NDAS_mask_poly_conus + - pass - 3.676 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_pb2nc_indy.xml + +TEST: pb2nc_NDAS_mask_sid_list + - pass - 1.602 sec +TEST: pb2nc_NDAS_mask_sid_file + - pass - 1.765 sec +TEST: pb2nc_NDAS_mask_grid_data_cfg + - pass - 4.469 sec +TEST: pb2nc_compute_pbl_cape + - pass - 13.715 sec +TEST: pb2nc_NDAS_var_all + - pass - 19.439 sec +TEST: pb2nc_vertical_level_500 + - pass - 1.392 sec +TEST: pb2nc_NDAS_summary + - pass - 6.866 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_gen_vx_mask.xml + +TEST: gen_vx_mask_POLY_GFS_LATLON + - pass - 11.973 sec +TEST: gen_vx_mask_POLY_GFS_MERCATOR + - pass - 0.607 sec +TEST: gen_vx_mask_POLY_NAM_LAMBERT + - pass - 4.092 sec +TEST: gen_vx_mask_POLY_HMT_STEREO + - pass - 1.026 sec +TEST: gen_vx_mask_POLY_GFS_LATLON_NAK + - pass - 1.476 sec +TEST: gen_vx_mask_POLY_LATLON_RECTANGLE + - pass - 0.552 sec +TEST: gen_vx_mask_POLY_XY_RECTANGLE + - pass - 0.544 sec +TEST: gen_vx_mask_GRID_NAM_HMT_STEREO + - pass - 0.732 sec +TEST: gen_vx_mask_GRID_NAMED_GRIDS + - pass - 0.618 sec +TEST: gen_vx_mask_GRID_SPEC_STRINGS + - pass - 0.598 sec +TEST: gen_vx_mask_CIRCLE + - pass - 0.685 sec +TEST: gen_vx_mask_CIRCLE_MASK + - pass - 0.642 sec +TEST: gen_vx_mask_CIRCLE_COMPLEMENT + - pass - 0.649 sec +TEST: gen_vx_mask_TRACK + - pass - 1.096 sec +TEST: gen_vx_mask_TRACK_MASK + - pass - 1.098 sec +TEST: gen_vx_mask_DATA_APCP_24 + - pass - 1.067 sec +TEST: gen_vx_mask_POLY_PASS_THRU + - pass - 0.633 sec +TEST: gen_vx_mask_POLY_INTERSECTION + - pass - 0.633 sec +TEST: gen_vx_mask_POLY_UNION + - pass - 0.634 sec +TEST: gen_vx_mask_POLY_SYMDIFF + - pass - 0.627 sec +TEST: gen_vx_mask_DATA_INPUT_FIELD + - pass - 1.211 sec +TEST: gen_vx_mask_BOX + - pass - 0.536 sec +TEST: gen_vx_mask_SOLAR_ALT + - pass - 0.557 sec +TEST: gen_vx_mask_SOLAR_AZI + - pass - 0.673 sec +TEST: gen_vx_mask_LAT + - pass - 0.543 sec +TEST: gen_vx_mask_LON + - pass - 0.548 sec +TEST: gen_vx_mask_SHAPE + - pass - 0.549 sec +TEST: gen_vx_mask_SHAPE_STR + - pass - 0.695 sec +TEST: gen_vx_mask_SHAPE_STR_MULTI + - pass - 0.605 sec +TEST: gen_vx_mask_PYTHON + - pass - 1.605 sec +TEST: gen_vx_mask_DATA_TWO_FILE_TYPES + - pass - 1.22 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_gen_ens_prod.xml + +TEST: gen_ens_prod_NO_CTRL + - pass - 9.428 sec +TEST: gen_ens_prod_WITH_CTRL + - pass - 8.858 sec +TEST: gen_ens_prod_SINGLE_FILE_NC_NO_CTRL + - pass - 1.23 sec +TEST: gen_ens_prod_SINGLE_FILE_NC_WITH_CTRL + - pass - 1.144 sec +TEST: gen_ens_prod_SINGLE_FILE_GRIB_NO_CTRL + - pass - 1.206 sec +TEST: gen_ens_prod_SINGLE_FILE_GRIB_WITH_CTRL + - pass - 1.213 sec +TEST: gen_ens_prod_NORMALIZE + - pass - 6.058 sec +TEST: gen_ens_prod_CLIMO_ANOM_ENS_MEMBER_ID + - pass - 0.88 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_pcp_combine.xml + +TEST: pcp_combine_sum_GRIB1 + - pass - 28.664 sec +TEST: pcp_combine_sum_GRIB1_MISSING + - pass - 14.362 sec +TEST: pcp_combine_sum_GRIB1_MULTIPLE_FIELDS + - pass - 42.993 sec +TEST: pcp_combine_sum_GRIB2 + - pass - 1.445 sec +TEST: pcp_combine_add_GRIB1 + - pass - 1.686 sec +TEST: pcp_combine_add_GRIB2 + - pass - 0.601 sec +TEST: pcp_combine_add_STAGEIV + - pass - 1.207 sec +TEST: pcp_combine_add_ACCUMS + - pass - 1.414 sec +TEST: pcp_combine_sub_GRIB1 + - pass - 1.089 sec +TEST: pcp_combine_sub_GRIB1_run2 + - pass - 0.675 sec +TEST: pcp_combine_sub_GRIB2 + - pass - 0.553 sec +TEST: pcp_combine_sub_NC_MET_06 + - pass - 0.631 sec +TEST: pcp_combine_sub_P_INTERP + - pass - 0.811 sec +TEST: pcp_combine_add_VARNAME + - pass - 0.891 sec +TEST: pcp_combine_sub_DIFFERENT_INIT + - pass - 0.669 sec +TEST: pcp_combine_sub_NEGATIVE_ACCUM + - pass - 0.694 sec +TEST: pcp_combine_sub_SUBTRACT_MULTIPLE_FIELDS + - pass - 1.045 sec +TEST: pcp_combine_derive_LIST_OF_FILES + - pass - 1.104 sec +TEST: pcp_combine_derive_MULTIPLE_FIELDS + - pass - 3.137 sec +TEST: pcp_combine_derive_VLD_THRESH + - pass - 1.208 sec +TEST: pcp_combine_derive_CUSTOM_NAMES + - pass - 0.747 sec +TEST: pcp_combine_sub_ROT_LL + - pass - 1.096 sec +TEST: pcp_combine_LAEA_GRIB2 + - pass - 1.428 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_wwmca_regrid.xml + +TEST: wwmca_regrid_G003_NO_AGE + - pass - 2.472 sec +TEST: wwmca_regrid_G003_AGE_60 + - pass - 1.532 sec +TEST: wwmca_regrid_G003_AGE_120 + - pass - 1.531 sec +TEST: wwmca_regrid_G003_AGE_240 + - pass - 1.575 sec +TEST: wwmca_regrid_G003_WRITE_PIXEL_AGE + - pass - 1.547 sec +TEST: wwmca_regrid_GFS_LATLON + - pass - 4.694 sec +TEST: wwmca_regrid_GFS_MERCATOR + - pass - 0.699 sec +TEST: wwmca_regrid_NAM_LAMBERT + - pass - 5.288 sec +TEST: wwmca_regrid_HMT_STEREO + - pass - 0.963 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_point_stat.xml + +TEST: point_stat_GRIB1_NAM_GDAS + - pass - 49.029 sec +TEST: point_stat_GRIB1_NAM_GDAS_WINDS + - pass - 11.773 sec +TEST: point_stat_GRIB1_NAM_GDAS_MASK_SID + - pass - 43.827 sec +TEST: point_stat_GRIB2_NAM_NDAS + - pass - 36.403 sec +TEST: point_stat_GRIB2_SREF_GDAS + - pass - 28.574 sec +TEST: point_stat_GRIB1_NAM_TRMM + - pass - 15.594 sec +TEST: point_stat_GRIB2_SREF_TRMM + - pass - 15.265 sec +TEST: point_stat_NCMET_NAM_HMTGAGE + - pass - 1.756 sec +TEST: point_stat_NCMET_NAM_NDAS_SEEPS + - pass - 9.972 sec +TEST: point_stat_NCPINT_TRMM + - pass - 15.005 sec +TEST: point_stat_NCPINT_NDAS + - pass - 7.636 sec +TEST: point_stat_GRIB2_SREF_TRMM_prob + - pass - 2.426 sec +TEST: point_stat_GTG_lc + - pass - 60.144 sec +TEST: point_stat_GTG_latlon + - pass - 43.921 sec +TEST: point_stat_SID_INC_EXC + - pass - 6.573 sec +TEST: point_stat_SID_INC_EXC_CENSOR + - pass - 7.393 sec +TEST: point_stat_GRIB1_NAM_GDAS_INTERP_OPTS + - pass - 5.202 sec +TEST: point_stat_GRIB1_NAM_GDAS_INTERP_OPTS_name + - pass - 20.228 sec +TEST: point_stat_LAND_TOPO_MASK + - pass - 36.319 sec +TEST: point_stat_MPR_THRESH + - pass - 57.575 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_stat_analysis_ps.xml + +TEST: stat_analysis_CONFIG_POINT_STAT + - pass - 44.426 sec +TEST: stat_analysis_POINT_STAT_SUMMARY + - pass - 34.47 sec +TEST: stat_analysis_POINT_STAT_SUMMARY_UNION + - pass - 19.293 sec +TEST: stat_analysis_POINT_STAT_FILTER_OBS_SID + - pass - 1.695 sec +TEST: stat_analysis_POINT_STAT_FILTER_TIMES + - pass - 8.536 sec +TEST: stat_analysis_POINT_STAT_SEEPS + - pass - 3.168 sec +TEST: stat_analysis_RAMPS + - pass - 3.089 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_duplicate_flag.xml + +TEST: point_stat_DUP_NONE + - pass - 0.694 sec +TEST: point_stat_DUP_UNIQUE + - pass - 0.679 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_obs_summary.xml + +TEST: ascii2nc_obs_summary + - pass - 0.571 sec +TEST: point_stat_OS_NONE + - pass - 0.684 sec +TEST: point_stat_OS_NEAREST + - pass - 0.728 sec +TEST: point_stat_OS_MIN + - pass - 0.676 sec +TEST: point_stat_OS_MAX + - pass - 0.686 sec +TEST: point_stat_OS_UW_MEAN + - pass - 0.681 sec +TEST: point_stat_OS_DW_MEAN + - pass - 0.676 sec +TEST: point_stat_OS_MEDIAN + - pass - 0.677 sec +TEST: point_stat_OS_PERC + - pass - 0.682 sec +TEST: point_stat_OS_UNIQUE_ALL + - pass - 1.406 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_grid_stat.xml + +TEST: grid_stat_GRIB_lvl_typ_val + - pass - 102.432 sec +TEST: grid_stat_GRIB_set_attr + - pass - 26.404 sec +TEST: grid_stat_GRIB2_NAM_RTMA + - pass - 21.167 sec +TEST: grid_stat_GRIB2_NAM_RTMA_NP2 + - pass - 19.316 sec +TEST: grid_stat_GRIB1_NAM_STAGE4 + - pass - 35.211 sec +TEST: grid_stat_GRIB1_NAM_STAGE4_CENSOR + - pass - 3.779 sec +TEST: grid_stat_GTG_lc + - pass - 2.261 sec +TEST: grid_stat_GTG_latlon + - pass - 3.405 sec +TEST: grid_stat_GRIB2_SREF_STAGE4_prob_as_scalar + - pass - 2.357 sec +TEST: grid_stat_APPLY_MASK_TRUE + - pass - 5.515 sec +TEST: grid_stat_APPLY_MASK_FALSE + - pass - 5.392 sec +TEST: grid_stat_GFS_FOURIER + - pass - 8.642 sec +TEST: grid_stat_MPR_THRESH + - pass - 59.558 sec +TEST: grid_stat_UK_SEEPS + - pass - 4.344 sec +TEST: grid_stat_WRF_pres + - pass - 1.113 sec +TEST: grid_stat_GEN_ENS_PROD + - pass - 3.09 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_stat_analysis_gs.xml + +TEST: stat_analysis_CONFIG_GRID_STAT + - pass - 0.878 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_wavelet_stat.xml + +TEST: wavelet_stat_GRIB1_NAM_STAGE4 + - pass - 27.062 sec +TEST: wavelet_stat_GRIB1_NAM_STAGE4_NO_THRESH + - pass - 15.383 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_stat_analysis_ws.xml + +TEST: stat_analysis_AGG_ISC + - pass - 0.64 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_ensemble_stat.xml + +TEST: ensemble_stat_CMD_LINE + - pass - 5.766 sec +TEST: ensemble_stat_FILE_LIST + - pass - 5.425 sec +TEST: ensemble_stat_MASK_SID + - pass - 1.392 sec +TEST: ensemble_stat_MASK_SID_CTRL + - pass - 1.345 sec +TEST: ensemble_stat_MASK_SID_CENSOR + - pass - 1.732 sec +TEST: ensemble_stat_SKIP_CONST + - pass - 5.18 sec +TEST: ensemble_stat_OBSERR + - pass - 13.099 sec +TEST: ensemble_stat_SINGLE_FILE_NC_NO_CTRL + - pass - 3.044 sec +TEST: ensemble_stat_SINGLE_FILE_NC_WITH_CTRL + - pass - 3.158 sec +TEST: ensemble_stat_SINGLE_FILE_GRIB_NO_CTRL + - pass - 2.072 sec +TEST: ensemble_stat_SINGLE_FILE_GRIB_WITH_CTRL + - pass - 2.109 sec +TEST: ensemble_stat_RPS_CLIMO_BIN_PROB + - pass - 0.581 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_stat_analysis_es.xml + +TEST: stat_analysis_AGG_RHIST + - pass - 0.843 sec +TEST: stat_analysis_AGG_PHIST + - pass - 0.843 sec +TEST: stat_analysis_AGG_RELP + - pass - 1.007 sec +TEST: stat_analysis_AGG_ECNT + - pass - 1.031 sec +TEST: stat_analysis_AGG_STAT_ORANK_RHIST_PHIST + - pass - 5.348 sec +TEST: stat_analysis_AGG_STAT_ORANK_RELP + - pass - 4.691 sec +TEST: stat_analysis_AGG_STAT_ORANK_SSVAR + - pass - 5.3 sec +TEST: stat_analysis_AGG_STAT_ORANK_ECNT + - pass - 11.662 sec +TEST: stat_analysis_AGG_SSVAR + - pass - 1.132 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_mode.xml + +TEST: mode_NO_MATCH_MERGE + - pass - 2.319 sec +TEST: mode_NO_MERGE + - pass - 1.94 sec +TEST: mode_MERGE_BOTH + - pass - 3.724 sec +TEST: mode_MASK_POLY + - pass - 1.96 sec +TEST: mode_QUILT + - pass - 5.595 sec +TEST: mode_CONFIG_MERGE + - pass - 3.549 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_mode_multivar.xml + +TEST: mode_multivar_snow + - pass - 36.544 sec +TEST: mode_multivar_snow_3_2 + - pass - 19.229 sec +TEST: mode_multivar_snow_super + - pass - 31.774 sec +TEST: mode_multivar_FAKE_DATA + - pass - 4.152 sec +TEST: mode_multivar_FAKE_DATA_with_intensities + - pass - 6.589 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_mode_analysis.xml + +TEST: mode_analysis_BYCASE_SIMPLE + - pass - 0.758 sec +TEST: mode_analysis_BYCASE_CLUSTER + - pass - 0.55 sec +TEST: mode_analysis_MET-644_LOOKIN_BY_DIR + - pass - 0.593 sec +TEST: mode_analysis_MET-644_LOOKIN_BY_FILE + - pass - 0.553 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_plot_point_obs.xml + +TEST: plot_point_obs_G218 + - pass - 4.803 sec +TEST: plot_point_obs_TMP_ADPUPA + - pass - 4.345 sec +TEST: plot_point_obs_CONFIG + - pass - 4.309 sec +TEST: plot_point_obs_CONFIG_REGRID + - pass - 4.069 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_plot_data_plane.xml + +TEST: plot_data_plane_GRIB1 + - pass - 1.385 sec +TEST: plot_data_plane_GRIB1_REC + - pass - 0.934 sec +TEST: plot_data_plane_GRIB1_CODE + - pass - 0.916 sec +TEST: plot_data_plane_GRIB1_ENS + - pass - 0.825 sec +TEST: plot_data_plane_GRIB1_ENS_HI + - pass - 0.793 sec +TEST: plot_data_plane_GRIB1_rotlatlon + - pass - 0.721 sec +TEST: plot_data_plane_GRIB2 + - pass - 0.858 sec +TEST: plot_data_plane_GRIB2_ENS + - pass - 0.715 sec +TEST: plot_data_plane_GRIB2_ENS_LOW + - pass - 0.725 sec +TEST: plot_data_plane_GRIB2_PROB + - pass - 0.645 sec +TEST: plot_data_plane_NC_PINTERP + - pass - 0.755 sec +TEST: plot_data_plane_NC_MET + - pass - 0.804 sec +TEST: plot_data_plane_NCCF_lc_0 + - pass - 0.746 sec +TEST: plot_data_plane_NCCF_lc_25 + - pass - 0.815 sec +TEST: plot_data_plane_NCCF_lc_50 + - pass - 0.802 sec +TEST: plot_data_plane_NCCF_latlon_0 + - pass - 1.003 sec +TEST: plot_data_plane_NCCF_latlon_12 + - pass - 0.976 sec +TEST: plot_data_plane_NCCF_latlon_25 + - pass - 0.976 sec +TEST: plot_data_plane_NCCF_latlon_by_value + - pass - 0.956 sec +TEST: plot_data_plane_NCCF_north_to_south + - pass - 4.35 sec +TEST: plot_data_plane_NCCF_time + - pass - 0.681 sec +TEST: plot_data_plane_NCCF_time_int64 + - pass - 0.73 sec +TEST: plot_data_plane_NCCF_rotlatlon + - pass - 0.934 sec +TEST: plot_data_plane_TRMM_3B42_3hourly_nc + - pass - 1.041 sec +TEST: plot_data_plane_TRMM_3B42_daily_nc + - pass - 1.233 sec +TEST: plot_data_plane_TRMM_3B42_daily_packed + - pass - 1.209 sec +TEST: plot_data_plane_TRMM_3B42_daily_packed_CONVERT + - pass - 1.71 sec +TEST: plot_data_plane_EaSM_CMIP5_rcp85 + - pass - 0.752 sec +TEST: plot_data_plane_EaSM_CMIP5_rcp85_time_slice + - pass - 0.76 sec +TEST: plot_data_plane_EaSM_CESM + - pass - 0.771 sec +TEST: plot_data_plane_GRIB2_NBM_CWASP_L0 + - pass - 3.072 sec +TEST: plot_data_plane_GRIB2_NBM_CWASP_PERC_5 + - pass - 3.146 sec +TEST: plot_data_plane_GRIB2_NBM_CWASP_PROB_50 + - pass - 2.262 sec +TEST: plot_data_plane_GRIB2_NBM_WETBT_MIXED_LEVELS + - pass - 3.377 sec +TEST: plot_data_plane_GRIB2_NBM_FICEAC_A48_PERC_10 + - pass - 2.102 sec +TEST: plot_data_plane_LAEA_GRIB2 + - pass - 1.631 sec +TEST: plot_data_plane_LAEA_NCCF + - pass - 1.595 sec +TEST: plot_data_plane_LAEA_MET_NC + - pass - 1.634 sec +TEST: plot_data_plane_NCCF_POLAR_STEREO + - pass - 1.747 sec +TEST: plot_data_plane_NCCF_POLAR_ELLIPSOIDAL + - pass - 0.719 sec +TEST: plot_data_plane_GRIB2_TABLE_4.48 + - pass - 1.514 sec +TEST: plot_data_plane_WRF_west_east_stag + - pass - 0.864 sec +TEST: plot_data_plane_WRF_south_north_stag + - pass - 0.863 sec +TEST: plot_data_plane_WRF_num_press_levels_stag + - pass - 0.761 sec +TEST: plot_data_plane_WRF_num_z_levels_stag + - pass - 0.741 sec +TEST: plot_data_plane_WRF_bottom_top + - pass - 0.866 sec +TEST: plot_data_plane_WRF_bottom_top_stag + - pass - 0.852 sec +TEST: plot_data_plane_set_attr_grid + - pass - 14.222 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_wwmca_plot.xml + +TEST: wwmca_plot_NH_SH_AGE_240 + - pass - 2.607 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_series_analysis.xml + +TEST: series_analysis_CMD_LINE + - pass - 8.969 sec +TEST: series_analysis_AGGR_CMD_LINE + - pass - 10.585 sec +TEST: series_analysis_FILE_LIST + - pass - 6.317 sec +TEST: series_analysis_AGGR_FILE_LIST + - pass - 7.913 sec +TEST: series_analysis_UPPER_AIR + - pass - 3.876 sec +TEST: series_analysis_CONDITIONAL + - pass - 3.963 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_tc_dland.xml + +TEST: tc_dland_ONE_DEG + - pass - 17.215 sec +TEST: tc_dland_HALF_DEG + - pass - 65.738 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_tc_pairs.xml + +TEST: tc_pairs_ALAL2010 + - pass - 2.412 sec +TEST: tc_pairs_CONSENSUS + - pass - 5.688 sec +TEST: tc_pairs_INTERP12_FILL + - pass - 0.923 sec +TEST: tc_pairs_INTERP12_REPLACE + - pass - 0.938 sec +TEST: tc_pairs_PROBRIRW + - pass - 2.656 sec +TEST: tc_pairs_BASIN_MAP + - pass - 2.661 sec +TEST: tc_pairs_LEAD_REQ + - pass - 1.023 sec +TEST: tc_pairs_WRITE_VALID + - pass - 0.741 sec +TEST: tc_pairs_WRITE_VALID_PROBRIRW + - pass - 2.105 sec +TEST: tc_pairs_DIAGNOSTICS + - pass - 0.932 sec +TEST: tc_pairs_DIAGNOSTICS_CONSENSUS + - pass - 5.124 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_tc_stat.xml + +TEST: tc_stat_ALAL2010 + - pass - 24.263 sec +TEST: tc_stat_FILTER_STRINGS + - pass - 2.475 sec +TEST: tc_stat_PROBRIRW + - pass - 40.831 sec +TEST: tc_stat_LEAD_REQ + - pass - 1.483 sec +TEST: tc_stat_FALSE_ALARMS + - pass - 2.438 sec +TEST: tc_stat_DIAGNOSTICS + - pass - 7.112 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_plot_tc.xml + +TEST: plot_tc_TCMPR + - pass - 9.155 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_tc_rmw.xml + +TEST: tc_rmw_PRESSURE_LEV_OUT + - pass - 39.751 sec +TEST: tc_rmw_GONZALO + - pass - 9.403 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_rmw_analysis.xml + +TEST: rmw_analysis + - pass - 1.61 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_tc_diag.xml + +TEST: tc_diag_IAN + - pass - 117.499 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_tc_gen.xml + +TEST: tc_gen_2016 + - pass - 94.491 sec +TEST: tc_gen_prob + - pass - 1.05 sec +TEST: tc_gen_2021_shape + - pass - 9.322 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_met_test_scripts.xml + +TEST: test_all_gen_vx_mask_1 + - pass - 1.263 sec +TEST: test_all_gen_vx_mask_2 + - pass - 1.054 sec +TEST: test_all_gen_ens_prod + - pass - 1.72 sec +TEST: test_all_pcp_combine_1 + - pass - 1.855 sec +TEST: test_all_pcp_combine_2 + - pass - 3.97 sec +TEST: test_all_pcp_combine_3 + - pass - 5.307 sec +TEST: test_all_pcp_combine_4 + - pass - 1.066 sec +TEST: test_all_pcp_combine_5 + - pass - 1.075 sec +TEST: test_all_pcp_combine_6 + - pass - 1.563 sec +TEST: test_all_mode_1 + - pass - 2.505 sec +TEST: test_all_mode_2 + - pass - 2.463 sec +TEST: test_all_mode_3 + - pass - 3.169 sec +TEST: test_all_grid_stat_1 + - pass - 2.189 sec +TEST: test_all_grid_stat_2 + - pass - 0.646 sec +TEST: test_all_grid_stat_3 + - pass - 1.26 sec +TEST: test_all_grid_stat_4 + - pass - 8.559 sec +TEST: test_all_pb2nc + - pass - 3.645 sec +TEST: test_all_plot_point_obs + - pass - 5.294 sec +TEST: test_all_ascii2nc_1 + - pass - 0.599 sec +TEST: test_all_ascii2nc_2 + - pass - 0.715 sec +TEST: test_all_madis2nc + - pass - 1.521 sec +TEST: test_all_point_stat + - pass - 72.135 sec +TEST: test_all_wavelet_stat_1 + - pass - 4.892 sec +TEST: test_all_wavelet_stat_2 + - pass - 2.751 sec +TEST: test_all_ensemble_stat + - pass - 9.129 sec +TEST: test_all_stat_analysis + - pass - 15.05 sec +TEST: test_all_mode_analysis_1 + - pass - 0.713 sec +TEST: test_all_mode_analysis_2 + - pass - 0.561 sec +TEST: test_all_mode_analysis_3 + - pass - 0.574 sec +TEST: test_all_plot_data_plane_1 + - pass - 1.109 sec +TEST: test_all_plot_data_plane_2 + - pass - 0.662 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_modis.xml + +TEST: modis_regrid_SURFACE_TEMPERATURE + - pass - 3.171 sec +TEST: modis_regrid_CLOUD_FRACTION + - pass - 2.371 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_ref_config_lead_00.xml + +TEST: gen_vx_mask + - pass - 2.464 sec +TEST: pb2nc_ndas_lead_00 + - pass - 8.045 sec +TEST: point_stat_lead_00_upper_air_AFWAv3.4_Noahv2.7.1 + - pass - 48.708 sec +TEST: point_stat_lead_00_surface_AFWAv3.4_Noahv2.7.1 + - pass - 24.294 sec +TEST: point_stat_lead_00_winds_AFWAv3.4_Noahv2.7.1 + - pass - 56.158 sec +TEST: point_stat_lead_00_upper_air_AFWAv3.4_Noahv3.3 + - pass - 48.776 sec +TEST: point_stat_lead_00_surface_AFWAv3.4_Noahv3.3 + - pass - 24.526 sec +TEST: point_stat_lead_00_winds_AFWAv3.4_Noahv3.3 + - pass - 56.448 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_ref_config_lead_12.xml + +TEST: gen_vx_mask + - pass - 2.225 sec +TEST: pb2nc_ndas_lead_12 + - pass - 8.146 sec +TEST: pcp_combine_ST2ml_3hr_09_12 + - pass - 0.909 sec +TEST: pcp_combine_wrf_3hr_09_12 + - pass - 1.143 sec +TEST: point_stat_lead_12_upper_air_AFWAv3.4_Noahv2.7.1 + - pass - 48.863 sec +TEST: point_stat_lead_12_surface_AFWAv3.4_Noahv2.7.1 + - pass - 25.401 sec +TEST: point_stat_lead_12_winds_AFWAv3.4_Noahv2.7.1 + - pass - 55.69 sec +TEST: point_stat_lead_12_upper_air_AFWAv3.4_Noahv3.3 + - pass - 48.919 sec +TEST: point_stat_lead_12_surface_AFWAv3.4_Noahv3.3 + - pass - 25.006 sec +TEST: point_stat_lead_12_winds_AFWAv3.4_Noahv3.3 + - pass - 55.808 sec +TEST: grid_stat_3hr_accum_time_12 + - pass - 0.883 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_ref_config_lead_24.xml + +TEST: gen_vx_mask + - pass - 2.181 sec +TEST: pb2nc_ndas_lead_24 + - pass - 7.926 sec +TEST: pcp_combine_ST2ml_3hr_21_24 + - pass - 0.76 sec +TEST: pcp_combine_wrf_3hr_21_24 + - pass - 1.115 sec +TEST: point_stat_lead_24_upper_air_AFWAv3.4_Noahv2.7.1 + - pass - 47.982 sec +TEST: point_stat_lead_24_surface_AFWAv3.4_Noahv2.7.1 + - pass - 23.972 sec +TEST: point_stat_lead_24_winds_AFWAv3.4_Noahv2.7.1 + - pass - 54.219 sec +TEST: point_stat_lead_24_upper_air_AFWAv3.4_Noahv3.3 + - pass - 48.24 sec +TEST: point_stat_lead_24_surface_AFWAv3.4_Noahv3.3 + - pass - 24.114 sec +TEST: point_stat_lead_24_winds_AFWAv3.4_Noahv3.3 + - pass - 54.602 sec +TEST: grid_stat_3hr_accum_time_24 + - pass - 0.892 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_ref_config_lead_36.xml + +TEST: gen_vx_mask + - pass - 2.186 sec +TEST: pb2nc_ndas_lead_36 + - pass - 8.025 sec +TEST: pcp_combine_ST2ml_3hr_33_36 + - pass - 0.697 sec +TEST: pcp_combine_ST2ml_24hr_12_36 + - pass - 0.596 sec +TEST: pcp_combine_wrf_3hr_33_36 + - pass - 1.092 sec +TEST: pcp_combine_wrf_24hr_12_36 + - pass - 1.108 sec +TEST: point_stat_lead_36_upper_air_AFWAv3.4_Noahv2.7.1 + - pass - 48.391 sec +TEST: point_stat_lead_36_surface_AFWAv3.4_Noahv2.7.1 + - pass - 24.7 sec +TEST: point_stat_lead_36_winds_AFWAv3.4_Noahv2.7.1 + - pass - 54.79 sec +TEST: point_stat_lead_36_upper_air_AFWAv3.4_Noahv3.3 + - pass - 48.341 sec +TEST: point_stat_lead_36_surface_AFWAv3.4_Noahv3.3 + - pass - 24.723 sec +TEST: point_stat_lead_36_winds_AFWAv3.4_Noahv3.3 + - pass - 54.725 sec +TEST: grid_stat_3hr_accum_time_36 + - pass - 0.917 sec +TEST: grid_stat_24hr_accum_time_36 + - pass - 0.896 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_ref_config_lead_48.xml + +TEST: gen_vx_mask + - pass - 2.193 sec +TEST: pb2nc_ndas_lead_48 + - pass - 8.014 sec +TEST: pcp_combine_ST2ml_3hr_45_48 + - pass - 0.72 sec +TEST: pcp_combine_wrf_3hr_45_48 + - pass - 1.101 sec +TEST: point_stat_lead_48_upper_air_AFWAv3.4_Noahv2.7.1 + - pass - 48.027 sec +TEST: point_stat_lead_48_surface_AFWAv3.4_Noahv2.7.1 + - pass - 24.714 sec +TEST: point_stat_lead_48_winds_AFWAv3.4_Noahv2.7.1 + - pass - 54.824 sec +TEST: point_stat_lead_48_upper_air_AFWAv3.4_Noahv3.3 + - pass - 48.048 sec +TEST: point_stat_lead_48_surface_AFWAv3.4_Noahv3.3 + - pass - 24.41 sec +TEST: point_stat_lead_48_winds_AFWAv3.4_Noahv3.3 + - pass - 55.39 sec +TEST: grid_stat_3hr_accum_time_48 + - pass - 0.893 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_ref_config.xml + +TEST: stat_analysis_GO_Index + - pass - 1.468 sec +TEST: stat_analysis_GO_Index_out_stat + - pass - 1.179 sec +TEST: stat_analysis_SFC_SS_Index_out + - pass - 0.874 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_mode_graphics.xml + +TEST: mode_graphics_PLOT_MULTIPLE + - pass - 63.828 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_regrid.xml + +TEST: regrid_grid_stat_ST4_TO_HMT + - pass - 1.052 sec +TEST: regrid_grid_stat_HMT_TO_ST4 + - pass - 8.112 sec +TEST: regrid_grid_stat_BOTH_TO_DTC165 + - pass - 1.594 sec +TEST: regrid_grid_stat_BOTH_TO_NAM + - pass - 5.106 sec +TEST: regrid_grid_stat_BOTH_TO_HMT_D02 + - pass - 1.577 sec +TEST: regrid_data_plane_GFS_TO_HMT_NEAREST + - pass - 2.858 sec +TEST: regrid_data_plane_GFS_ROTLATLON_GRID_SPEC + - pass - 2.465 sec +TEST: regrid_data_plane_GFS_TO_HMT_BILIN + - pass - 2.782 sec +TEST: regrid_data_plane_GFS_TO_HMT_BUDGET + - pass - 2.539 sec +TEST: regrid_data_plane_GFS_TO_HMT_MIN_3 + - pass - 3.824 sec +TEST: regrid_data_plane_GFS_TO_HMT_MAX_3 + - pass - 3.887 sec +TEST: regrid_data_plane_GFS_TO_HMT_UW_MEAN_3 + - pass - 3.837 sec +TEST: regrid_data_plane_GFS_TO_HMT_UW_MEAN_9 + - pass - 19.083 sec +TEST: regrid_data_plane_GFS_TO_HMT_DW_MEAN_3 + - pass - 3.94 sec +TEST: regrid_data_plane_HRRR_MAXGAUSS + - pass - 4.686 sec +TEST: regrid_data_plane_GFS_TO_HMT_MEDIAN_3 + - pass - 3.921 sec +TEST: regrid_data_plane_GFS_TO_HMT_LS_FIT_3 + - pass - 3.982 sec +TEST: regrid_data_plane_GFS_TO_HMT_MAX_5_SQUARE + - pass - 1.92 sec +TEST: regrid_data_plane_GFS_TO_G212_CONVERT_CENSOR + - pass - 0.893 sec +TEST: regrid_data_plane_WRAP_LON + - pass - 1.438 sec +TEST: regrid_data_plane_NC_ROT_LAT_LON + - pass - 2.385 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_gsi_tools.xml + +TEST: gsid2mpr_CONV + - pass - 2.161 sec +TEST: gsid2mpr_DUP + - pass - 1.27 sec +TEST: gsid2mpr_RAD + - pass - 2.469 sec +TEST: gsidens2orank_CONV_NO_MEAN + - pass - 5.607 sec +TEST: gsidens2orank_CONV_ENS_MEAN + - pass - 4.809 sec +TEST: gsidens2orank_RAD + - pass - 4.108 sec +TEST: gsidens2orank_RAD_CHANNEL + - pass - 1.14 sec +TEST: stat_analysis_MPR_TO_CNT + - pass - 2.631 sec +TEST: stat_analysis_ORANK_TO_RHIST + - pass - 23.803 sec +TEST: stat_analysis_ORANK_TO_SSVAR + - pass - 23.779 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_aeronet.xml + +TEST: ascii2nc_AERONET_daily + - pass - 0.814 sec +TEST: ascii2nc_AERONET_v3_daily + - pass - 0.537 sec +TEST: ascii2nc_AERONET_v3_concat + - pass - 0.574 sec +TEST: ascii2nc_AERONET_vld_thresh + - pass - 0.545 sec +TEST: ascii2nc_AERONET_monthly + - pass - 0.698 sec +TEST: point_stat_GRIB2_f18_NGAC_AERONET_daily + - pass - 0.59 sec +TEST: point_stat_GRIB2_f18_NGAC_AERONET_monthly + - pass - 0.639 sec +TEST: point_stat_GRIB2_f21_NGAC_AERONET_daily + - pass - 0.595 sec +TEST: point_stat_GRIB2_f21_NGAC_AERONET_monthly + - pass - 0.639 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_shift_data_plane.xml + +TEST: shift_data_plane_GRIB1 + - pass - 4.387 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_mtd.xml + +TEST: mtd_basic + - pass - 45.285 sec +TEST: mtd_conv_time + - pass - 47.593 sec +TEST: mtd_single + - pass - 11.058 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_climatology_1.0deg.xml + +TEST: climatology_POINT_STAT_GFS_1.0DEG + - pass - 128.342 sec +TEST: climatology_POINT_STAT_GFS_1.0DEG_CLIMO_PREV_MONTH + - pass - 127.347 sec +TEST: climatology_POINT_STAT_PROB_GFS_1.0DEG + - pass - 11.152 sec +TEST: climatology_GRID_STAT_PROB_GFS_1.0DEG + - pass - 8.402 sec +TEST: climatology_STAT_ANALYSIS_1.0DEG + - pass - 3.124 sec +TEST: climatology_SERIES_ANALYSIS_1.0DEG + - pass - 168.005 sec +TEST: climatology_SERIES_ANALYSIS_1.0DEG_CONST_CLIMO + - pass - 49.084 sec +TEST: climatology_SERIES_ANALYSIS_1.0DEG_AGGR + - pass - 215.56 sec +TEST: climatology_SERIES_ANALYSIS_PROB_1.0DEG + - pass - 20.95 sec +TEST: climatology_SERIES_ANALYSIS_PROB_1.0DEG_AGGR + - pass - 24.843 sec +TEST: climatology_ENSEMBLE_STAT_1.0DEG + - pass - 31.429 sec +TEST: climatology_ENSEMBLE_STAT_1.0DEG_ONE_CDF_BIN + - pass - 11.051 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_climatology_1.5deg.xml + +TEST: climatology_POINT_STAT_WMO_1.5DEG + - pass - 245.695 sec +TEST: climatology_STAT_ANALYSIS_WMO_1.5DEG_MPR_AGG_STAT + - pass - 0.741 sec +TEST: climatology_STAT_ANALYSIS_WMO_1.5DEG_VAL1L2_AGG_STAT + - pass - 0.607 sec +TEST: climatology_STAT_ANALYSIS_WMO_1.5DEG_FILTER + - pass - 0.758 sec +TEST: climatology_GRID_STAT_WMO_1.5DEG + - pass - 253.773 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_climatology_2.5deg.xml + +TEST: climatology_POINT_STAT_GFS_2.5DEG + - pass - 207.139 sec +TEST: climatology_GRID_STAT_WRAP_YEAR_2.5DEG + - pass - 126.605 sec +TEST: climatology_GRID_STAT_SINGLE_MONTH_2.5DEG + - pass - 64.927 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_climatology_mixed.xml + +TEST: climatology_GRID_STAT_FCST_NCEP_1.0DEG_OBS_WMO_1.5DEG + - pass - 72.612 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_grib_tables.xml + +TEST: GRIB1_um_dcf + - pass - 2.76 sec +TEST: GRIB2_um_raw + - pass - 2.366 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_grid_weight.xml + +TEST: grid_weight_GRID_STAT_NONE + - pass - 6.082 sec +TEST: grid_weight_GRID_STAT_COS_LAT + - pass - 6.075 sec +TEST: grid_weight_GRID_STAT_AREA + - pass - 6.081 sec +TEST: grid_weight_ENSEMBLE_STAT_NONE + - pass - 2.327 sec +TEST: grid_weight_ENSEMBLE_STAT_COS_LAT + - pass - 2.287 sec +TEST: grid_weight_ENSEMBLE_STAT_AREA + - pass - 2.291 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_netcdf.xml + +TEST: ascii2nc_no_compression + - pass - 16.393 sec +TEST: ascii2nc_compression2_by_config + - pass - 16.728 sec +TEST: ascii2nc_compression3_by_env + - pass - 16.411 sec +TEST: ascii2nc_compression4_by_argument + - pass - 16.483 sec +TEST: 365_days + - pass - 1.913 sec +TEST: netcdf_1byte_time + - pass - 0.934 sec +TEST: netcdf_months_units + - pass - 0.653 sec +TEST: netcdf_months_units_from_day2 + - pass - 0.535 sec +TEST: netcdf_months_units_to_next_month + - pass - 0.537 sec +TEST: netcdf_years_units + - pass - 0.535 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_hira.xml + +WARNING: unable to read test_dir from /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_hira.xml +TEST: point_stat_NCMET_NAM_HMTGAGE_HIRA + - pass - 10.392 sec +TEST: point_stat_HIRA_EMPTY_PROB_CAT_THRESH + - pass - 9.472 sec +TEST: stat_analysis_CONFIG_HIRA + - pass - 4.317 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_interp_shape.xml + +TEST: grid_stat_INTERP_SQUARE + - pass - 6.366 sec +TEST: grid_stat_INTERP_CIRCLE + - pass - 5.351 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_lidar2nc.xml + +TEST: lidar2nc_CALIPSO + - pass - 1.126 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_ioda2nc.xml + +TEST: ioda2nc_mask_sid_list + - pass - 1.612 sec +TEST: ioda2nc_var_all + - pass - 0.549 sec +TEST: ioda2nc_summary + - pass - 0.566 sec +TEST: ioda2nc_same_input + - pass - 0.565 sec +TEST: ioda2nc_int_datetime + - pass - 0.575 sec +TEST: ioda2nc_v2_string_sid + - pass - 0.576 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_airnow.xml + +TEST: pb2nc_AIRNOW + - pass - 16.993 sec +TEST: point_stat_GRIB2_AIRNOW + - pass - 2.994 sec + +CALLING: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/xml/unit_python.xml + +TEST: python_numpy_grid_name + - pass - 1.728 sec +TEST: python_numpy_grid_string + - pass - 1.147 sec +TEST: python_numpy_grid_data_file + - pass - 1.163 sec +TEST: python_numpy_plot_data_plane + - pass - 1.221 sec +TEST: python_xarray_plot_data_plane + - pass - 1.193 sec +TEST: python_numpy_plot_data_plane_missing + - FAIL - 0.533 sec +export MET_PYTHON_EXE=${MET_TEST_MET_PYTHON_EXE} +/d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../share/met/../../bin/plot_data_plane \ + PYTHON_NUMPY \ + /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../test_output/python/letter_numpy_0_to_missing.ps \ + 'name = "/d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../share/met/python/examples/read_ascii_numpy.py /d1/projects/MET/MET_test_data/unit_test/python/letter.txt LETTER 0.0";' \ + -plot_range 0.0 255.0 \ + -title "Python enabled numpy plot_data_plane" \ + -v 1 +DEBUG 1: Start plot_data_plane by johnhg(6088) at 2024-10-07 17:45:28Z cmd: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../share/met/../../bin/plot_data_plane PYTHON_NUMPY /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../test_output/python/letter_numpy_0_to_missing.ps name = "/d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../share/met/python/examples/read_ascii_numpy.py /d1/projects/MET/MET_test_data/unit_test/python/letter.txt LETTER 0.0"; -plot_range 0.0 255.0 -title Python enabled numpy plot_data_plane -v 1 +DEBUG 1: Opening data file: PYTHON_NUMPY +sh: 1: /usr/local/python3/bin/python3: not found +ERROR : +ERROR : tmp_nc_dataplane() -> command "${MET_TEST_MET_PYTHON_EXE} /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../share/met/python/pyembed/write_tmp_dataplane.py /tmp/tmp_met_data_386958_0 /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/../../share/met/python/examples/read_ascii_numpy /d1/projects/MET/MET_test_data/unit_test/python/letter.txt LETTER 0.0" failed ... status = 32512 +ERROR : +unset MET_PYTHON_EXE + + +ERROR: /d1/personal/johnhg/MET/MET_development/MET-feature_2887_categorical_weights/internal/test_unit/python/unit.py unit_python.xml failed. + +*** UNIT TESTS FAILED *** + diff --git a/internal/test_unit/xml/unit_grid_weight.xml b/internal/test_unit/xml/unit_grid_weight.xml index 85005feec1..979ebad495 100644 --- a/internal/test_unit/xml/unit_grid_weight.xml +++ b/internal/test_unit/xml/unit_grid_weight.xml @@ -22,6 +22,7 @@ &MET_BIN;/grid_stat OUTPUT_PREFIX NO_WEIGHT + DESC NO_WEIGHT CLIMO_MEAN_FILE &DATA_DIR_CLIMO;/NCEP_1.0deg/cmean_1d.19790410 GRID_WEIGHT NONE @@ -32,7 +33,7 @@ -outdir &OUTPUT_DIR;/grid_weight -v 1 - &OUTPUT_DIR;/grid_weight/grid_stat_NO_WEIGHT_240000L_20120410_000000V.stat + &OUTPUT_DIR;/grid_weight/grid_stat_NO_WEIGHT_240000L_20120410_000000V.stat &OUTPUT_DIR;/grid_weight/grid_stat_NO_WEIGHT_240000L_20120410_000000V_pairs.nc @@ -41,6 +42,7 @@ &MET_BIN;/grid_stat OUTPUT_PREFIX COS_LAT_WEIGHT + DESC COS_LAT_WEIGHT CLIMO_MEAN_FILE &DATA_DIR_CLIMO;/NCEP_1.0deg/cmean_1d.19790410 GRID_WEIGHT COS_LAT @@ -51,7 +53,7 @@ -outdir &OUTPUT_DIR;/grid_weight -v 1 - &OUTPUT_DIR;/grid_weight/grid_stat_COS_LAT_WEIGHT_240000L_20120410_000000V.stat + &OUTPUT_DIR;/grid_weight/grid_stat_COS_LAT_WEIGHT_240000L_20120410_000000V.stat &OUTPUT_DIR;/grid_weight/grid_stat_COS_LAT_WEIGHT_240000L_20120410_000000V_pairs.nc @@ -60,6 +62,7 @@ &MET_BIN;/grid_stat OUTPUT_PREFIX AREA_WEIGHT + DESC AREA_WEIGHT CLIMO_MEAN_FILE &DATA_DIR_CLIMO;/NCEP_1.0deg/cmean_1d.19790410 GRID_WEIGHT AREA @@ -70,7 +73,7 @@ -outdir &OUTPUT_DIR;/grid_weight -v 1 - &OUTPUT_DIR;/grid_weight/grid_stat_AREA_WEIGHT_240000L_20120410_000000V.stat + &OUTPUT_DIR;/grid_weight/grid_stat_AREA_WEIGHT_240000L_20120410_000000V.stat &OUTPUT_DIR;/grid_weight/grid_stat_AREA_WEIGHT_240000L_20120410_000000V_pairs.nc @@ -78,9 +81,11 @@ &MET_BIN;/ensemble_stat - OUTPUT_PREFIX NO_WEIGHT - CLIMO_MEAN_FILE &DATA_DIR_CLIMO;/NCEP_1.0deg/cmean_1d.19790410 - GRID_WEIGHT NONE + OUTPUT_PREFIX NO_WEIGHT + DESC NO_WEIGHT + CLIMO_MEAN_FILE &DATA_DIR_CLIMO;/NCEP_1.0deg/cmean_1d.19790410 + CLIMO_STDEV_FILE &DATA_DIR_CLIMO;/NCEP_1.0deg/cstdv_1d.19790410 + GRID_WEIGHT NONE \ 6 \ @@ -104,7 +109,9 @@ &MET_BIN;/ensemble_stat OUTPUT_PREFIX COS_LAT_WEIGHT + DESC COS_LAT_WEIGHT CLIMO_MEAN_FILE &DATA_DIR_CLIMO;/NCEP_1.0deg/cmean_1d.19790410 + CLIMO_STDEV_FILE &DATA_DIR_CLIMO;/NCEP_1.0deg/cstdv_1d.19790410 GRID_WEIGHT COS_LAT \ @@ -129,7 +136,9 @@ &MET_BIN;/ensemble_stat OUTPUT_PREFIX AREA_WEIGHT + DESC AREA_WEIGHT CLIMO_MEAN_FILE &DATA_DIR_CLIMO;/NCEP_1.0deg/cmean_1d.19790410 + CLIMO_STDEV_FILE &DATA_DIR_CLIMO;/NCEP_1.0deg/cstdv_1d.19790410 GRID_WEIGHT AREA \ diff --git a/src/libcode/vx_stat_out/stat_columns.cc b/src/libcode/vx_stat_out/stat_columns.cc index d971fe2cf7..ac8530f6a5 100644 --- a/src/libcode/vx_stat_out/stat_columns.cc +++ b/src/libcode/vx_stat_out/stat_columns.cc @@ -101,19 +101,19 @@ ConcatString append_climo_bin(const ConcatString &mask_name, void write_header_row(const char * const * cols, int n_cols, int hdr_flag, AsciiTable &at, int r, int c) { - int i; // Write the header column names if requested if(hdr_flag) { - for(i=0; in_obs; i++) { + for(int i=0; in_obs; i++) { // Set the observation valid time shc.set_obs_valid_beg(pd_ptr->vld_ta[i]); @@ -1700,7 +1699,6 @@ void write_isc_row(StatHdrColumns &shc, const ISCInfo &isc_info, STATOutputType out_type, AsciiTable &stat_at, int &stat_row, AsciiTable &txt_at, int &txt_row) { - int i; // ISC line type shc.set_line_type(stat_isc_str); @@ -1714,7 +1712,7 @@ void write_isc_row(StatHdrColumns &shc, const ISCInfo &isc_info, // Write a line for each scale plus one for the thresholded binary // field and one for the father wavelet - for(i=-1; i<=isc_info.n_scale; i++) { + for(int i=-1; i<=isc_info.n_scale; i++) { // Write the header columns write_header_cols(shc, stat_at, stat_row); @@ -1902,7 +1900,6 @@ void write_orank_row(StatHdrColumns &shc, const PairDataEnsemble *pd_ptr, STATOutputType out_type, AsciiTable &stat_at, int &stat_row, AsciiTable &txt_at, int &txt_row) { - int i; // Observation Rank line type shc.set_line_type(stat_orank_str); @@ -1914,7 +1911,7 @@ void write_orank_row(StatHdrColumns &shc, const PairDataEnsemble *pd_ptr, shc.set_alpha(bad_data_double); // Write a line for each ensemble pair - for(i=0; in_obs; i++) { + for(int i=0; in_obs; i++) { // Set the observation valid time shc.set_obs_valid_beg(pd_ptr->vld_ta[i]); @@ -1947,7 +1944,6 @@ void write_ssvar_row(StatHdrColumns &shc, const PairDataEnsemble *pd_ptr, double alpha, STATOutputType out_type, AsciiTable &stat_at, int &stat_row, AsciiTable &txt_at, int &txt_row) { - int i; // SSVAR line type shc.set_line_type(stat_ssvar_str); @@ -1961,7 +1957,7 @@ void write_ssvar_row(StatHdrColumns &shc, const PairDataEnsemble *pd_ptr, shc.set_alpha(alpha); // Write a line for each ssvar bin - for(i=0; issvar_bins[0].n_bin; i++) { + for(int i=0; issvar_bins[0].n_bin; i++) { // Write the header columns write_header_cols(shc, stat_at, stat_row); @@ -2088,7 +2084,7 @@ void write_fho_cols(const CTSInfo &cts_info, // O_RATE // at.set_entry(r, c+0, // Total Count - cts_info.cts.n()); + cts_info.cts.n_pairs()); at.set_entry(r, c+1, // Forecast Rate = FY/N cts_info.cts.f_rate()); @@ -2114,7 +2110,7 @@ void write_ctc_cols(const CTSInfo &cts_info, // FN_OY, FN_ON, EC_VALUE // at.set_entry(r, c+0, // Total Count - cts_info.cts.n()); + cts_info.cts.n_pairs()); at.set_entry(r, c+1, // FY_OY cts_info.cts.fy_oy()); @@ -2167,7 +2163,7 @@ void write_cts_cols(const CTSInfo &cts_info, int i, // EC_VALUE // at.set_entry(r, c+0, // Total count - cts_info.cts.n()); + cts_info.cts.n_pairs()); at.set_entry(r, c+1, // Base Rate (oy_tp) cts_info.baser.v); @@ -2805,15 +2801,14 @@ void write_cnt_cols(const CNTInfo &cnt_info, int i, void write_mctc_cols(const MCTSInfo &mcts_info, AsciiTable &at, int r, int c) { - int i, j, col; // // Multi-Category Contingency Table Counts // Dump out the MCTC line: // TOTAL, N_CAT, Fi_Oj, EC_VALUE // - at.set_entry(r, c+0, // Total Count - mcts_info.cts.total()); + at.set_entry(r, c+0, // Total number of pairs + mcts_info.cts.n_pairs()); at.set_entry(r, c+1, // Number of categories mcts_info.cts.nrows()); @@ -2821,8 +2816,9 @@ void write_mctc_cols(const MCTSInfo &mcts_info, // // Loop through the contingency table rows and columns // - for(i=0, col=c+2; irhist_na.n_elements(); i++) { + int col = c+2; + for(int i=0; irhist_na.n_elements(); i++) { at.set_entry(r, col, // RANK_i nint(pd_ptr->rhist_na[i])); @@ -4486,7 +4482,6 @@ void write_rhist_cols(const PairDataEnsemble *pd_ptr, void write_phist_cols(const PairDataEnsemble *pd_ptr, AsciiTable &at, int r, int c) { - int i, col; // // Probability Integral Transform Histogram @@ -4505,7 +4500,8 @@ void write_phist_cols(const PairDataEnsemble *pd_ptr, // // Write BIN_i count for each bin // - for(i=0, col=c+3; iphist_na.n_elements(); i++) { + int col = c+3; + for(int i=0; iphist_na.n_elements(); i++) { at.set_entry(r, col, // BIN_i nint(pd_ptr->phist_na[i])); @@ -4519,7 +4515,6 @@ void write_phist_cols(const PairDataEnsemble *pd_ptr, void write_orank_cols(const PairDataEnsemble *pd_ptr, int i, AsciiTable &at, int r, int c) { - int j, col; // // Ensemble Observation Rank Matched Pairs @@ -4573,7 +4568,8 @@ void write_orank_cols(const PairDataEnsemble *pd_ptr, int i, // // Write ENS_j for each ensemble member // - for(j=0, col=c+12; jn_ens; j++) { + int col = c+12; + for(int j=0; jn_ens; j++) { at.set_entry(r, col, // ENS_j pd_ptr->e_na[j][i]); @@ -4767,7 +4763,6 @@ void write_ssvar_cols(const PairDataEnsemble *pd_ptr, int i, void write_relp_cols(const PairDataEnsemble *pd_ptr, AsciiTable &at, int r, int c) { - int i, col; // // Relative Position @@ -4783,7 +4778,8 @@ void write_relp_cols(const PairDataEnsemble *pd_ptr, // // Write RELP_i count for each bin // - for(i=0, col=c+2; irelp_na.n_elements(); i++) { + int col = c+2; + for(int i=0; irelp_na.n_elements(); i++) { at.set_entry(r, col, // RELP_i pd_ptr->relp_na[i]); diff --git a/src/libcode/vx_statistics/compute_ci.cc b/src/libcode/vx_statistics/compute_ci.cc index 8296cc5d76..3633d61acb 100644 --- a/src/libcode/vx_statistics/compute_ci.cc +++ b/src/libcode/vx_statistics/compute_ci.cc @@ -61,14 +61,14 @@ void compute_normal_ci(double v, double alpha, double se, // //////////////////////////////////////////////////////////////////////// -void compute_proportion_ci(double p, int n, double alpha, double vif, +void compute_proportion_ci(double p, int n_pairs, double alpha, double vif, double &p_cl, double &p_cu) { // // Compute the confidence interval using the Wilson method for all // sizes of n, since it provides a better approximation // - compute_wilson_ci(p, n, alpha, vif, p_cl, p_cu); + compute_wilson_ci(p, n_pairs, alpha, vif, p_cl, p_cu); return; } @@ -81,7 +81,7 @@ void compute_proportion_ci(double p, int n, double alpha, double vif, // //////////////////////////////////////////////////////////////////////// -void compute_wald_ci(double p, int n, double alpha, double vif, +void compute_wald_ci(double p, int n_pairs, double alpha, double vif, double &p_cl, double &p_cu) { double v, cv_normal_l, cv_normal_u; @@ -100,7 +100,7 @@ void compute_wald_ci(double p, int n, double alpha, double vif, // // Compute the upper and lower bounds of the confidence interval // - v = vif*p*(1.0-p)/n; + v = vif*p*(1.0-p)/n_pairs; if(v < 0.0) { p_cl = bad_data_double; @@ -122,10 +122,10 @@ void compute_wald_ci(double p, int n, double alpha, double vif, // //////////////////////////////////////////////////////////////////////// -void compute_wilson_ci(double p, int n_int, double alpha, double vif, +void compute_wilson_ci(double p, int n_pairs, double alpha, double vif, double &p_cl, double &p_cu) { double v, cv_normal_l, cv_normal_u; - long long n = n_int; + long long n = n_pairs; if(is_bad_data(p)) { p_cl = p_cu = bad_data_double; diff --git a/src/libcode/vx_statistics/compute_ci.h b/src/libcode/vx_statistics/compute_ci.h index ddaf68d36a..5617ced8f3 100644 --- a/src/libcode/vx_statistics/compute_ci.h +++ b/src/libcode/vx_statistics/compute_ci.h @@ -28,13 +28,13 @@ static const int wald_sample_threshold = 100; extern void compute_normal_ci(double x, double alpha, double se, double &cl, double &cu); -extern void compute_proportion_ci(double p, int n, double alpha, +extern void compute_proportion_ci(double p, int n_pairs, double alpha, double vif, double &p_cl, double &p_cu); -extern void compute_wald_ci(double p, int n, double alpha, +extern void compute_wald_ci(double p, int n_pairs, double alpha, double vif, double &p_cl, double &p_cu); -extern void compute_wilson_ci(double p, int n, double alpha, +extern void compute_wilson_ci(double p, int n_pairs, double alpha, double vif, double &p_cl, double &p_cu); extern void compute_woolf_ci(double odds, double alpha, diff --git a/src/libcode/vx_statistics/compute_stats.cc b/src/libcode/vx_statistics/compute_stats.cc index 094587b732..3ebd4b9058 100644 --- a/src/libcode/vx_statistics/compute_stats.cc +++ b/src/libcode/vx_statistics/compute_stats.cc @@ -576,7 +576,7 @@ void compute_ctsinfo(const PairDataPoint &pd, const NumArray &i_na, // ClimoPntInfo cpi(pd.fcmn_na[j], pd.fcsd_na[j], pd.ocmn_na[j], pd.ocsd_na[j]); - cts_info.add(pd.f_na[j], pd.o_na[j], &cpi); + cts_info.add(pd.f_na[j], pd.o_na[j], pd.wgt_na[j], &cpi); } // end for i @@ -675,7 +675,7 @@ void compute_mctsinfo(const PairDataPoint &pd, const NumArray &i_na, // ClimoPntInfo cpi(pd.fcmn_na[j], pd.fcsd_na[j], pd.ocmn_na[j], pd.ocsd_na[j]); - mcts_info.add(pd.f_na[j], pd.o_na[j], &cpi); + mcts_info.add(pd.f_na[j], pd.o_na[j], pd.wgt_na[j], &cpi); } // end for i @@ -811,12 +811,12 @@ void compute_pctinfo(const PairDataPoint &pd, bool pstd_flag, // Check the observation thresholds and increment accordingly // if(pct_info.othresh.check(pd.o_na[i], &cpi)) { - pct_info.pct.inc_event(pd.f_na[i]); - if(cmn_flag) pct_info.climo_pct.inc_event(climo_prob[i]); + pct_info.pct.inc_event(pd.f_na[i], pd.wgt_na[i]); + if(cmn_flag) pct_info.climo_pct.inc_event(climo_prob[i], pd.wgt_na[i]); } else { - pct_info.pct.inc_nonevent(pd.f_na[i]); - if(cmn_flag) pct_info.climo_pct.inc_nonevent(climo_prob[i]); + pct_info.pct.inc_nonevent(pd.f_na[i], pd.wgt_na[i]); + if(cmn_flag) pct_info.climo_pct.inc_nonevent(climo_prob[i], pd.wgt_na[i]); } } // end for i diff --git a/src/libcode/vx_statistics/contable.cc b/src/libcode/vx_statistics/contable.cc index a5b2ab49a1..b0e2e2d5e1 100644 --- a/src/libcode/vx_statistics/contable.cc +++ b/src/libcode/vx_statistics/contable.cc @@ -74,6 +74,9 @@ ContingencyTable & ContingencyTable::operator+=(const ContingencyTable & t) { exit(1); } + // Increment the number of pairs + Npairs += t.Npairs; + // Increment table entries for(int i=0; i E; + // This is really a two-dimensional array (Nrows, Ncols) + std::vector E; - int Nrows; - int Ncols; + int Nrows; + int Ncols; - double ECvalue; + int Npairs; + double ECvalue; - ConcatString Name; + ConcatString Name; public: @@ -67,6 +69,7 @@ class ContingencyTable { virtual void set_size(int); virtual void set_size(int NR, int NC); + void set_n_pairs(int); void set_ec_value(double); void set_name(const char *); @@ -74,6 +77,7 @@ class ContingencyTable { int nrows() const; int ncols() const; + int n_pairs() const; double ec_value() const; ConcatString name() const; @@ -110,6 +114,7 @@ class ContingencyTable { inline int ContingencyTable::nrows() const { return Nrows; } inline int ContingencyTable::ncols() const { return Ncols; } +inline int ContingencyTable::n_pairs() const { return Npairs; } inline double ContingencyTable::ec_value() const { return ECvalue; } inline ConcatString ContingencyTable::name() const { return Name; } @@ -159,18 +164,16 @@ class Nx2ContingencyTable : public ContingencyTable { void inc_nonevent (double value, double weight=1.0); // Get table entries - double event_count_by_thresh(double) const; - double nonevent_count_by_thresh(double) const; + double event_total_by_thresh(double) const; + double nonevent_total_by_thresh(double) const; - double event_count_by_row(int row) const; - double nonevent_count_by_row(int row) const; + double event_total_by_row(int row) const; + double nonevent_total_by_row(int row) const; // Set counts void set_event(int row, double); void set_nonevent(int row, double); - double n() const; - // Column totals double event_col_total() const; double nonevent_col_total() const; @@ -202,8 +205,8 @@ class Nx2ContingencyTable : public ContingencyTable { //////////////////////////////////////////////////////////////////////// -inline double Nx2ContingencyTable::event_count_by_row (int row) const { return entry(row, nx2_event_column); } -inline double Nx2ContingencyTable::nonevent_count_by_row (int row) const { return entry(row, nx2_nonevent_column); } +inline double Nx2ContingencyTable::event_total_by_row (int row) const { return entry(row, nx2_event_column); } +inline double Nx2ContingencyTable::nonevent_total_by_row (int row) const { return entry(row, nx2_nonevent_column); } inline double Nx2ContingencyTable::event_col_total () const { return col_total(nx2_event_column); } inline double Nx2ContingencyTable::nonevent_col_total () const { return col_total(nx2_nonevent_column); } @@ -253,8 +256,6 @@ class TTContingencyTable : public ContingencyTable { double fn() const; double fy() const; - double n() const; - // FHO rates where: // f_rate = FY/N // h_rate = fy_oy/N diff --git a/src/libcode/vx_statistics/contable_nx2.cc b/src/libcode/vx_statistics/contable_nx2.cc index 15d6a18b67..b41ec7a798 100644 --- a/src/libcode/vx_statistics/contable_nx2.cc +++ b/src/libcode/vx_statistics/contable_nx2.cc @@ -90,12 +90,6 @@ void Nx2ContingencyTable::assign(const Nx2ContingencyTable & t) { //////////////////////////////////////////////////////////////////////// -double Nx2ContingencyTable::n() const { - return total(); -} - -//////////////////////////////////////////////////////////////////////// - void Nx2ContingencyTable::set_size(int N) { ContingencyTable::set_size(N, 2); return; @@ -205,11 +199,11 @@ void Nx2ContingencyTable::inc_nonevent(double t, double weight) { //////////////////////////////////////////////////////////////////////// -double Nx2ContingencyTable::event_count_by_thresh(double t) const { +double Nx2ContingencyTable::event_total_by_thresh(double t) const { int r = value_to_row(t); if(r < 0) { - mlog << Error << "\nNx2ContingencyTable::event_count_by_thresh(double) -> " + mlog << Error << "\nNx2ContingencyTable::event_total_by_thresh(double) -> " << "bad value ... " << t << "\n\n"; exit(1); } @@ -219,11 +213,11 @@ double Nx2ContingencyTable::event_count_by_thresh(double t) const { //////////////////////////////////////////////////////////////////////// -double Nx2ContingencyTable::nonevent_count_by_thresh(double t) const { +double Nx2ContingencyTable::nonevent_total_by_thresh(double t) const { int r = value_to_row(t); if(r < 0) { - mlog << Error << "\nNx2ContingencyTable::nonevent_count_by_thresh(double) -> " + mlog << Error << "\nNx2ContingencyTable::nonevent_total_by_thresh(double) -> " << "bad value ... " << t << "\n\n"; exit(1); } @@ -241,6 +235,8 @@ void Nx2ContingencyTable::set_event(int row, double value) { exit(1); } + // Number of pairs defined by set_n_pairs(int) + set_entry(row, nx2_event_column, value); return; @@ -256,6 +252,8 @@ void Nx2ContingencyTable::set_nonevent(int row, double value) { exit(1); } + // Number of pairs defined by set_n_pairs(int) + set_entry(row, nx2_nonevent_column, value); return; @@ -264,7 +262,7 @@ void Nx2ContingencyTable::set_nonevent(int row, double value) { //////////////////////////////////////////////////////////////////////// double Nx2ContingencyTable::baser() const { - return compute_proportion(event_col_total(), n()); + return compute_proportion(event_col_total(), total()); } //////////////////////////////////////////////////////////////////////// @@ -273,7 +271,7 @@ double Nx2ContingencyTable::baser_ci(double alpha, double &cl, double &cu) const { double v = baser(); - compute_proportion_ci(v, n(), alpha, 1.0, cl, cu); + compute_proportion_ci(v, Npairs, alpha, 1.0, cl, cu); return v; } @@ -285,27 +283,27 @@ double Nx2ContingencyTable::brier_score() const { if(E.empty()) return bad_data_double; double sum = 0.0; - double count; + double row_total; double yi; double t; // Terms for event for(int j=0; j 1 so that degf > 0 in the call to gsl_cdf_tdist_Pinv() @@ -337,8 +335,8 @@ double Nx2ContingencyTable::brier_ci_halfwidth(double alpha) const { for(int j=0; j 0) { + if(cts_info[m].cts.n_pairs() == 0) continue; + // Write out FHO + if(conf_info.vx_opt[i].output_flag[i_fho] != STATOutputType::None) { write_fho_row(shc, cts_info[m], conf_info.vx_opt[i].output_flag[i_fho], stat_at, i_stat_row, @@ -964,9 +965,7 @@ void process_scores() { } // Write out CTC - if(conf_info.vx_opt[i].output_flag[i_ctc] != STATOutputType::None && - cts_info[m].cts.n() > 0) { - + if(conf_info.vx_opt[i].output_flag[i_ctc] != STATOutputType::None) { write_ctc_row(shc, cts_info[m], conf_info.vx_opt[i].output_flag[i_ctc], stat_at, i_stat_row, @@ -974,9 +973,7 @@ void process_scores() { } // Write out CTS - if(conf_info.vx_opt[i].output_flag[i_cts] != STATOutputType::None && - cts_info[m].cts.n() > 0) { - + if(conf_info.vx_opt[i].output_flag[i_cts] != STATOutputType::None) { write_cts_row(shc, cts_info[m], conf_info.vx_opt[i].output_flag[i_cts], stat_at, i_stat_row, @@ -984,9 +981,7 @@ void process_scores() { } // Write out ECLV - if(conf_info.vx_opt[i].output_flag[i_eclv] != STATOutputType::None && - cts_info[m].cts.n() > 0) { - + if(conf_info.vx_opt[i].output_flag[i_eclv] != STATOutputType::None) { write_eclv_row(shc, cts_info[m], conf_info.vx_opt[i].eclv_points, conf_info.vx_opt[i].output_flag[i_eclv], stat_at, i_stat_row, @@ -1007,10 +1002,10 @@ void process_scores() { // Compute MCTS do_mcts(mcts_info, i, &pd); - // Write out MCTC - if(conf_info.vx_opt[i].output_flag[i_mctc] != STATOutputType::None && - mcts_info.cts.total() > 0) { + if(mcts_info.cts.n_pairs() == 0) continue; + // Write out MCTC + if(conf_info.vx_opt[i].output_flag[i_mctc] != STATOutputType::None) { write_mctc_row(shc, mcts_info, conf_info.vx_opt[i].output_flag[i_mctc], stat_at, i_stat_row, @@ -1018,9 +1013,7 @@ void process_scores() { } // Write out MCTS - if(conf_info.vx_opt[i].output_flag[i_mcts] != STATOutputType::None && - mcts_info.cts.total() > 0) { - + if(conf_info.vx_opt[i].output_flag[i_mcts] != STATOutputType::None) { write_mcts_row(shc, mcts_info, conf_info.vx_opt[i].output_flag[i_mcts], stat_at, i_stat_row, @@ -1713,7 +1706,7 @@ void process_scores() { for(n=0; n 0 - if(nbrcts_info[n].cts_info.cts.n() > 0) { + if(nbrcts_info[n].cts_info.cts.n_pairs() > 0) { // Write out NBRCTC if(conf_info.vx_opt[i].output_flag[i_nbrctc] != STATOutputType::None) { @@ -2481,7 +2474,7 @@ void do_pct(const GridStatVxOpt &vx_opt, const PairDataPoint *pd_ptr) { } // Compute the probabilistic counts and statistics - compute_pctinfo(pd, ( STATOutputType::None!=vx_opt.output_flag[i_pstd]), pct_info[j]); + compute_pctinfo(pd, (STATOutputType::None!=vx_opt.output_flag[i_pstd]), pct_info[j]); // Check for no matched pairs to process if(pd.n_obs == 0) continue; diff --git a/src/tools/core/grid_stat/grid_stat_conf_info.cc b/src/tools/core/grid_stat/grid_stat_conf_info.cc index 19c5a48e83..d334804850 100644 --- a/src/tools/core/grid_stat/grid_stat_conf_info.cc +++ b/src/tools/core/grid_stat/grid_stat_conf_info.cc @@ -228,6 +228,19 @@ void GridStatConfInfo::process_config(GrdFileType ftype, // Summarize output flags across all verification tasks process_flags(); + // FHO output is not compatible with grid weights + if(output_flag[i_fho] != STATOutputType::None && + grid_weight_flag != GridWeightType::None) { + + mlog << Warning << "\nGridStatConfInfo::process_config() -> " + << "Disabling FHO output that is not compatible with grid weighting. " + << "Set \"grid_weight_flag = NONE\" to write FHO output.\n\n"; + + // Disable FHO output + for(i=0; i 0) { + if(mcts_info.cts.n_pairs() == 0) continue; + // Write out MCTC + if(conf_info.vx_opt[i_vx].output_flag[i_mctc] != STATOutputType::None) { write_mctc_row(shc, mcts_info, conf_info.vx_opt[i_vx].output_flag[i_mctc], stat_at, i_stat_row, @@ -1212,9 +1212,7 @@ void process_scores() { } // Write out MCTS - if(conf_info.vx_opt[i_vx].output_flag[i_mcts] != STATOutputType::None && - mcts_info.cts.total() > 0) { - + if(conf_info.vx_opt[i_vx].output_flag[i_mcts] != STATOutputType::None) { write_mcts_row(shc, mcts_info, conf_info.vx_opt[i_vx].output_flag[i_mcts], stat_at, i_stat_row, diff --git a/src/tools/core/series_analysis/series_analysis.cc b/src/tools/core/series_analysis/series_analysis.cc index a817768c3a..fa2d4a8ef7 100644 --- a/src/tools/core/series_analysis/series_analysis.cc +++ b/src/tools/core/series_analysis/series_analysis.cc @@ -1499,8 +1499,12 @@ void read_aggr_mctc(int n, const MCTSInfo &mcts_info, // Get the n-th value double v = aggr_data[var_name].buf()[n]; + // Store the number of pairs + if(c == "TOTAL" && !is_bad_data(v)) { + aggr_mcts.cts.set_n_pairs(nint(v)); + } // Check the number of categories - if(c == "N_CAT" && !is_bad_data(v) && + else if(c == "N_CAT" && !is_bad_data(v) && aggr_mcts.cts.nrows() != nint(v)) { mlog << Error << "\nread_aggr_mctc() -> " << "the number of MCTC categories do not match (" @@ -1618,8 +1622,12 @@ void read_aggr_pct(int n, const PCTInfo &pct_info, // Get the n-th value double v = aggr_data[var_name].buf()[n]; + // Store the number of pairs + if(c == "TOTAL" && !is_bad_data(v)) { + aggr_pct.pct.set_n_pairs(nint(v)); + } // Check the number of thresholds - if(c == "N_THRESH" && !is_bad_data(v) && + else if(c == "N_THRESH" && !is_bad_data(v) && (aggr_pct.pct.nrows()+1) != nint(v)) { mlog << Error << "\nread_aggr_pct() -> " << "the number of PCT thresholds do not match (" diff --git a/src/tools/core/stat_analysis/aggr_stat_line.cc b/src/tools/core/stat_analysis/aggr_stat_line.cc index 6c0a7add52..3c6dcd3f22 100644 --- a/src/tools/core/stat_analysis/aggr_stat_line.cc +++ b/src/tools/core/stat_analysis/aggr_stat_line.cc @@ -589,12 +589,15 @@ void aggr_summary_lines(LineDataFile &f, STATAnalysisJob &job, int &n_in, int &n_out) { STATLine line; AggrSummaryInfo aggr; - ConcatString key, cs; - StringArray sa, req_stat, req_lty, req_col; + ConcatString cs; + StringArray sa; + StringArray req_stat; + StringArray req_lty; + StringArray req_col; STATLineType lty; NumArray empty_na; - int i, n_add; - double v, w; + double v; + double w; // // Objects for derived statistics @@ -607,7 +610,7 @@ void aggr_summary_lines(LineDataFile &f, STATAnalysisJob &job, // // Build list of requested line types and column names // - for(i=0; i::iterator it; // @@ -860,7 +862,7 @@ void aggr_ctc_lines(LineDataFile &f, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Add a new map entry, if necessary @@ -888,14 +890,7 @@ void aggr_ctc_lines(LineDataFile &f, STATAnalysisJob &job, // Increment counts in the existing map entry // else { - m[key].cts_info.cts.set_fy_oy(m[key].cts_info.cts.fy_oy() + - cur.cts.fy_oy()); - m[key].cts_info.cts.set_fy_on(m[key].cts_info.cts.fy_on() + - cur.cts.fy_on()); - m[key].cts_info.cts.set_fn_oy(m[key].cts_info.cts.fn_oy() + - cur.cts.fn_oy()); - m[key].cts_info.cts.set_fn_on(m[key].cts_info.cts.fn_on() + - cur.cts.fn_on()); + m[key].cts_info.cts += cur.cts; } // @@ -971,7 +966,8 @@ void aggr_ctc_lines(LineDataFile &f, STATAnalysisJob &job, // // Sort the valid times // - n = it->second.valid_ts.rank_array(n_ties); + int n_ties; + int n = it->second.valid_ts.rank_array(n_ties); if(n_ties > 0 || n != it->second.valid_ts.n()) { mlog << Error << "\naggr_ctc_lines() -> " @@ -1019,9 +1015,7 @@ void aggr_mctc_lines(LineDataFile &f, STATAnalysisJob &job, STATLine line; AggrMCTCInfo aggr; MCTSInfo cur; - ConcatString key; unixtime ut; - int i, k, n, n_ties; map::iterator it; // @@ -1056,7 +1050,7 @@ void aggr_mctc_lines(LineDataFile &f, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Add a new map entry, if necessary @@ -1093,8 +1087,8 @@ void aggr_mctc_lines(LineDataFile &f, STATAnalysisJob &job, // // Increment the counts // - for(i=0; isecond.valid_ts.rank_array(n_ties); + int n_ties; + int n = it->second.valid_ts.rank_array(n_ties); if(n_ties > 0 || n != it->second.valid_ts.n()) { mlog << Error << "\naggr_mctc_lines() -> " @@ -1199,9 +1194,7 @@ void aggr_pct_lines(LineDataFile &f, STATAnalysisJob &job, STATLine line; AggrPCTInfo aggr; PCTInfo cur; - ConcatString key; unixtime ut; - int i, n, oy, on, n_ties; map::iterator it; // @@ -1236,7 +1229,7 @@ void aggr_pct_lines(LineDataFile &f, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Add a new map entry, if necessary @@ -1256,45 +1249,8 @@ void aggr_pct_lines(LineDataFile &f, STATAnalysisJob &job, // Increment counts in the existing map entry // else { - - // - // The size of the contingency table must remain the same - // - if(m[key].pct_info.pct.nrows() != cur.pct.nrows()) { - mlog << Error << "\naggr_pct_lines() -> " - << "when aggregating PCT lines the number of " - << "thresholds must remain the same for all lines, " - << m[key].pct_info.pct.nrows() << " != " - << cur.pct.nrows() << "\n\n"; - throw 1; - } - - // - // Increment the counts - // - for(i=0; i " - << "when aggregating PCT lines the threshold " - << "values must remain the same for all lines, " - << m[key].pct_info.pct.threshold(i) << " != " - << cur.pct.threshold(i) << "\n\n"; - throw 1; - } - - oy = m[key].pct_info.pct.event_count_by_row(i); - on = m[key].pct_info.pct.nonevent_count_by_row(i); - - m[key].pct_info.pct.set_entry(i, nx2_event_column, - oy + cur.pct.event_count_by_row(i)); - m[key].pct_info.pct.set_entry(i, nx2_nonevent_column, - on + cur.pct.nonevent_count_by_row(i)); - } // end for i - } // end else + m[key].pct_info.pct += cur.pct; + } // // Keep track of scores for each time step for VIF @@ -1362,7 +1318,8 @@ void aggr_pct_lines(LineDataFile &f, STATAnalysisJob &job, // // Sort the valid times // - n = it->second.valid_ts.rank_array(n_ties); + int n_ties; + int n = it->second.valid_ts.rank_array(n_ties); if(n_ties > 0 || n != it->second.valid_ts.n()) { mlog << Error << "\naggr_pct_lines() -> " @@ -1399,9 +1356,7 @@ void aggr_psum_lines(LineDataFile &f, STATAnalysisJob &job, VL1L2Info cur_vl1l2; NBRCNTInfo cur_nbrcnt; CNTInfo cur_cnt; - ConcatString key; unixtime ut; - int n, n_ties; map::iterator it; // @@ -1467,7 +1422,7 @@ void aggr_psum_lines(LineDataFile &f, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Add a new map entry, if necessary @@ -1563,7 +1518,8 @@ void aggr_psum_lines(LineDataFile &f, STATAnalysisJob &job, // // Sort the valid times // - n = it->second.valid_ts.rank_array(n_ties); + int n_ties; + int n = it->second.valid_ts.rank_array(n_ties); if(n_ties > 0 || n != it->second.valid_ts.n()) { mlog << Error << "\naggr_psum_lines() -> " @@ -1599,7 +1555,6 @@ void aggr_grad_lines(LineDataFile &f, STATAnalysisJob &job, STATLine line; AggrGRADInfo aggr; GRADInfo cur; - ConcatString key; map::iterator it; // @@ -1630,7 +1585,7 @@ void aggr_grad_lines(LineDataFile &f, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Add a new map entry, if necessary @@ -1689,8 +1644,10 @@ void aggr_wind_lines(LineDataFile &f, STATAnalysisJob &job, STATLine line; AggrWindInfo aggr; VL1L2Info cur; - ConcatString key; - double uf, vf, uo, vo; + double uf; + double vf; + double uo; + double vo; // // Process the STAT lines @@ -1745,7 +1702,7 @@ void aggr_wind_lines(LineDataFile &f, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Add a new map entry, if necessary @@ -1795,14 +1752,7 @@ void aggr_mpr_wind_lines(LineDataFile &f, STATAnalysisJob &job, AggrWindInfo aggr; VL1L2Info v_info; MPRData cur; - ConcatString hdr, key; - double uf, uo, ufcmn, ufcsd, uocmn, uocsd; - double vf, vo, vfcmn, vfcsd, vocmn, vocsd; - double fcst_wind, obs_wind; - double fcmn_wind, fcsd_wind; - double ocmn_wind, ocsd_wind; - bool is_ugrd; - int i; + ConcatString hdr; map::iterator it; // @@ -1819,19 +1769,19 @@ void aggr_mpr_wind_lines(LineDataFile &f, STATAnalysisJob &job, job.dump_stat_line(line); parse_mpr_line(line, cur); - is_ugrd = (cur.fcst_var == ugrd_abbr_str); - uf = (is_ugrd ? cur.fcst : bad_data_double); - uo = (is_ugrd ? cur.obs : bad_data_double); - ufcmn = (is_ugrd ? cur.fcst_climo_mean : bad_data_double); - ufcsd = (is_ugrd ? cur.fcst_climo_stdev : bad_data_double); - uocmn = (is_ugrd ? cur.obs_climo_mean : bad_data_double); - uocsd = (is_ugrd ? cur.obs_climo_stdev : bad_data_double); - vf = (is_ugrd ? bad_data_double : cur.fcst); - vo = (is_ugrd ? bad_data_double : cur.obs); - vfcmn = (is_ugrd ? bad_data_double : cur.fcst_climo_mean); - vfcsd = (is_ugrd ? bad_data_double : cur.fcst_climo_stdev); - vocmn = (is_ugrd ? bad_data_double : cur.obs_climo_mean); - vocsd = (is_ugrd ? bad_data_double : cur.obs_climo_stdev); + bool is_ugrd = (cur.fcst_var == ugrd_abbr_str); + double uf = (is_ugrd ? cur.fcst : bad_data_double); + double uo = (is_ugrd ? cur.obs : bad_data_double); + double ufcmn = (is_ugrd ? cur.fcst_climo_mean : bad_data_double); + double ufcsd = (is_ugrd ? cur.fcst_climo_stdev : bad_data_double); + double uocmn = (is_ugrd ? cur.obs_climo_mean : bad_data_double); + double uocsd = (is_ugrd ? cur.obs_climo_stdev : bad_data_double); + double vf = (is_ugrd ? bad_data_double : cur.fcst); + double vo = (is_ugrd ? bad_data_double : cur.obs); + double vfcmn = (is_ugrd ? bad_data_double : cur.fcst_climo_mean); + double vfcsd = (is_ugrd ? bad_data_double : cur.fcst_climo_stdev); + double vocmn = (is_ugrd ? bad_data_double : cur.obs_climo_mean); + double vocsd = (is_ugrd ? bad_data_double : cur.obs_climo_stdev); // // Build header string for matching UGRD and VGRD lines @@ -1860,7 +1810,7 @@ void aggr_mpr_wind_lines(LineDataFile &f, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Add a new map entry, if necessary @@ -1907,6 +1857,7 @@ void aggr_mpr_wind_lines(LineDataFile &f, STATAnalysisJob &job, // // Add data for existing header entry // + int i; if(m[key].hdr_sa.has(hdr, i)) { // @@ -1989,7 +1940,7 @@ void aggr_mpr_wind_lines(LineDataFile &f, STATAnalysisJob &job, // // Loop over the pairs for the current map entry // - for(i=0; isecond.hdr_sa.n(); i++) { + for(int i=0; isecond.hdr_sa.n(); i++) { // // Check for missing UGRD data @@ -2020,18 +1971,18 @@ void aggr_mpr_wind_lines(LineDataFile &f, STATAnalysisJob &job, job.out_obs_wind_thresh.get_type() != thresh_na) { // Compute wind speeds - fcst_wind = convert_u_v_to_wind(it->second.pd_u.f_na[i], - it->second.pd_v.f_na[i]); - obs_wind = convert_u_v_to_wind(it->second.pd_u.o_na[i], - it->second.pd_v.o_na[i]); - fcmn_wind = convert_u_v_to_wind(it->second.pd_u.fcmn_na[i], - it->second.pd_v.fcmn_na[i]); - fcsd_wind = convert_u_v_to_wind(it->second.pd_u.fcsd_na[i], - it->second.pd_v.fcsd_na[i]); - ocmn_wind = convert_u_v_to_wind(it->second.pd_u.ocmn_na[i], - it->second.pd_v.ocmn_na[i]); - ocsd_wind = convert_u_v_to_wind(it->second.pd_u.ocsd_na[i], - it->second.pd_v.ocsd_na[i]); + double fcst_wind = convert_u_v_to_wind(it->second.pd_u.f_na[i], + it->second.pd_v.f_na[i]); + double obs_wind = convert_u_v_to_wind(it->second.pd_u.o_na[i], + it->second.pd_v.o_na[i]); + double fcmn_wind = convert_u_v_to_wind(it->second.pd_u.fcmn_na[i], + it->second.pd_v.fcmn_na[i]); + double fcsd_wind = convert_u_v_to_wind(it->second.pd_u.fcsd_na[i], + it->second.pd_v.fcsd_na[i]); + double ocmn_wind = convert_u_v_to_wind(it->second.pd_u.ocmn_na[i], + it->second.pd_v.ocmn_na[i]); + double ocsd_wind = convert_u_v_to_wind(it->second.pd_u.ocsd_na[i], + it->second.pd_v.ocsd_na[i]); // Store climo data ClimoPntInfo cpi(fcmn_wind, fcsd_wind, ocmn_wind, ocsd_wind); @@ -2094,8 +2045,12 @@ void aggr_mpr_wind_lines(LineDataFile &f, STATAnalysisJob &job, // ClimoPntInfo cpi; aggr.hdr_sa.add(it->second.hdr_sa[i]); + double uf; + double vf; convert_u_v_to_unit(it->second.pd_u.f_na[i], it->second.pd_v.f_na[i], uf, vf); + double uo; + double vo; convert_u_v_to_unit(it->second.pd_u.o_na[i], it->second.pd_v.o_na[i], uo, vo); aggr.pd_u.add_grid_pair(uf, uo, cpi, default_grid_weight); @@ -2119,7 +2074,6 @@ void aggr_mpr_lines(LineDataFile &f, STATAnalysisJob &job, STATLine line; AggrMPRInfo aggr; MPRData cur; - ConcatString key; // // Process the STAT lines @@ -2157,7 +2111,7 @@ void aggr_mpr_lines(LineDataFile &f, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Add a new map entry, if necessary @@ -2248,9 +2202,8 @@ void aggr_isc_lines(LineDataFile &ldf, STATAnalysisJob &job, STATLine line; AggrISCInfo aggr; ISCInfo cur; - ConcatString key; - int i, k, iscale; - double total, w, den, baser_fbias_sum; + int iscale; + double den; map::iterator it; // @@ -2289,7 +2242,7 @@ void aggr_isc_lines(LineDataFile &ldf, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Add a new map entry, if necessary @@ -2398,20 +2351,20 @@ void aggr_isc_lines(LineDataFile &ldf, STATAnalysisJob &job, // Get the sum of the totals, compute the weight, and sum the // weighted scores // - for(i=0; isecond.isc_info.n_scale+2; i++) { + for(int i=0; isecond.isc_info.n_scale+2; i++) { // Total number of points for this scale - total = it->second.total_na[i].sum(); + double total = it->second.total_na[i].sum(); // Initialize - baser_fbias_sum = 0.0; + double baser_fbias_sum = 0.0; // Loop through all scores for this scale - for(k=0; ksecond.total_na[i].n(); k++) { + for(int k=0; ksecond.total_na[i].n(); k++) { // Compute the weight for each score to be aggregated // based on the number of points it represents - w = it->second.total_na[i][k]/total; + double w = it->second.total_na[i][k]/total; // Sum scores for the binary fields if(i == 0) { @@ -2507,8 +2460,7 @@ void aggr_ecnt_lines(LineDataFile &f, STATAnalysisJob &job, STATLine line; AggrENSInfo aggr; ECNTData cur; - ConcatString key; - double crps_emp, crps_emp_fair, spread_md, crpscl_emp, crps_gaus, crpscl_gaus, v; + double v; map::iterator it; // @@ -2540,7 +2492,7 @@ void aggr_ecnt_lines(LineDataFile &f, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Add a new map entry, if necessary @@ -2626,12 +2578,10 @@ void aggr_ecnt_lines(LineDataFile &f, STATAnalysisJob &job, v = it->second.mse_oerr_na.wmean(it->second.ens_pd.wgt_na); it->second.ens_pd.rmse_oerr = (is_bad_data(v) ? bad_data_double : sqrt(v)); - crps_emp = it->second.ens_pd.crps_emp_na.wmean(it->second.ens_pd.wgt_na); - crps_emp_fair = it->second.ens_pd.crps_emp_fair_na.wmean(it->second.ens_pd.wgt_na); - spread_md = it->second.ens_pd.spread_md_na.wmean(it->second.ens_pd.wgt_na); - crpscl_emp = it->second.ens_pd.crpscl_emp_na.wmean(it->second.ens_pd.wgt_na); - crps_gaus = it->second.ens_pd.crps_gaus_na.wmean(it->second.ens_pd.wgt_na); - crpscl_gaus = it->second.ens_pd.crpscl_gaus_na.wmean(it->second.ens_pd.wgt_na); + double crps_emp = it->second.ens_pd.crps_emp_na.wmean(it->second.ens_pd.wgt_na); + double crpscl_emp = it->second.ens_pd.crpscl_emp_na.wmean(it->second.ens_pd.wgt_na); + double crps_gaus = it->second.ens_pd.crps_gaus_na.wmean(it->second.ens_pd.wgt_na); + double crpscl_gaus = it->second.ens_pd.crpscl_gaus_na.wmean(it->second.ens_pd.wgt_na); // Compute aggregated empirical CRPSS it->second.ens_pd.crpss_emp = @@ -2656,7 +2606,6 @@ void aggr_rps_lines(LineDataFile &f, STATAnalysisJob &job, STATLine line; AggrRPSInfo aggr; RPSInfo cur; - ConcatString key; map::iterator it; // @@ -2691,7 +2640,7 @@ void aggr_rps_lines(LineDataFile &f, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Add a new map entry, if necessary @@ -2750,8 +2699,6 @@ void aggr_rhist_lines(LineDataFile &f, STATAnalysisJob &job, STATLine line; AggrENSInfo aggr; RHISTData cur; - ConcatString key; - int i; map::iterator it; // @@ -2783,14 +2730,14 @@ void aggr_rhist_lines(LineDataFile &f, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Add a new map entry, if necessary // if(m.count(key) == 0) { aggr.clear(); - for(i=0; i::iterator it; // @@ -2873,7 +2818,7 @@ void aggr_phist_lines(LineDataFile &f, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Add a new map entry, if necessary @@ -2906,7 +2851,7 @@ void aggr_phist_lines(LineDataFile &f, STATAnalysisJob &job, // // Aggregate the probability integral transform histogram counts // - for(i=0; i::iterator it; // @@ -2964,7 +2907,7 @@ void aggr_relp_lines(LineDataFile &f, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Add a new map entry, if necessary @@ -2996,7 +2939,7 @@ void aggr_relp_lines(LineDataFile &f, STATAnalysisJob &job, // // Aggregate the RELP histogram counts // - for(i=0; i::iterator it; // @@ -3056,7 +2996,7 @@ void aggr_orank_lines(LineDataFile &f, STATAnalysisJob &job, // // Build the map key for the current line // - key = job.get_case_info(line); + ConcatString key(job.get_case_info(line)); // // Skip missing data @@ -3073,10 +3013,10 @@ void aggr_orank_lines(LineDataFile &f, STATAnalysisJob &job, aggr.ens_pd.obs_error_flag = !is_bad_data(cur.ens_mean_oerr); aggr.ens_pd.set_ens_size(cur.n_ens); aggr.ens_pd.extend(cur.total); - for(i=0; i thresh(n); - for(i=0; i&m) { << it->second.Info.cts.fn_on() << " correct negatives.\n"; // Increment the counts for the existing key - RIRWMap[it->first].Info.cts.set_fy_oy( - RIRWMap[it->first].Info.cts.fy_oy() + - it->second.Info.cts.fy_oy()); - RIRWMap[it->first].Info.cts.set_fy_on( - RIRWMap[it->first].Info.cts.fy_on() + - it->second.Info.cts.fy_on()); - RIRWMap[it->first].Info.cts.set_fn_oy( - RIRWMap[it->first].Info.cts.fn_oy() + - it->second.Info.cts.fn_oy()); - RIRWMap[it->first].Info.cts.set_fn_on( - RIRWMap[it->first].Info.cts.fn_on() + - it->second.Info.cts.fn_on()); + RIRWMap[it->first].Info.cts += it->second.Info.cts; RIRWMap[it->first].Hdr.add_uniq(it->second.Hdr); RIRWMap[it->first].AModel.add_uniq(it->second.AModel);