2025-07-29_02:05:34 [INFO] : __main__ : Starting main()
2025-07-29_02:05:34 [INFO] : analysis.StatisticsDatabase.mpas : Control Experiment: 2025-07-27
2025-07-29_02:05:34 [INFO] : analysis.StatisticsDatabase.mpas : ('Non-control Experiment(s): ', ['2025-07-28'])
2025-07-29_02:05:34 [INFO] : __main__ : 
2025-07-29_02:05:34 [INFO] : __main__ : Analyzing StatsDB for mpas
2025-07-29_02:05:34 [INFO] : analysis.StatisticsDatabase.mpas : =====================================================
2025-07-29_02:05:34 [INFO] : analysis.StatisticsDatabase.mpas : Construct pandas dataframe from static database files
2025-07-29_02:05:34 [INFO] : analysis.StatisticsDatabase.mpas : =====================================================
2025-07-29_02:05:34 [INFO] : analysis.StatisticsDatabase.mpas : Reading intermediate statistics files
2025-07-29_02:05:34 [INFO] : analysis.StatisticsDatabase.mpas : with 128 out of 128 processors
Generating CY-type figures
control: 2025-07-27
experiments: ['2025-07-27:jwittig_3denvar-60-iter_O120km_VarBC.2025-07-27_cron', '2025-07-28:jwittig_3denvar-60-iter_O120km_VarBC.2025-07-28_cron']
model forecast None 2018-04-21 18:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-15 00:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-15 06:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-15 12:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-15 18:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-16 00:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-16 06:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-16 12:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-16 18:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-17 00:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-17 06:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-17 12:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-17 18:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-18 00:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-18 06:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-18 12:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-18 18:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-19 00:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-19 06:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-19 12:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-19 18:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-20 00:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-20 06:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-20 12:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-20 18:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-21 00:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-21 06:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-21 12:00:00
2025-07-29_02:05:35 [INFO] : analysis.StatisticsDatabase.mpas :   Working on cycle time 2018-04-21 18:00:00
2025-07-29_02:05:48 [INFO] : analysis.StatisticsDatabase.mpas : Concatenating statistics sub-dictionaries from multiple processors
2025-07-29_02:05:48 [INFO] : analysis.StatisticsDatabase.mpas : Constructing a dataframe from statistics dictionary
2025-07-29_02:05:50 [INFO] : analysis.StatisticsDatabase.mpas : Sorting the dataframe index
2025-07-29_02:05:50 [INFO] : analysis.StatisticsDatabase.mpas : Extracting index values
2025-07-29_02:05:53 [INFO] : analysis.StatisticsDatabase.mpas : availableDiagnostics: ['mmgfsan']
2025-07-29_02:05:53 [INFO] : analysis.Analyses.mpas : Analyses Constructed
2025-07-29_02:05:53 [INFO] : analysis.Analyses.mpas : Entering Analyses.analyze()
2025-07-29_02:05:53 [INFO] : analysis.AnalysisBase.mpas.BinValAxes2D : analyze()
2025-07-29_02:05:54 [INFO] : analysis.AnalysisBase.mpas.BinValAxes2D : mmgfsan, ModelLonLat2D, identity
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/StatisticsDatabase.py:459: FutureWarning: The behavior of indexing on a MultiIndex with a nested sequence of labels is deprecated and will change in a future version. `series.loc[label, sequence]` will raise if any members of 'sequence' or not present in the index's second level. To retain the old behavior, use `series.index.isin(sequence, level=1)`
  return self.df.loc[Loc, :]
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/StatisticsDatabase.py:459: FutureWarning: The behavior of indexing on a MultiIndex with a nested sequence of labels is deprecated and will change in a future version. `series.loc[label, sequence]` will raise if any members of 'sequence' or not present in the index's second level. To retain the old behavior, use `series.index.isin(sequence, level=1)`
  return self.df.loc[Loc, :]
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/StatisticsDatabase.py:461: FutureWarning: The behavior of indexing on a MultiIndex with a nested sequence of labels is deprecated and will change in a future version. `series.loc[label, sequence]` will raise if any members of 'sequence' or not present in the index's second level. To retain the old behavior, use `series.index.isin(sequence, level=1)`
  return self.df.loc[Loc, var]
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
2025-07-29_02:07:30 [INFO] : analysis.AnalysisBase.mpas.BinValAxes2D : mmgfsan, ModelLatLev2D, identity
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/StatisticsDatabase.py:459: FutureWarning: The behavior of indexing on a MultiIndex with a nested sequence of labels is deprecated and will change in a future version. `series.loc[label, sequence]` will raise if any members of 'sequence' or not present in the index's second level. To retain the old behavior, use `series.index.isin(sequence, level=1)`
  return self.df.loc[Loc, :]
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/StatisticsDatabase.py:459: FutureWarning: The behavior of indexing on a MultiIndex with a nested sequence of labels is deprecated and will change in a future version. `series.loc[label, sequence]` will raise if any members of 'sequence' or not present in the index's second level. To retain the old behavior, use `series.index.isin(sequence, level=1)`
  return self.df.loc[Loc, :]
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/StatisticsDatabase.py:461: FutureWarning: The behavior of indexing on a MultiIndex with a nested sequence of labels is deprecated and will change in a future version. `series.loc[label, sequence]` will raise if any members of 'sequence' or not present in the index's second level. To retain the old behavior, use `series.index.isin(sequence, level=1)`
  return self.df.loc[Loc, var]
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/StatisticsDatabase.py:461: FutureWarning: The behavior of indexing on a MultiIndex with a nested sequence of labels is deprecated and will change in a future version. `series.loc[label, sequence]` will raise if any members of 'sequence' or not present in the index's second level. To retain the old behavior, use `series.index.isin(sequence, level=1)`
  return self.df.loc[Loc, var]
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/AnalysisBase.py:296: RuntimeWarning: divide by zero encountered in double_scalars
  dmax = np.nanmax([dmax, 1.0/dmin])
/glade/derecho/scratch/jwittig/repos-s/mpas-jedi-cron/graphics/analysis/StatisticsDatabase.py:459: FutureWarning: The behavior of indexing on a MultiIndex with a nested sequence of labels is deprecated and will change in a future version. `series.loc[label, sequence]` will raise if any members of 'sequence' or not present in the index's second level. To retain the old behavior, use `series.index.isin(sequence, level=1)`
  return self.df.loc[Loc, :]
2025-07-29_02:07:41 [INFO] : analysis.Analyses.mpas : Exiting Analyses.analyze()
2025-07-29_02:07:41 [INFO] : __main__ : Finished main() successfully