2025-09-10_16:41:59 [INFO] : __main__ : Starting main() 2025-09-10_16:41:59 [INFO] : analysis.StatisticsDatabase.mpas : Control Experiment: 2025-06-22-4wks 2025-09-10_16:41:59 [INFO] : analysis.StatisticsDatabase.mpas : ('Non-control Experiment(s): ', ['2025-06-29-4wks']) 2025-09-10_16:41:59 [INFO] : __main__ :  2025-09-10_16:41:59 [INFO] : __main__ : Analyzing StatsDB for mpas 2025-09-10_16:41:59 [INFO] : analysis.StatisticsDatabase.mpas : ===================================================== 2025-09-10_16:41:59 [INFO] : analysis.StatisticsDatabase.mpas : Construct pandas dataframe from static database files 2025-09-10_16:41:59 [INFO] : analysis.StatisticsDatabase.mpas : ===================================================== 2025-09-10_16:41:59 [INFO] : analysis.StatisticsDatabase.mpas : Reading intermediate statistics files 2025-09-10_16:41:59 [INFO] : analysis.StatisticsDatabase.mpas : with 128 out of 128 processors Generating CY-type figures control: 2025-06-22-4wks experiments: ['2025-06-22-4wks:jwittig_3denvar-60-iter_O120km_VarBC.2025-06-22-4wks_cron', '2025-06-29-4wks:jwittig_3denvar-60-iter_O120km_VarBC.2025-06-29-4wks_cron'] model forecast None 2018-05-12 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-15 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-15 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-15 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-15 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-16 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-16 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-16 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-16 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-17 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-17 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-17 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-17 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-18 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-18 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-18 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-18 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-19 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-19 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-19 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-19 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-20 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-20 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-20 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-20 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-21 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-21 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-21 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-21 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-22 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-22 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-22 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-22 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-23 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-23 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-23 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-23 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-24 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-24 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-24 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-24 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-25 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-25 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-25 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-25 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-26 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-26 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-26 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-26 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-27 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-27 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-27 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-27 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-28 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-28 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-28 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-28 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-29 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-29 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-29 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-29 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-30 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-30 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-30 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-04-30 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-01 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-01 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-01 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-01 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-02 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-02 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-02 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-02 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-03 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-03 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-03 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-03 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-04 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-04 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-04 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-04 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-05 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-05 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-05 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-05 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-06 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-06 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-06 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-06 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-07 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-07 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-07 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-07 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-08 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-08 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-08 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-08 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-09 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-09 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-09 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-09 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-10 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-10 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-10 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-10 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-11 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-11 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-11 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-11 18:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-12 00:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-12 06:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-12 12:00:00 2025-09-10_16:42:00 [INFO] : analysis.StatisticsDatabase.mpas : Working on cycle time 2018-05-12 18:00:00 2025-09-10_16:42:26 [INFO] : analysis.StatisticsDatabase.mpas : Concatenating statistics sub-dictionaries from multiple processors 2025-09-10_16:42:27 [INFO] : analysis.StatisticsDatabase.mpas : Constructing a dataframe from statistics dictionary 2025-09-10_16:42:32 [INFO] : analysis.StatisticsDatabase.mpas : Sorting the dataframe index 2025-09-10_16:42:34 [INFO] : analysis.StatisticsDatabase.mpas : Extracting index values 2025-09-10_16:42:48 [INFO] : analysis.StatisticsDatabase.mpas : availableDiagnostics: ['mmgfsan'] 2025-09-10_16:42:48 [INFO] : analysis.Analyses.mpas : Analyses Constructed 2025-09-10_16:42:48 [INFO] : analysis.Analyses.mpas : Entering Analyses.analyze() 2025-09-10_16:42:48 [INFO] : analysis.AnalysisBase.mpas.BinValAxisProfile : analyze() /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, :] 2025-09-10_16:42:50 [INFO] : analysis.AnalysisBase.mpas.BinValAxisProfile : mmgfsan, lat, 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, :] 2025-09-10_16:42:58 [INFO] : analysis.AnalysisBase.mpas.BinValAxisProfile : mmgfsan, ModLev, ITCZ /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] 2025-09-10_16:43:01 [INFO] : analysis.AnalysisBase.mpas.BinValAxisProfile : mmgfsan, ModLev, NTro /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] 2025-09-10_16:43:04 [INFO] : analysis.AnalysisBase.mpas.BinValAxisProfile : mmgfsan, ModLev, NXTro /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] 2025-09-10_16:43:07 [INFO] : analysis.AnalysisBase.mpas.BinValAxisProfile : mmgfsan, ModLev, STro /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] 2025-09-10_16:43:10 [INFO] : analysis.AnalysisBase.mpas.BinValAxisProfile : mmgfsan, ModLev, SXTro /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] 2025-09-10_16:43:13 [INFO] : analysis.AnalysisBase.mpas.BinValAxisProfile : mmgfsan, ModLev, Tro /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] 2025-09-10_16:43:16 [INFO] : analysis.AnalysisBase.mpas.BinValAxisProfile : mmgfsan, ModLev, abi_g16 /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] 2025-09-10_16:43:19 [INFO] : analysis.AnalysisBase.mpas.BinValAxisProfile : mmgfsan, ModLev, ahi_himawari8 /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] 2025-09-10_16:43:22 [INFO] : analysis.AnalysisBase.mpas.BinValAxisProfile : mmgfsan, ModLev, 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/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, :] 2025-09-10_16:43:25 [INFO] : analysis.Analyses.mpas : Exiting Analyses.analyze() 2025-09-10_16:43:25 [INFO] : __main__ : Finished main() successfully