Graphics Maintenance - compatible with latest npl

graphics/AnalyzeStats.py

@@ -12,6 +12,7 @@
 import logsetup
 import multiprocessing as mp
 import os
+import pandas as pd
 
 from analysis.Analyses import Analyses
 import analysis.StatisticsDatabase as sdb
@@ -66,12 +67,8 @@ def main():
       # where each pair is colon delimited, e.g. exp1:long_name_exp1,exp2:long_name_exp2
       exps = args.experiments.split(',')
 
-    firstCycle = None
-    lastCycle = None
-    if args.firstCycle:
-      firstCycle= dt.datetime.strptime(args.firstCycle, '%Y%m%dT%H')
-    if args.lastCycle:
-      lastCycle= dt.datetime.strptime(args.lastCycle, '%Y%m%dT%H')
+    firstCycle = pd.to_datetime(args.firstCycle)
+    lastCycle = pd.to_datetime(args.lastCycle)
 
     anconf.adjust_experiments(args.controlExperiment, exps, args.verifySpace, args.verifyType, firstCycle, lastCycle)
 

graphics/analysis/AnalysisBase.py

@@ -10,7 +10,6 @@
 import logging
 import numpy as np
 from pathlib import Path
-import plot_utils as pu
 import os
 import var_utils as vu
 import modelsp_utils as mu
@@ -239,22 +238,26 @@ def initLimits(dmin0=np.NaN, dmax0=np.NaN, d=None):
         dmax = np.nanmax(d)
       return dmin, dmax
 
+
     def oneHundredCenteredLimiter(self, dmin0=np.NaN, dmax0=np.NaN, d=None):
-      dmin, dmax = self.initLimits(dmin0, dmax0, d)
+        dmin, dmax = self.initLimits(dmin0, dmax0, d)
 
-      # update dmax, conditional on dmin
-      dmax = np.nanmax([dmax, 100.0/(dmin/100.0)])
+        # Clamp dmin to its "Safety Envelope" (66.7% to 98.0%)
+        # This prevents division by zero and keeps the plot from being too squashed or too wide.
+        dmin = np.nanmax([dmin, 66.7])
+        dmin = np.nanmin([dmin, 98.0])
 
-      # set absolute min/max in case there are large outliers
-      #if np.isfinite(dmin):
-      dmin = np.nanmax([dmin, 66.7])
-      dmin = np.nanmin([dmin, 98.0])
+        # Clamp dmax to its "Safety Envelope" (102.0% to 150.0%)
+        dmax = np.nanmax([dmax, 102.0])
+        dmax = np.nanmin([dmax, 150.0])
 
-      #if np.isfinite(dmax):
-      dmax = np.nanmin([dmax, 150.0])
-      dmax = np.nanmax([dmax, 102.0])
+        # Symmetry Check
+        # Since dmin is at least 66.7, 100/(66.7/100) is ~150.
+        # This will never exceed our 150.0 ceiling.
+        dmax = np.nanmax([dmax, 100.0 / (dmin / 100.0)])
+
+        return dmin, dmax
 
-      return dmin, dmax
 
     def zeroCenteredPercentDifference(self,
       experiment,
@@ -292,23 +295,24 @@ def zeroCenteredLabeler(self):
       #return '100 x (EXP-'+self.cntrlExpName+') / \n'+self.cntrlExpName+''
       #return '100 x (EXP-CONTROL)/\nCONTROL'
 
+
     def zeroCenteredLimiter(self, dmin0=np.NaN, dmax0=np.NaN, d=None):
-      dmin, dmax = self.initLimits(dmin0, dmax0, d)
+        dmin, dmax = self.initLimits(dmin0, dmax0, d)
 
-      # update dmax, conditional on dmin
-      #dmax = np.nanmax([dmax, 100.0/((100.0+dmin)/100.0) - 100.0])
-      dmax = np.nanmax([dmax, 1.0/dmin])
+        # Ensures we always see at least -2.0 to +2.0
+        dmin = np.nanmin([dmin, -2.0])
+        dmax = np.nanmax([dmax, 2.0])
 
-      # set absolute min/max in case there are large outliers
-      #if np.isfinite(dmin):
-      dmin = np.nanmax([dmin, -33.3])
-      dmin = np.nanmin([dmin, -2.0])
+        # Prevents one bad observation from squishing the whole plot
+        dmin = np.nanmax([dmin, -33.3])
+        dmax = np.nanmin([dmax, 50.0])
 
-      #if np.isfinite(dmax):
-      dmax = np.nanmin([dmax, 50.0])
-      dmax = np.nanmax([dmax, 2.0])
+        # Symmetry (Optional/Legacy logic)
+        # With dmin clamped to -2.0, this maxes at 0.5.
+        # It won't override the 2.0 floor, but it's safe to keep.
+        dmax = np.nanmax([dmax, 1.0 / np.abs(dmin)])
 
-      return dmin, dmax
+        return dmin, dmax
 
 
     def binMethodFile(self, binMethod, before = True):
@@ -357,7 +361,7 @@ def statPlotAttributes(self, diagnosticGroup, statName,
         truncateDiagnostics = ommDiagnostics+mmoDiagnostics
         diagnosticGroup_ = diagnosticGroup
         #for diag in truncateDiagnostics:
-        #    if pu.prepends(diag, diagnosticGroup_) or pu.postpends(diag, diagnosticGroup_):
+        #    if diagnosticGroup_.startswith(diag) or diagnosticGroup_.endswith(diag):
         #        diagnosticGroup_ = diag
 
         allDiagnosticNames_ = deepcopy(allDiagnosticNames)
@@ -395,18 +399,18 @@ def statPlotAttributes(self, diagnosticGroup, statName,
         centralValue = None
 
         #for diag in truncateDiagnostics:
-        #    if pu.prepends(diag, cntrlDiagnosticName) or pu.postpends(diag, cntrlDiagnosticName):
+        #    if cntrlDiagnosticName.startswith(diag) or cntrlDiagnosticName.endswith(diag):
         #        cntrlDiagnosticName = diag
         #    for idiag, adiag in enumerate(allDiagnosticNames_):
-        #        if pu.prepends(diag, adiag) or pu.postpends(diag, adiag):
+        #        if adiag.startswith(diag) or adiag.endswith(diag):
         #            allDiagnosticNames_[idiag] = diag
 
         oneCenteredRatioDiagPrefixes = ['CRx', 'CRy', 'InnovationRatio', 'SRx', 'SRy', 'OENI']
         allOneCenteredDiagnostics = statName not in ['Count', 'Mean', 'Skew', 'ExcessKurtosis']
         for diag in allDiagnosticNames_:
             oneCentered = False
             for diagPrefix in oneCenteredRatioDiagPrefixes:
-                oneCentered = oneCentered or pu.prepends(diagPrefix, diag)
+                oneCentered = oneCentered or diag.startswith(diagPrefix)
             allOneCenteredDiagnostics = allOneCenteredDiagnostics and oneCentered
         if allOneCenteredDiagnostics:
             centralValue = 1.
@@ -419,7 +423,7 @@ def statPlotAttributes(self, diagnosticGroup, statName,
         # for diag in allDiagnosticNames_:
         #     logScaled = False
         #     for diagPrefix in logScaledDiagPrefixes:
-        #         logScaled = logScaled or pu.prepends(diagPrefix, diag)
+        #         logScaled = logScaled or diag.startswith(diagPrefix)
         #     allLogScaledDiagnostics = allLogScaledDiagnostics and logScaled
         # if allLogScaledDiagnostics:
         #     logScale = True and not isDifferencePlot

graphics/analysis/BinValAxisStatsComposite.py

@@ -132,9 +132,9 @@ def analyze_(self, workers = None):
                           varMapLoc.append((varName, varLabel))
 
                 nsubplots = nVarsLoc
-                nxplots = np.int(np.ceil(np.sqrt(nsubplots)))
+                nxplots = int(np.ceil(np.sqrt(nsubplots)))
                 while nsubplots%nxplots > 0 and nsubplots%nxplots / nxplots <= 0.5: nxplots += 1
-                nyplots = np.int(np.ceil(np.true_divide(nsubplots, nxplots)))
+                nyplots = int(np.ceil(np.true_divide(nsubplots, nxplots)))
 
                 ptLoc = {}