CLN: Dynamically import matplotlib and pyplot

Normally dynamically importing modules is fairly straight forward: you
can just import them in the function or method they are used in.
However, by default matplotlib will look for display settings on the
machine it's being run on and compute cluster nodes do not have this
set.

The suggested solution to this is to import matplotlib before anything
else is imported, and set its backend to `use("Agg")` which doesn't look
for display information. xtgeo implemented this by importing matplotlib
in the root __init__, and it generally complicates the dynamic loading
situation.

This solution tries to ensure that using the Agg backend will still be
triggered if it xtgeo believes it is in batch mode and wraps a getting
around `sys.modules` after importing it. It also tries not to repeat the
import logic if it is already imported.

src/xtgeo/__init__.py

@@ -50,34 +50,23 @@ def _xprint(msg):
 except Exception:
     ROXAR = False
 
-
-# to avoid problems in batch runs when no DISPLAY is set:
-_xprint("Import matplotlib etc...")
 if not ROXAR:
-    import matplotlib as mplib
-
-    display = os.environ.get("DISPLAY", "")
-    host1 = os.environ.get("HOSTNAME", "")
-    host2 = os.environ.get("HOST", "")
-    dhost = host1 + host2 + display
+    _display = os.environ.get("DISPLAY", "")
+    _hostname = os.environ.get("HOSTNAME", "")
+    _host = os.environ.get("HOST", "")
 
-    ertbool = "LSB_JOBID" in os.environ
+    _dhost = _hostname + _host + _display
+    _lsf_job = "LSB_JOBID" in os.environ
 
-    if display == "" or "grid" in dhost or "lgc" in dhost or ertbool:
+    if _display == "" or "grid" in _dhost or "lgc" in _dhost or _lsf_job:
         _xprint("")
         _xprint("=" * 79)
-
         _xprint(
             "XTGeo info: No display found or a batch (e.g. ERT) server. "
             "Using non-interactive Agg backend for matplotlib"
         )
-        mplib.use("Agg")
         _xprint("=" * 79)
-
-#
-# Order matters!
-#
-_xprint("Import matplotlib etc...DONE")
+        os.environ["MPLBACKEND"] = "Agg"
 
 from xtgeo._cxtgeo import XTGeoCLibError
 from xtgeo.common import XTGeoDialog

src/xtgeo/plot/__init__.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """The XTGeo plot package"""
 
 
@@ -7,5 +6,3 @@
 # flake8: noqa
 from xtgeo.plot.xsection import XSection
 from xtgeo.plot.xtmap import Map
-
-# from ._colortables import random, random40, xtgeocolors, colorsfromfile

src/xtgeo/plot/baseplot.py

@@ -1,7 +1,4 @@
 """The baseplot module."""
-import matplotlib as mpl
-import matplotlib.pyplot as plt
-from matplotlib.colors import LinearSegmentedColormap
 from packaging.version import parse as versionparse
 
 from xtgeo.common import XTGeoDialog, null_logger
@@ -14,7 +11,11 @@
 
 def _get_colormap(name):
     """For matplotlib compatibility."""
+    import matplotlib as mpl
+
     if versionparse(mpl.__version__) < versionparse("3.6"):
+        import matplotlib.plt as plt
+
         return plt.cm.get_cmap(name)
     else:
         return mpl.colormaps[name]
@@ -56,7 +57,9 @@ def colormap(self):
 
     @colormap.setter
     def colormap(self, cmap):
-        if isinstance(cmap, LinearSegmentedColormap):
+        import matplotlib as mpl
+
+        if isinstance(cmap, mpl.colors.LinearSegmentedColormap):
             self._colormap = cmap
         elif isinstance(cmap, str):
             logger.info("Definition of a colormap from string name: %s", cmap)
@@ -85,6 +88,9 @@ def define_any_colormap(cfile, colorlist=None):
                 from 0 index. Default is just keep the linear sequence as is.
 
         """
+        import matplotlib as mpl
+        import matplotlib.pyplot as plt
+
         valid_maps = sorted(m for m in plt.cm.datad)
 
         logger.info("Valid color maps: %s", valid_maps)
@@ -99,21 +105,37 @@ def define_any_colormap(cfile, colorlist=None):
 
         elif cfile == "xtgeo":
             colors = _ctable.xtgeocolors()
-            cmap = LinearSegmentedColormap.from_list(cfile, colors, N=len(colors))
+            cmap = mpl.colors.LinearSegmentedColormap.from_list(
+                cfile,
+                colors,
+                N=len(colors),
+            )
             cmap.name = "xtgeo"
         elif cfile == "random40":
             colors = _ctable.random40()
-            cmap = LinearSegmentedColormap.from_list(cfile, colors, N=len(colors))
+            cmap = mpl.colors.LinearSegmentedColormap.from_list(
+                cfile,
+                colors,
+                N=len(colors),
+            )
             cmap.name = "random40"
 
         elif cfile == "randomc":
             colors = _ctable.randomc(256)
-            cmap = LinearSegmentedColormap.from_list(cfile, colors, N=len(colors))
+            cmap = mpl.colors.LinearSegmentedColormap.from_list(
+                cfile,
+                colors,
+                N=len(colors),
+            )
             cmap.name = "randomc"
 
         elif isinstance(cfile, str) and "rms" in cfile:
             colors = _ctable.colorsfromfile(cfile)
-            cmap = LinearSegmentedColormap.from_list("rms", colors, N=len(colors))
+            cmap = mpl.colors.LinearSegmentedColormap.from_list(
+                "rms",
+                colors,
+                N=len(colors),
+            )
             cmap.name = cfile
         elif cfile in valid_maps:
             cmap = _get_colormap(cfile)
@@ -138,7 +160,11 @@ def define_any_colormap(cfile, colorlist=None):
                     logger.warning("Color list out of range")
                     ctable.append(colors[0])
 
-            cmap = LinearSegmentedColormap.from_list(ctable, colors, N=len(colors))
+            cmap = mpl.colors.LinearSegmentedColormap.from_list(
+                ctable,
+                colors,
+                N=len(colors),
+            )
             cmap.name = "user"
 
         return cmap
@@ -182,7 +208,8 @@ def canvas(self, title=None, subtitle=None, infotext=None, figscaling=1.0):
 
 
         """
-        # self._fig, (ax1, ax2) = plt.subplots(2, figsize=(11.69, 8.27))
+        import matplotlib.pyplot as plt
+
         self._fig, self._ax = plt.subplots(
             figsize=(11.69 * figscaling, 8.27 * figscaling)
         )
@@ -204,6 +231,8 @@ def show(self):
             self._fig.tight_layout()
 
         if self._showok:
+            import matplotlib.pyplot as plt
+
             logger.info("Calling plt show method...")
             plt.show()
             return True
@@ -218,6 +247,8 @@ def close(self):
         After close is called, no more operations can be performed on the plot.
 
         """
+        import matplotlib.pyplot as plt
+
         for fig in self._allfigs:
             plt.close(fig)
 
@@ -247,6 +278,8 @@ def savefig(self, filename, fformat="png", last=True, **kwargs):
             self._fig.tight_layout()
 
         if self._showok:
+            import matplotlib.pyplot as plt
+
             plt.savefig(filename, format=fformat, **kwargs)
             if last:
                 self.close()

src/xtgeo/plot/grid3d_slice.py

@@ -1,10 +1,5 @@
 """Module for 3D Grid slice plots, using matplotlib."""
 
-
-import matplotlib.pyplot as plt
-from matplotlib.collections import PatchCollection
-from matplotlib.patches import Polygon
-
 from xtgeo.common import null_logger
 from xtgeo.plot.baseplot import BasePlot
 
@@ -105,51 +100,6 @@ def plot_gridslice(
         else:
             self._plot_layer()
 
-    # def _plot_row(self):
-
-    #     geomlist = self._geomlist
-
-    #     if self._window is None:
-    #         xmin = geomlist[3] - 0.05 * (abs(geomlist[4] - geomlist[3]))
-    #         xmax = geomlist[4] + 0.05 * (abs(geomlist[4] - geomlist[3]))
-    #         zmin = geomlist[7] - 0.05 * (abs(geomlist[8] - geomlist[7]))
-    #         zmax = geomlist[8] + 0.05 * (abs(geomlist[8] - geomlist[7]))
-    #     else:
-    #         xmin, xmax, zmin, zmax = self._window
-
-    #     # now some numpy operations, numbering is intended
-    #     clist = self._clist
-    #     xz0 = np.column_stack((clist[0].values1d, clist[2].values1d))
-    #     xz1 = np.column_stack((clist[3].values1d, clist[5].values1d))
-    #     xz2 = np.column_stack((clist[15].values1d, clist[17].values1d))
-    #     xz3 = np.column_stack((clist[12].values1d, clist[14].values1d))
-
-    #     xyc = np.column_stack((xz0, xz1, xz2, xz3))
-    #     xyc = xyc.reshape(self._grid.nlay, self._grid.ncol * self._grid.nrow, 4, 2)
-
-    #     patches = []
-
-    #     for pos in range(self._grid.nrow * self._grid.nlay):
-    #         nppol = xyc[self._index - 1, pos, :, :]
-    #         if nppol.mean() > 0.0:
-    #             polygon = Polygon(nppol, True)
-    #             patches.append(polygon)
-
-    #     black = (0, 0, 0, 1)
-    #     patchcoll = PatchCollection(patches, edgecolors=(black,), cmap=self.colormap)
-
-    #     # patchcoll.set_array(np.array(pvalues))
-
-    #     # patchcoll.set_clim([minvalue, maxvalue])
-
-    #     im = self._ax.add_collection(patchcoll)
-    #     self._ax.set_xlim((xmin, xmax))
-    #     self._ax.set_ylim((zmin, zmax))
-    #     self._ax.invert_yaxis()
-    #     self._fig.colorbar(im)
-
-    #     # plt.gca().set_aspect("equal", adjustable="box")
-
     def _plot_layer(self):
         xyc, ibn = self._grid.get_layer_slice(self._index, activeonly=self._active)
 
@@ -171,13 +121,15 @@ def _plot_layer(self):
 
         patches = []
 
+        import matplotlib as mpl
+
         for pos in range(len(ibn)):
             nppol = xyc[pos, :, :]
             if nppol.mean() > 0.0:
-                polygon = Polygon(nppol)
+                polygon = mpl.patches.Polygon(nppol)
                 patches.append(polygon)
 
-        patchcoll = PatchCollection(
+        patchcoll = mpl.collections.PatchCollection(
             patches, edgecolors=(self._linecolor,), cmap=self.colormap
         )
 
@@ -203,4 +155,6 @@ def _plot_layer(self):
         self._ax.set_ylim((ymin, ymax))
         self._fig.colorbar(im)
 
+        import matplotlib.pyplot as plt
+
         plt.gca().set_aspect("equal", adjustable="box")

src/xtgeo/plot/xsection.py

@@ -5,12 +5,9 @@
 import warnings
 from collections import OrderedDict
 
-import matplotlib.pyplot as plt
 import numpy as np
 import numpy.ma as ma
 import pandas as pd
-from matplotlib import collections as mc
-from matplotlib.lines import Line2D
 from scipy.ndimage import gaussian_filter
 
 from xtgeo.common import XTGeoDialog, null_logger
@@ -265,6 +262,7 @@ def canvas(self, title=None, subtitle=None, infotext=None, figscaling=1.0):
 
         """
         # overriding the base class canvas
+        import matplotlib.pyplot as plt
 
         plt.rcParams["axes.xmargin"] = 0  # fill the plot margins
 
@@ -445,6 +443,8 @@ def set_xaxis_md(self, gridlines=False):
         md_start_round = int(math.floor(md_start / 100.0)) * 100
         md_start_delta = md_start - md_start_round
 
+        import matplotlib.pyplot as plt
+
         auto_ticks = plt.xticks()
         auto_ticks_delta = auto_ticks[0][1] - auto_ticks[0][0]
 
@@ -566,7 +566,9 @@ def _plot_well_zlog(self, df, ax, bba, zonelogname, logwidth=4, legend=False):
             df, idx_zshift, ctable, zonelogname, fillnavalue
         )
 
-        lc = mc.LineCollection(
+        import matplotlib as mpl
+
+        lc = mpl.collections.LineCollection(
             segments, colors=segments_colors, linewidth=logwidth, zorder=202
         )
 
@@ -610,7 +612,9 @@ def _plot_well_faclog(self, df, ax, bba, facieslogname, logwidth=9, legend=True)
             df, idx, ctable, facieslogname, fillnavalue
         )
 
-        lc = mc.LineCollection(
+        import matplotlib as mpl
+
+        lc = mpl.collections.LineCollection(
             segments, colors=segments_colors, linewidth=logwidth, zorder=201
         )
 
@@ -656,7 +660,9 @@ def _plot_well_perflog(self, df, ax, bba, perflogname, logwidth=12, legend=True)
             df, idx, ctable, perflogname, fillnavalue
         )
 
-        lc = mc.LineCollection(
+        import matplotlib as mpl
+
+        lc = mpl.collections.LineCollection(
             segments, colors=segments_colors, linewidth=logwidth, zorder=200
         )
 
@@ -769,9 +775,11 @@ def _drawproxylegend(self, ax, bba, items, title=None):
         proxies = []
         labels = []
 
+        import matplotlib as mpl
+
         for item in items:
             color = items[item]
-            proxies.append(Line2D([0, 1], [0, 1], color=color, linewidth=5))
+            proxies.append(mpl.lines.Line2D([0, 1], [0, 1], color=color, linewidth=5))
             labels.append(item)
 
         ax.legend(