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####
#### MIT License
####
#### ParaMonte: plain powerful parallel Monte Carlo library.
####
#### Copyright (C) 2012-present, The Computational Data Science Lab
####
#### This file is part of the ParaMonte library.
####
#### Permission is hereby granted, free of charge, to any person obtaining a
#### copy of this software and associated documentation files (the "Software"),
#### to deal in the Software without restriction, including without limitation
#### the rights to use, copy, modify, merge, publish, distribute, sublicense,
#### and/or sell copies of the Software, and to permit persons to whom the
#### Software is furnished to do so, subject to the following conditions:
####
#### The above copyright notice and this permission notice shall be
#### included in all copies or substantial portions of the Software.
####
#### THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
#### EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
#### MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
#### IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
#### DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
#### OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
#### OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
####
#### ACKNOWLEDGMENT
####
#### ParaMonte is an honor-ware and its currency is acknowledgment and citations.
#### As per the ParaMonte library license agreement terms, if you use any parts of
#### this library for any purposes, kindly acknowledge the use of ParaMonte in your
#### work (education/research/industry/development/...) by citing the ParaMonte
#### library as described on this page:
####
#### https://github.com/cdslaborg/paramonte/blob/3548c097f2a25dfc0613061800656d27d0e0ddbe/ACKNOWLEDGMENT.md
####
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import os
import numpy as np
import typing as tp
import weakref as wref
import _paramonte as pm
import _pmutils as pmutils
Struct = pmutils.Struct
newline = pmutils.newline
####################################################################################################################################
#### BasePlot class
####################################################################################################################################
[docs]class BasePlot:
"""
This is the class for generating instances of
basic plots with minimally one (X)-axis. It serves as the
superclass for a wide variety of other multi-axes ParaMonte plots.
**Parameters**
plotType
A string indicating the name of the plot that is to be constructed.
dataFrame (optional)
A pandas dataFrame whose data will be plotted.
methodName (optional)
The name of the ParaMonte sample requesting the BasePlot.
reportEnabled (optional)
A boolean whose value indicates whether guidelines should be
printed in the standard output.
resetPlot (optional)
A function that resets the properties of the plot as desired
from outside. If provided, a pointer to this function will be
saved for future internal usage.
**Attributes**
_dfref
A weak reference to the input dataFrame whose data is used to
generate plots.
rows
A numeric vector that determines the rows of dataFrame
to be visualized. It can be either:
1. a ``range(start,stop,step)``, or,
2. a list of row indices in ``dataFrame.index``.
Examples:
1. ``rows = range(17,7,-2)``
2. ``rows = [i for i in range(7,17)]``
If not provided, the default includes all rows of the dataframe.
set
A structure with two attributes:
enabled
A boolean indicating whether a call to the ``set()``
function of the seaborn library should be made or not.
kws
A structure whose components are directly passed as
keyword arguments to the ``set()`` function.
Example usage:
.. code-block:: python
set.kws.style = "darkgrid"
**NOTE**
If a desired property is missing among the ``kws``
attributes, simply add the field and its value to
the component.
axes (available only in 1D and 2D plots)
A structure with one attribute:
kws
A structure whose components are directly passed as
keyword arguments to the ``gca()`` function of the
matplotlib library.
Example usage:
.. code-block:: python
axes.kws.facecolor = "w"
**NOTE**
If a desired property is missing among the ``kws``
attributes, simply add the field and its value to
the component.
axes3d (available only in 3D plots)
A structure with one attribute:
kws
A structure whose components are directly passed as
keyword arguments to the ``Axes3D()`` function of the
matplotlib library.
Example usage:
.. code-block:: python
axes3d.kws.facecolor = "w"
**NOTE**
If a desired property is missing among the ``kws``
attributes, simply add the field and its value to
the component.
figure
A structure with two attributes:
enabled
A boolean indicating whether a call to the ``figure()``
function of the matplotlib library should be made or not.
If a call is made, a new figure will be generated.
Otherwise, the current active figure will be used.
kws
A structure whose components are directly passed as
keyword arguments to the ``figure()`` function of
the matplotlib library.
Example usage:
.. code-block:: python
figure.kws.facecolor = "w"
**NOTE**
If a desired property is missing among the ``kws``
attributes, simply add the field and its value to
the component.
legend (may not exist for some types of plots)
A structure with two attributes:
enabled
A boolean indicating whether a call to the ``legend()``
function of the matplotlib library should be made or not.
If a call is made, a new figure will be generated.
Otherwise, the current active figure will be used.
kws
A structure whose components are directly passed as
keyword arguments to the ``legend()`` function of
the matplotlib library.
Example usage:
.. code-block:: python
legend.kws.labels = ["Variable1", "Variable2"]
**NOTE**
If a desired property is missing among the ``kws``
attributes, simply add the field and its value to
the component.
A legend will be added to a plot only if no color-mappings are
requested in the plot.
currentFig
A structure whose attributes are the outputs of various plotting
tools used to make the current figure. These include the handle
to the current figure, the handle to the current axes in the plot,
the handle to the colorbar (if any exists), and other Python
plotting tools used to make to generate the figure.
**Returns**
An object of ``BasePlot`` class.
---------------------------------------------------------------------------
"""
################################################################################################################################
#### __init__
################################################################################################################################
def __init__( self
, plotType
, dataFrame = None
, methodName = "ParaMonte"
, reportEnabled = True
, resetPlot = None
):
self._dfref = None if dataFrame is None else wref.ref(dataFrame)
self._type = Struct()
self._type.name = plotType
plotTypeLower = plotType.lower()
self._type.is3d = "3" in plotTypeLower
self._type.isLine = "line" in plotTypeLower
self._type.isScatter = "scatter" in plotTypeLower
self._type.isHeatmap = "heatmap" in plotTypeLower
self._type.isKdeplot1 = "kdeplot1" in plotTypeLower
self._type.isKdeplot2 = "kdeplot2" in plotTypeLower
self._type.isHistplot = "histplot" in plotTypeLower
self._type.isJointplot = "jointplot" in plotTypeLower
self._type.isEllipsoid = "covmat" in plotTypeLower
self._type.isEllipsoid = "cormat" in plotTypeLower
self._type.isGridPlot = "grid" in plotTypeLower
self._type.isContour3 = "contour3" in plotTypeLower
self._type.isContourf = "contourf" in plotTypeLower
self._type.isContour = "contour" in plotTypeLower and not (self._type.isContour3 or self._type.isContourf)
self._type.is1d = self._type.isKdeplot1 or self._type.isHistplot
self._type.is2d = not (self._type.isGridPlot or self._type.is1d or self._type.is3d)
self._type.isDiffusionPlot = self._type.isContour or self._type.isContourf or self._type.isContour3
self._methodName = methodName
self._reportEnabled = reportEnabled
self._progress = pm.utils.Progress ( msg = "creating a " + self._type.name + " plot object from scratch... "
, methodName = methodName
, reportEnabled = reportEnabled
, end = ""
)
self._reset()
if resetPlot is None:
self._resetPlot = self._reset
else:
self._resetPlot = resetPlot
################################################################################################################################
#### reset
################################################################################################################################
[docs] def reset ( self
, resetType = "soft"
, **kwargs
):
"""
Reset the properties of the plot to the original default settings.
Use this method when you change many attributes of the plot and
you want to clean up and go back to the default settings.
**Parameters**
resetType (optional)
An optional string with possible value of ``"hard"``.
If provided, the plot object will be regenerated from scratch.
This includes reading the original data frame again and resetting
everything. If not provided, then only the plot settings will be
reset without reseting the dataFrame.
**Returns**
None
**Example**
.. code-block:: python
:linenos:
reset() # reset the plot to the default settings
reset("soft") # reset the plot to the default settings
reset("hard") # regenerate the plot from scratch
"""
try:
self._resetPlot(resetType = resetType, plotNames = self._type.name) # call external reset function
except:
self._resetPlot(**kwargs) # call _reset(self)
################################################################################################################################
#### _reset
################################################################################################################################
[docs] def _reset(self):
self.rows = None
self._fnameOld = None # used in savefig() to generate unique random file names.
self.set = Struct()
self.set.enabled = True
self.set.kws = Struct()
if self._type.isKdeplot2 or self._type.isJointplot:
self.set.kws.style = "ticks"
elif self._type.is3d:
self.set.kws.style = "white"
else:
self.set.kws.style = "darkgrid"
self.figure = Struct()
self.figure.enabled = True
self.figure.kws = Struct()
self.currentFig = Struct()
if self._type.is3d:
self.axes3d = Struct()
self.axes3d.kws = Struct()
self.axes3d.kws.alpha = 1
self.axes3d.kws.visible = True
#self.axes3d.enabled = self.figure.enabled
else:
self.axes = Struct()
self.axes.kws = Struct()
self.axes.kws.alpha = 1
self.axes.kws.visible = True
#self.axes.enabled = self.figure.enabled
if not (self._type.isGridPlot or self._type.isHeatmap or self._type.isDiffusionPlot):
self.legend = Struct()
self.legend.enabled = False
self.legend.kws = Struct()
################################################################################################################################
#### savefig
################################################################################################################################
[docs] def savefig ( self
, reself = False
, **savefig_kws
):
"""
Export the current figure to external file.
**Parameters**
reself
A logical variable. If ``True``, the path to
the output generated file will be returned.
The default value is ``False``.
**savefig_kws (optional)
The set of input arguments to the ``savefig()`` function
of the matplotlib library, including the ``fname``. If
the file name is not provided, a unique random filename
will be generated and used to save the figure to an
output file.
Example:
.. code-block:: python
savefig(fname = "gridplot.png")
**Returns**
None
**Example**
.. code-block:: python
savefig() # use a unique random filename to output the plot.
savefig(fname = "thisPlot.png")
savefig(pad_inches = 0.0, bbox_inches = "tight")
"""
if "fname" not in savefig_kws.keys() or (isinstance(self._fnameOld,str) and isinstance(savefig_kws["fname"],str) and savefig_kws["fname"]==self._fnameOld):
savefig_kws["fname"] = os.path.join( os.getcwd(), pm.utils.getRandomFilePrefix(prefix=self._type.name+"_") + ".png" )
self._fnameOld = savefig_kws["fname"]
import matplotlib.pyplot as plt # xxx how is this figure activated?
self._progress.note( msg = "saving the plot to file: \"" + savefig_kws["fname"] + "\"", end = "\n", pre = True )
plt.savefig(**savefig_kws)
self._progress.note( end = "\n", pre = True )
################################################################################################################################
#### getLinSpace
################################################################################################################################
[docs] def getLinSpace ( self
, skip = None
, lowerLim : tp.Optional[ np.int32 ] = 1
, upperLim : tp.Optional[ np.int32 ] = None
):
"""
Generate linearly-spaced **unique** integer numbers
between the input lowerLim and upperLim. These numbers
can be used as the row indices in the plots.
**Parameters**
skip (optional)
The linear spacing between the generated points.
If ``skip`` is specified as input, any input value
for ``npoint`` will be ignored.
The default value is ``None``.
lowerLim (optional)
The natural (non-logarithmic) lower limit of the
generated linearly-spaced integer numbers.
If not provided, the default value is ``1``.
upperLim (optional)
The natural (non-logarithmic) upper limit of the
generated linearly-spaced integer numbers.
If not provided, the default value is the maximum
of the number of the rows of the input dataframe
to the ``BasePlot`` constructor.
**Returns**
A set of unique linearly-spaced integer numbers.
**Example**
.. code-block:: python
rows = getLinSpace(3, 1, 10000)
"""
if upperLim is None and self._dfref is not None:
upperLim = len(self._dfref().index)
elif upperLim is None and self.rows is not None:
upperLim = len(self.rows[-1])
else:
pm.abort( msg = "Either the input argument ``upperLim`` or the dataFrame of the plot object" + newline
+ "must be given in order to generate a linear range of values. " + newline
+ "Here is the documentation of getLinSpace():" + newline
+ newline
+ self.getLinSpace.__doc__
+ newline
, marginTop = 1
, marginBot = 1
, methodName = self._methodName
)
if skip is None:
skip = (upperLim - lowerLim) // 256
if skip<1: skip = 1
return range(lowerLim,upperLim,skip)
################################################################################################################################
#### getLogLinSpace
################################################################################################################################
[docs] def getLogLinSpace ( self
, base : tp.Optional[ np.float64 ] = 1.2
, logskip : tp.Optional[ np.int32 ] = 0.2
, lowerLim : tp.Optional[ np.int32 ] = 1
, upperLim : tp.Optional[ np.int32 ] = None
):
"""
Generate logarithmically-uniformly-spaced **unique** integer
numbers between the input lowerLim and upperLim. These numbers
are to be used as the row indices in the plots.
**Parameters**
base (optional)
The base of the logarithm used for generating the
logarithmically-uniform range of integers.
The default value is ``1.2``.
logskip (optional)
The minimum logarithmic space jump between
the generated log-linearly-spaced integer numbers.
The default value is ``0.2``.
lowerLim (optional)
The natural (non-logarithmic) lower limit of the
generated log-linearly-spaced integer numbers.
If not provided, the default value is ``1``.
upperLim (optional)
The natural (non-logarithmic) upper limit of the
generated log-linearly-spaced integer numbers.
If not provided, the default value is the maximum
of the number of the rows of the input dataframe
to the BasePlot constructor.
**Returns**
A set of unique log-linearly-spaced integer numbers.
**Example**
.. code-block:: python
rows = getLogLinSpace(1.01, 1, 1, 10000)
"""
if upperLim is None and self._dfref is not None:
upperLim = len(self._dfref().index)
elif upperLim is None and self.rows is not None:
upperLim = len(self.rows[-1])
else:
pm.abort( msg = "Either the input argument ``upperLim`` or the dataFrame of the plot object" + newline
+ "must be given in order to generate a log-linear range of values. " + newline
+ "Here is the documentation of getLogLinSpace():" + newline
+ newline
+ self.getLogLinSpace.__doc__
+ newline
, marginTop = 1
, marginBot = 1
, methodName = self._methodName
)
return pm.utils.getLogIntSpace(base,logskip,lowerLim,upperLim)
################################################################################################################################
#### _constructBasePlot
################################################################################################################################
[docs] def _constructBasePlot(self):
"""
Generate Figure and Axes instances if needed and return nothing.
**Parameters**
None
**Returns**
None
"""
end = "\n" if "grid"==self._type.name else ""
self._progress.note( msg = "making the " + self._type.name + " plot... ", end = end, pre = True )
import seaborn as sns
from matplotlib import pyplot as plt
plt.ion() # turn on the interactive mode. Used to detach the figure from the command line in ipython
if self._type.is3d: from mpl_toolkits.mplot3d import Axes3D
if self.set.enabled:
sns.set( **vars(self.set.kws) )
if not (self._type.isJointplot or self._type.isGridPlot): # jointplot and gridplot create their own figure and axes
if self.figure.enabled:
self.currentFig.figure = plt.figure( **vars(self.figure.kws) )
if self._type.is3d:
self.currentFig.axes = Axes3D(self.currentFig.figure, **vars(self.axes3d.kws) )
else:
self.currentFig.axes = self.currentFig.figure.gca( **vars(self.axes.kws) )
else:
self.currentFig.figure = plt.gcf() # self.currentFig.axes.get_figure()
self.currentFig.axes = self.currentFig.figure.gca()
################################################################################################################################
#### _finalizeBasePlot
################################################################################################################################
[docs] def _finalizeBasePlot(self):
"""
Set the base Figure properties, such as legends, ...
**Parameters**
None
**Returns**
None
"""
############################################################################################################################
#### _finalizeBasePlot
############################################################################################################################
if not (self._type.isGridPlot or self._type.isHeatmap or self._type.isDiffusionPlot) and self.legend.enabled:
labelsNeeded = False
if not hasattr(self.legend.kws, "labels"):
labelsNeeded = True
elif self.legend.kws.labels is None:
labelsNeeded = True
if labelsNeeded and hasattr(self.legend,"_labels") and self.legend._labels is not None:
self.legend.kws.labels = self.legend._labels
if self._type.isJointplot:
self.currentFig.legend = self.currentFig.jointplot.ax_joint.legend( **vars(self.legend.kws) )
else:
self.currentFig.legend = self.currentFig.axes.legend( **vars(self.legend.kws) )
self.currentFig.legend.set_zorder(20) # bring legend to the top
if not self._type.is3d:
import matplotlib.pyplot as plt
plt.tight_layout()
self._progress.note()
################################################################################################################################
#### _checkDataType
################################################################################################################################
[docs] def _checkDataType(self):
"""
Verify the integrity of the dataFrame and return nothing.
**Parameters**
None
**Returns**
None
"""
import pandas as pd
fatalmsg = None
if self._dfref is None:
fatalmsg = "It appears that no data has been passed for plotting." + newline
elif not isinstance(self._dfref,wref.ref):
fatalmsg = "It appears that you have messed with the " + newline + " internal representation of data in the object." + newline
elif not isinstance(self._dfref(), pd.DataFrame):
fatalmsg = ""
if fatalmsg is not None:
raise Exception ( fatalmsg
+ "The input data must be a pandas' dataframe." + newline
+ "Please pass a dataFrame to the constructor or at" + newline
+ "the time of calling the object (which is callable with" + newline
+ "the same input arguments as the object's constructor."
)
################################################################################################################################
#### _getDocString
################################################################################################################################
[docs] def _getDocString(self):
docString = newline \
+ "Here is the help information on the BasePlot class:" + newline \
+ newline \
+ self.__doc__
return docString
####################################################################################################################################