end plots

skcriteria/plot/__init__.py

@@ -58,6 +58,7 @@
 from .. import norm
 from .radar import radar_plot
 from .multihist import multihist_plot
+from .scmtx import scmtx_plot
 
 
 # =============================================================================
@@ -89,7 +90,7 @@ def __str__(self):
     def __repr__(self):
         return str(self)
 
-    def __call__(self, plotname="radar", **kwargs):
+    def __call__(self, plotname="scatter_matrix", **kwargs):
         method = getattr(self, plotname)
         return method(**kwargs)
 
@@ -118,3 +119,6 @@ def radar(self, **kwargs):
 
     def hist(self, **kwargs):
         self.plot(multihist_plot, **kwargs)
+
+    def scatter_matrix(self, **kwargs):
+        self.plot(scmtx_plot, **kwargs)

skcriteria/plot/multihist.py

@@ -43,15 +43,10 @@
 import numpy as np
 
 import matplotlib.pyplot as plt
-from matplotlib.path import Path
-from matplotlib.spines import Spine
-from matplotlib.projections.polar import PolarAxes
-from matplotlib.projections import register_projection
 from matplotlib import cm
 
 from six.moves import zip
 
-from .. import norm, util
 
 # =============================================================================
 # FUNCTIONS
@@ -60,19 +55,42 @@
 def multihist_plot(
         mtx, criteria, weights, anames, cnames,
         weighted=True, cmap=None, ax=None,
-        histtype="stepfilled", alpha=0.8, **kwargs):
+        subplots_kwargs=None, hist_kwargs=None):
 
     # create ax if necesary
     if ax is None:
-        ax = plt.subplots()[-1]
+        subplots_kwargs = subplots_kwargs or {}
+        ax = plt.subplots(**subplots_kwargs)[-1]
 
     # colors
     cmap = cm.get_cmap(name=cmap)
     colors = cmap(np.linspace(0, 1, mtx.shape[1]))
 
-    for arr, col in zip(mtx.T, colors):
-        ax.hist(arr, color=col, histtype=histtype, alpha=alpha, **kwargs)
+    # weight the data
+    if weighted and weights is not None:
+        wdata = np.multiply(mtx, weights)
+    else:
+        wdata = mtx
 
-    ax.legend(cnames, loc="best")
+    # histogram
+    hist_kwargs = hist_kwargs or {}
+    hist_kwargs.setdefault("histtype", "stepfilled")
+    hist_kwargs.setdefault("alpha", 0.8)
+
+    for arr, col in zip(wdata.T, colors):
+        ax.hist(arr, color=col, **hist_kwargs)
+
+    ax.spines['right'].set_visible(False)
+    ax.spines['top'].set_visible(False)
+
+    # labels for criteria
+    if weights is not None:
+        clabels = [
+            "{} (w.{:.2f})".format(cn, cw)
+            for cn, cw in zip(cnames, weights)]
+    else:
+        clabels = ["{}".format(cn) for cn in cnames]
+
+    ax.legend(clabels, loc="best")
 
     return ax

skcriteria/plot/radar.py

@@ -166,14 +166,17 @@ def unit_poly_verts(theta):
 
 
 def radar_plot(mtx, criteria, weights, anames, cnames, weighted=True,
-               frame="polygon", cmap=None, ax=None, labelrow=5):
+               frame="polygon", cmap=None, ax=None, labelrow=5,
+               subplots_kwargs=None):
 
     # register radar
     theta = radar_factory(len(criteria), frame=frame)
 
     # create ax if necesary
     if ax is None:
-        ax = plt.subplots(subplot_kw=dict(projection='radar'))[-1]
+        subplots_kwargs = subplots_kwargs or {}
+        subplots_kwargs.setdefault("subplot_kw", {"projection": "radar"})
+        ax = plt.subplots(**subplots_kwargs)[-1]
     figure = ax.get_figure()
 
     # invert the miniun criteria

skcriteria/plot/scmtx.py

@@ -0,0 +1,177 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2016-2017, Cabral, Juan; Luczywo, Nadia
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+
+# * Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+
+from __future__ import division, print_function
+
+
+# =============================================================================
+# META
+# =============================================================================
+
+__doc__ = """ Create a scatter-plot matrix using Matplotlib. """
+
+__author__ = "adrn <adrn@astro.columbia.edu>"
+
+
+# =============================================================================
+# IMPORTS
+# =============================================================================
+
+import numpy as np
+
+import matplotlib.pyplot as plt
+from matplotlib import cm
+
+from six.moves import range, zip
+
+
+# =============================================================================
+# FUNCTIONS
+# =============================================================================
+
+def scatter_plot_matrix(
+        data, labels, colors, axes=None,
+        subplots_kwargs=None, scatter_kwargs=None, hist_kwargs=None):
+    """ Create a scatter plot matrix from the given data.
+
+        Parameters
+        ----------
+        data : numpy.ndarray
+            A numpy array containined the scatter data to plot. The data
+            should be shape MxN where M is the number of dimensions and
+            with N data points.
+        labels : numpy.ndarray (optional)
+            A numpy array of length M containing the axis labels.
+        axes : matplotlib Axes array (optional)
+            If you've already created the axes objects, pass this in to
+            plot the data on that.
+        subplots_kwargs : dict (optional)
+            A dictionary of keyword arguments to pass to the
+            matplotlib.pyplot.subplots call. Note: only relevant if axes=None.
+        scatter_kwargs : dict (optional)
+            A dictionary of keyword arguments to pass to the
+            matplotlib.pyplot.scatter function calls.
+        hist_kwargs : dict (optional)
+            A dictionary of keyword arguments to pass to the
+            matplotlib.pyplot.hist function calls.
+    """
+
+    M, N = data.shape
+
+    if axes is None:
+        skwargs = subplots_kwargs or {}
+        skwargs.setdefault("sharex", False)
+        skwargs.setdefault("sharey", False)
+        fig, axes = plt.subplots(M, M, **skwargs)
+
+    sc_kwargs = scatter_kwargs or {}
+    sc_kwargs.setdefault("edgecolor", "none")
+    sc_kwargs.setdefault("s", 10)
+
+    hist_kwargs = hist_kwargs or {}
+    hist_kwargs.setdefault("histtype", "stepfilled")
+    hist_kwargs.setdefault("alpha", 0.8)
+
+    xticks, yticks = None, None
+
+    hist_color_idx = int(len(colors) / 2 - 1)
+    if hist_color_idx < 0:
+        hist_color_idx = 0
+    hist_color = colors[hist_color_idx]
+
+    icolors, colors_buff = iter(colors), {}
+
+    for ii in range(M):
+        for jj in range(M):
+            ax = axes[ii, jj]
+            col = (
+                colors_buff[(ii, jj)]
+                if (ii, jj) in colors_buff else
+                next(icolors))
+            if ii == jj:
+                ax.hist(data[ii], color=hist_color, **hist_kwargs)
+            else:
+                ax.scatter(data[jj], data[ii], color=col, **sc_kwargs)
+                colors_buff[(ii, jj)] = col
+                colors_buff[(jj, ii)] = col
+
+            if yticks is None:
+                yticks = ax.get_yticks()[1: -1]
+
+            if xticks is None:
+                xticks = ax.get_xticks()[1: -1]
+
+            # first column
+            if jj == 0:
+                ax.set_ylabel(labels[ii])
+
+                # Hack so ticklabels don't overlap
+                ax.yaxis.set_ticks(yticks)
+
+            # last row
+            if ii == M - 1:
+                ax.set_xlabel(labels[jj])
+
+                # Hack so ticklabels don't overlap
+                ax.xaxis.set_ticks(xticks)
+
+            ax.spines['right'].set_visible(False)
+            ax.spines['top'].set_visible(False)
+
+    fig = axes[0, 0].figure
+    fig.subplots_adjust(hspace=0.25, wspace=0.25, left=0.08,
+                        bottom=0.08, top=0.9, right=0.9)
+    return axes
+
+
+def scmtx_plot(mtx, criteria, weights, anames, cnames, weighted=True,
+               frame="polygon", cmap=None, ax=None, **kwargs):
+
+    cmap = cm.get_cmap(name=cmap)
+    colors = cmap(np.linspace(0, 1, mtx.shape[1] ** 2))
+
+    # weight the data
+    if weighted and weights is not None:
+        wdata = np.multiply(mtx, weights)
+    else:
+        wdata = mtx
+
+    # labels for criteria
+    if weights is not None:
+        clabels = [
+            "{} (w.{:.2f})".format(cn, cw)
+            for cn, cw in zip(cnames, weights)]
+    else:
+        clabels = ["{}".format(cn) for cn in cnames]
+
+    return scatter_plot_matrix(
+        wdata.T, labels=clabels, colors=colors, axes=ax, **kwargs)

skcriteria/tests/test_plot.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2016-2017, Cabral, Juan; Luczywo, Nadia
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+
+# * Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+
+
+# =============================================================================
+# FUTURE
+# =============================================================================
+
+from __future__ import unicode_literals
+
+
+# =============================================================================
+# DOC
+# =============================================================================
+
+__doc__ = """Test normalization functionalities"""
+
+
+# =============================================================================
+# IMPORTS
+# =============================================================================
+
+import random
+
+import numpy as np
+
+import mock
+
+from six.moves import range
+
+from . import core
+
+from .. import Data
+
+
+# =============================================================================
+# BASE
+# =============================================================================
+
+@mock.patch("matplotlib.pyplot.show")
+class PlotTestCase(core.SKCriteriaTestCase):
+
+    def setUp(self):
+        self.alternative_n, self.criteria_n = 5, 3
+        self.mtx = np.random.rand(self.alternative_n, self.criteria_n)
+        self.criteria = np.asarray([
+            random.choice([1, -1]) for n in range(self.criteria_n)])
+        self.weights = np.random.randint(1, 100, self.criteria_n)
+        self.data = Data(self.mtx, self.criteria, self.weights)
+
+    def test_scattermatrix(self, *args):
+        self.data.plot()
+        self.data.plot("scatter_matrix")
+        self.data.plot.scatter_matrix()
+
+    def test_radar(self, *args):
+        self.data.plot("radar")
+        self.data.plot.radar()
+
+    def test_hist(self, *args):
+        self.data.plot("hist")
+        self.data.plot.hist()