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quatrope · Jun 8, 2017 · 8f592da · 8f592da
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diff --git a/Untitled.ipynb b/Untitled.ipynb
@@ -0,0 +1,108 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "from skcriteria import Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th style=\"text-align: center;\"> ALT./CRIT. </th><th style=\"text-align: center;\"> C0 (min) W.19 </th><th style=\"text-align: center;\"> C1 (max) W.27 </th><th style=\"text-align: center;\"> C2 (min) W.94 </th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td style=\"text-align: center;\">     A0     </td><td style=\"text-align: center;\">   0.510896    </td><td style=\"text-align: center;\">   0.473174    </td><td style=\"text-align: center;\">    0.1366     </td></tr>\n",
+       "<tr><td style=\"text-align: center;\">     A1     </td><td style=\"text-align: center;\">   0.815747    </td><td style=\"text-align: center;\">   0.845667    </td><td style=\"text-align: center;\">   0.961278    </td></tr>\n",
+       "<tr><td style=\"text-align: center;\">     A2     </td><td style=\"text-align: center;\">   0.427846    </td><td style=\"text-align: center;\">   0.851048    </td><td style=\"text-align: center;\">   0.157045    </td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       " ALT./CRIT.    C0 (min) W.19    C1 (max) W.27    C2 (min) W.94\n",
+       "------------  ---------------  ---------------  ---------------\n",
+       "     A0          0.510896         0.473174          0.1366\n",
+       "     A1          0.815747         0.845667         0.961278\n",
+       "     A2          0.427846         0.851048         0.157045"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mtx = np.random.rand(3, 3)\n",
+    "criteria = np.asarray([random.choice((1, -1)) for e in range(3)])\n",
+    "weights = np.random.randint(1, 100, 3)\n",
+    "data = Data(mtx, criteria, weights)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "global name 'data' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-66a0473f7c22>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/home/juan/proyectos/scikit-criteria/src/skcriteria/plot/__init__.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, plotname, *args, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplotname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"radar\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplotname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/juan/proyectos/scikit-criteria/src/skcriteria/plot/__init__.py\u001b[0m in \u001b[0;36mradar\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     96\u001b[0m             \u001b[0mmtx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmtx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriteria\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcriteria\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0manames\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m             cnames=data.cnames, *args, **kwargs)\n\u001b[0m",
+      "\u001b[0;32m/home/juan/proyectos/scikit-criteria/src/skcriteria/plot/radar.pyc\u001b[0m in \u001b[0;36mradar_plot\u001b[0;34m(mtx, criteria, weights, anames, cnames, frame, cmap)\u001b[0m\n\u001b[1;32m    166\u001b[0m                frame=\"polygon\", cmap=None):\n\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m     \u001b[0mmincrits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcriteria\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0msc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMIN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m     \u001b[0mmincrits_inverted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmtx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmincrits\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m     \u001b[0mpdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmtx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmincrits_inverted\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: global name 'data' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "data.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/coso.py b/coso.py
@@ -0,0 +1,11 @@
+import random
+
+import numpy as np
+
+from skcriteria import Data
+
+mtx = np.random.rand(10, 10)
+criteria = np.asarray([1,-1, 1, 1,1,1,1,1,1,1])
+weights = np.random.randint(1, 100, 10)
+data = Data(mtx, criteria, weights)
+data.plot(mnorm="vector", wnorm="sum", cmap="jet", frame="circle")
diff --git a/skcriteria/_oop.py b/skcriteria/_oop.py
@@ -46,7 +46,7 @@
 
 from tabulate import tabulate
 
-from . import util, norm
+from . import util, norm, plot
 
 
 # =============================================================================
@@ -93,6 +93,8 @@ def __init__(self, mtx, criteria, weights=None, anames=None, cnames=None):
                 len(self._cnames), len(self._criteria))
             raise util.DataValidationError(msg)
 
+        self._plot = plot.PlotProxy(self)
+
     def _iter_rows(self):
         direction = map(CRITERIA_AS_ATR.get, self._criteria)
         title = ["ALT./CRIT."]
@@ -166,6 +168,10 @@ def criteria(self):
     def weights(self):
         return self._weights
 
+    @property
+    def plot(self):
+        return self._plot
+
 
 # =============================================================================
 # DECISION MAKER

diff --git a/skcriteria/plot/__init__.py b/skcriteria/plot/__init__.py
@@ -0,0 +1,113 @@
+#!/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
+
+
+# =============================================================================
+# DOCS
+# =============================================================================
+
+__doc__ = """Plotting utilities
+
+"""
+
+
+# =============================================================================
+# IMPORTS
+# =============================================================================
+
+from .. import norm
+
+from .radar import radar_plot
+
+
+# =============================================================================
+# CLASS
+# =============================================================================
+
+class PlotProxy(object):
+
+    def __init__(self, data):
+        self._data = data
+
+    def __unicode__(self):
+        return self.to_str()
+
+    def __bytes__(self):
+        encoding = sys.getdefaultencoding()
+        return self.__unicode__().encode(encoding, 'replace')
+
+    def __str__(self):
+        """Return a string representation for a particular Object
+
+        Invoked by str(df) in both py2/py3.
+        Yields Bytestring in Py2, Unicode String in py3.
+        """
+        if six.PY3:
+            return self.__unicode__()
+        return self.__bytes__()
+
+    def __repr__(self):
+        return str(self)
+
+    def __call__(self, plotname="radar", **kwargs):
+        method = getattr(self, plotname)
+        return method(**kwargs)
+
+    def to_str(self):
+        return "PlotProxy for {}".format(self._data)
+
+    def preprocess(self, data, mnorm, wnorm):
+        nmtx = norm.norm(mnorm, data.mtx, criteria=data.criteria, axis=0)
+        nweights = (
+            norm.norm(wnorm, data.weights, criteria=data.criteria)
+            if data.weights is not None else
+            np.ones(data.criteria.shape))
+        return nmtx, data.criteria, nweights
+
+    def plot(self, func, mnorm="none", wnorm="none", **kwargs):
+        data = self._data
+        nmtx, criteria, nweights = self.preprocess(data, mnorm, wnorm)
+        kwargs.update({
+            "mtx": nmtx, "criteria": criteria,
+            "weights": nweights, "anames": data.anames,
+            "cnames": data.cnames})
+        return func(**kwargs)
+
+    def radar(self, **kwargs):
+        self.plot(radar_plot, **kwargs)