Merge pull request #137 from JaviMerino/bar-plot-doc

Documentation and tests for BarPlot

3 files changed, 262 insertions, 1 deletions

diff --git a/doc/BarPlotter.ipynb b/doc/BarPlotter.ipynb
new file mode 100644
index 0000000..fc3a675
--- /dev/null
+++ b/doc/BarPlotter.ipynb
@@ -0,0 +1,201 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:ca5da22b92912edbee0d2eda0f09e090d254c56896bbb84760126b772ce6d62c"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline\n",
+      "import pandas as pd\n",
+      "import trappy"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "/usr/lib/python2.7/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
+        "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "dfr = pd.DataFrame({\"foo\": [1, 2, 3], \"bar\": [2, 3, 1], \"baz\":[3, 2, 1]})"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Simple bar plot of one column"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.BarPlot(dfr, column=\"foo\").view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "/usr/lib/python2.7/dist-packages/matplotlib/__init__.py:874: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
+        "  warnings.warn(self.msg_depr % (key, alt_key))\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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EJgAAAAAFhCZkw04TUjDfD+REzwOgbIQmAAAAAAWEJmTDThNSMN8P5ETPA6Bs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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f3c1f878f50>"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Multiple columns"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.BarPlot(dfr, column=[\"foo\", \"bar\"], title=\"Multiple columns\").view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f3c1f73e610>"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Stacked plot"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.BarPlot(dfr, column=[\"foo\", \"bar\", \"baz\"], stacked=True).view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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k5513zvnnn1+1Mdx///0588wzBSEA0E5WrFiR3XffPcmm3q8pU6Zk4cKFWb9+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\noCjKEAAAAKAoyhAAAACgKP8LZiQRfMvPlloAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f3c1f5f8e50>"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Signals"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Signals only work with trappy traces.  Let's first get a trace."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trace = trappy.BareTrace()\n",
+      "trace.add_parsed_event(\"my_event\", dfr)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "If you have a trappy trace, you can use the signal syntax instead of columns and templates"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.BarPlot(trace, signals=[\"my_event:baz\", \"my_event:foo\"]).view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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5+uqr83/+z//J5ptvngkTJmyMsQMAAAC0m/X6yuEN7e6779aeU1PacwAAqkV7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\n+qwpgz1NgDqx1gJQNUITAAA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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f3c1f6a1690>"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/tests/test_plotter.py b/tests/test_plotter.py
index e675311..ae59384 100644
--- a/tests/test_plotter.py
+++ b/tests/test_plotter.py
@@ -246,3 +246,20 @@ class TestPlotter(BaseTestThermal):
         self.assertTrue([4, 5, 1] in data["task1-15411"])
         self.assertEquals(procs, expected_procs)
         self.assertEquals(window, [1, 5])
+
+class TestBarPlot(unittest.TestCase):
+    def setUp(self):
+        self.dfr = pd.DataFrame({"foo": [1, 2, 3],
+                                 "bar": [2, 3, 1],
+                                 "baz": [3, 2, 1]})
+
+    def test_barplot_dfr(self):
+        """BarPlot plots dataframes without exploding"""
+        trappy.BarPlot(self.dfr, column=["foo", "bar"]).view(test=True)
+
+    def test_barplot_trace(self):
+        """BarPlot plots traces without exploding"""
+        trace = trappy.BareTrace()
+        trace.add_parsed_event("event", self.dfr)
+
+        trappy.BarPlot(trace, signals=["event:foo", "event:bar"]).view(test=True)
diff --git a/trappy/plotter/BarPlot.py b/trappy/plotter/BarPlot.py
index 33442bb..fda4284 100644
--- a/trappy/plotter/BarPlot.py
+++ b/trappy/plotter/BarPlot.py
@@ -21,7 +21,50 @@ import numpy as np
 from trappy.plotter.StaticPlot import StaticPlot
 
 class BarPlot(StaticPlot):
-    """A matplotlib static plotter which produces line plots"""
+    """BarPlot can plot data as vertical bars
+
+    Values are plotted against their position in the list of data.
+
+    :param traces: The input data
+    :type traces: A single instance or a list of :mod:`trappy.trace.FTrace` or :mod:`pandas.DataFrame`
+
+    :param column: specifies the name of the column to be plotted.
+    :type column: str or list(str)
+
+    :param templates: TRAPpy events
+
+        .. note::
+
+                This is not required if a :mod:`pandas.DataFrame` is
+                used
+
+    :type templates: :mod:`trappy.base.Base`
+
+    :param signals: A string of the type event_name:column
+        to indicate the value that needs to be plotted
+
+        .. note::
+
+            - Only one of `signals` or both `templates` and
+              `columns` should be specified
+            - Signals format won't work for :mod:`pandas.DataFrame`
+              input
+
+    :type signals: str or list(string)
+
+    :param title: A title describing the generated plots
+    :type title: str
+
+    :param stacked: The series are grouped by default.  If you want a
+        stacked plot, set stacked to True.
+    :type stacked: bool
+
+    :param spacing: A proportion of the size of each group which
+        should be used as the spacing between the groups. e.g. 0.2
+        (default) means that 1/5 of the groups total width is used as
+        a spacing between groups.
+    :type spacing: float
+    """
 
     def __init__(self, traces, templates=None, **kwargs):
         # Default keys, each can be overridden in kwargs