diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..3aad73c Binary files /dev/null and b/.DS_Store differ diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..f24054f --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +2.7.15 diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/docs/.ipynb_checkpoints/easyplot_docs-checkpoint.ipynb b/docs/.ipynb_checkpoints/easyplot_docs-checkpoint.ipynb new file mode 100644 index 0000000..a9fe61d --- /dev/null +++ b/docs/.ipynb_checkpoints/easyplot_docs-checkpoint.ipynb @@ -0,0 +1,1075 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#easyplot\n", + "**easyplot:** A matplotlib wrapper written in Python to enable fast and easy creation of reusable plots.\n", + "\n", + "by [Sudeep Mandal](http://www.sudeepmandal.com)\n", + "\n", + "The EasyPlot module provides a thin wrapper to matplotlib, enabling fast and easy generation of beautiful, annotated plots with minimal code. It also enables the reuse of EasyPlot instances to generate new plots that maintain state from previous plots allowing for quick and easy generation of multiple plots of a similar type. EasyPlot supports all commonly used plot parameters and allows access to the underlying figure and axes instances to allow the user to further customize the generated plots if necessary.\n", + "
\n", + "I'd love to hear your comments and/or suggestions. You can get in touch with me via [twitter](https://twitter.com/hamsterhuey), [email](mailto:sudeepmandal@gmail.com) or [google+](https://plus.google.com/u/0/105292596991480463202/)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Sections\n", + "- [Requirements](#requirements)\n", + "- [Installation](#installation)\n", + "- [Motivation and Background](#motivation)\n", + "- [Features](#features)\n", + "- [Documentation](#documentation)\n", + " - [EasyPlot instance methods](#methods)\n", + " - [EasyPlot instance variables](#variables)\n", + " - [List of supported plot parameters](#plot_params)\n", + " - [Interactive/Non-Interactive plotting mode](#interactive_mode)\n", + "- [Usage and Examples](#usage)\n", + " - [Simple Plot](#simple_plot)\n", + " - [Multiple plots in the same figure (Interactive)](#multiple_plots)\n", + " - [Using EasyPlot object as template for new plots](#easyplot_template)\n", + " - [Autoscaling Plots](#autoscale)\n", + " - [Setting background grid](#grid)\n", + " - [Log/Linear/Symlog plots](#log_linear_scale)\n", + " - [Modifying Plot color cycle](#colorcycle)\n", + " - [Multiple plots in same figure iteratively using iter_plot](#iter_plot)\n", + "- [Advanced plotting](#advanced_plotting)\n", + " - [Using easyplot with subplots](#subplots)\n", + " - [Custom plot modifications via axes instance](#ax_mods)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Requirements\n", + "- Python 2.7.2+\n", + "- matplotlib\n", + "\n", + "Use of the [IPython](http://ipython.org/) shell is strongly recommended with this library (and matplotlib plotting in general). The `%matplotlib` magic command in IPython (or starting IPython using `ipython --matplotlib`) implements a number of backend tweaks to enable robust, interactive plotting using matplotlib." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Installation\n", + "[[back to section overview](#sections)]\n", + "\n", + "You can use the following commands to install EasyPlot:\n", + "\n", + "`pip install easyplot`\n", + "\n", + "or\n", + "\n", + "`easy_install easyplot`\n", + "\n", + "Alternatively, you could download the package manually from the Python Package Index: https://pypi.python.org/pypi/EasyPlot, unzip it, navigate into the package, and use the command:\n", + "\n", + "`python setup.py install`\n", + "\n", + "or\n", + "\n", + "`pip install .` " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Motivation and background\n", + "[[back to section overview](#sections)]\n", + "\n", + "Setting up aesthetically pleasing plots with plot titles, axes labels, etc requires several lines of boilerplate code in vanilla matplotlib. As an example, creating a basic plot in matplotlib requires the following lines of code: \n", + "```python\n", + "fig, ax = plt.subplots()\n", + "ax.plot(x, x**2, 'b-o', label=\"y = x**2\")\n", + "ax.plot(x, x**3, 'r--s', label=\"y = x**3\")\n", + "ax.legend(loc='best')\n", + "ax.grid()\n", + "ax.set_xlabel('x')\n", + "ax.set_ylabel('y')\n", + "ax.set_title('title')\n", + "```\n", + "\n", + "Pylab alleviates some of this, but still requires calls to a number of different functions that are commonly used (such as xlabel, xlim, etc.). More complicated plots can require several more lines of code. Typing all this code every time to generate plots gets tedious very quickly. This situation is further exacerbated when working in an IPython Notebook where all plots typically need to be labeled, annotated and looking their best. Having several lines of code preceeding every plot in a notebook can break the flow of the document and distract from the code/concepts being presented by the author. Furthermore, oftentimes, plots with similar labels and formatting need to be generated repeatedly with different datasets. Generating these sets of plots would require retyping these same lines of boilerplate code across different sections of your code/notebook.\n", + "\n", + "Easyplot is my attempt to address these issues and make generating quick, pleasant looking, annotated plots a bit easier. In keeping with [DRY philosophy](http://en.wikipedia.org/wiki/Don%27t_repeat_yourself), `easyplot` enables the creation of an `EasyPlot` object that maintains state information of all plot parameters passed to it in order to generate a plot. This can then be easily reused to generate new plots with the user only having to supply any additional plot parameters, or those parameters he or she wishes to override from the previous plot.\n", + "\n", + "Easyplot supports a large number of standard plot parameters that most users typically deal with when plotting in matplotlib. Additionally, it provides methods to access the figure and axes instance for the latest plot, enabling users to perform more custom plot modifications that are not directly supported by easyplot. It also supports interactive plotting where additional plot parameters can be passed to the current plot using the `update_plot` method. The plot above can be generated using `easyplot` as follows:\n", + "```python\n", + "eplot = EasyPlot(x, x**2, 'b-o', label='y = x**2', showlegend=True,\n", + " xlabel='x', ylabel='y', title='title', grid='on')\n", + "eplot.add_plot(x, x**3, 'r--s', label='y = x**3')\n", + "```\n", + "Along with the reduced typing, easyplot enables the consolidation and passing of all plot parameters into a single plot call. This is already quite handy, but the real benefit is evident when one needs to generate a new plot with the same plot parameters (such as axis labels and title) but with new data:\n", + "```python\n", + "eplot.new_plot(x, 1/x, 'g-D', label='y = 1/x')\n", + "```\n", + "\n", + "\n", + "`EasyPlot` also provides an `iter_plot()` method that iterates through x, y data and plot parameters that are provided in a list or dictionary format to automatically generate an annotated, multi-line plot with a single statement:\n", + "```python\n", + "eplot = EasyPlot(xlabel=r'$x$', ylabel='$y$', fontsize=16,\n", + " colorcycle=[\"#66c2a5\",\"#fc8d62\",\"#8da0cb\"], figsize=(8,5))\n", + "eplot.iter_plot(x, y_dict, linestyle=linestyle_dict, marker=marker_dict,\n", + " label=labels_dict, linewidth=3, ms=10, showlegend=True, grid='on')\n", + "```\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Features\n", + "[[back to section overview](#sections)]\n", + "- Access to a large number of the most used matplotlib plot parameters under a unified wrapper class\n", + "- Plot parameter aliases supported. Can be extended by user for arbitrary alias definitions for various plot parameters\n", + "- Ability to use `EasyPlot` objects as templates to rapidly generate annotated plots of a similar type\n", + "- `iter_plot()` method to easily iterate through x, y datasets and plot multiple plots with a single method call\n", + "- Draggable legend when using GUI backends (eg: qt, wx, etc.)\n", + "- Provides access to underlying figure, axes and line2D objects for advanced plot customization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Documentation\n", + "[[back to section overview](#sections)]\n", + "\n", + "EasyPlot consists of an `EasyPlot` class to create `EasyPlot()` objects in order to generate matplotlib plots. The object constructor allows for passing all commonly used plot parameters (such as `xlabel`, `ylabel`, `title`, `markersize`, `linewidth`, etc.) and the `x`, `y` data and an optional format string as arguments in order to generate an annotated plot via a single statement such as:\n", + "```python\n", + "fftplot = EasyPlot(freq, amplitude, 'g-o', markersize=9, linewidth=3, xlabel='Frequency (Hz)',\n", + " ylabel='Amplitude', label='FFT Data', showlegend=True)\n", + "```\n", + "If no `x`, `y` data is provided to the `EasyPlot` constuctor, creation of a figure and axes instance for the `EasyPlot` object is deferred and the plot parameters that are passed to the constructor are used to initialize the `EasyPlot` object. \n", + "\n", + "Apart from the usual plot parameters, `EasyPlot` constructor also takes the following special keyword arguments:\n", + "\n", + "`fig` : an optional reference to a figure object supplied by the user
\n", + "`ax` : an optional reference to an axes object (linked to `fig`) to use for displaying the plots. The [subplots example](#subplots) demonstrates the use of `fig` and `ax` plot parameters for custom plotting routines
\n", + "`figsize` : the size of the plot figure
\n", + "`showlegend` : set to True or False to display the plot legend\n", + "\n", + "The `EasyPlot` object also retains state information of most plot parameters passed to it, thus allowing the object to be reused as a template for generating new plots with similar formatting/labeling. The only parameters whose values are not persistent are `color`, `marker`, `linestyle` and `label` as they are assumed to be unique to a single plot.\n", + "\n", + "`EasyPlot` objects also provide access to their `figure`, `axes` and `Line2D` instances via the `get_figure()`, `get_axes()` methods and `line_list` instance variable respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### EasyPlot instance methods\n", + "The main instance methods for `EasyPlot` objects are:\n", + "
\n", + "`add_plot(*args, **kwargs)` : This is the main instance method to add additional plots and plot parameters to an existing plot. `*args` is a variable length argument, allowing for `x`, `y` pairs with an optional format string of the form `'b-o'`. The optional format string is a shorthand for specifying the plot color, linestyle and marker type. The [matplotlib docs](http://matplotlib.org/1.3.1/api/pyplot_api.html#matplotlib.pyplot.plot) provide more information regarding the format string notation. `**kwargs` are a list of named plot parameters as explained in the [next section](#plot_params).\n", + "
\n", + "`update_plot(**kwargs)` : Instance method to update plot parameters only (example: `xlabel`, `title`, etc.). This method is typically only useful when plotting interactively using an interactive matplotlib backend.\n", + "
\n", + "`new_plot(*args, **kwargs)` : Create a new plot using the plot parameters of the existing `EasyPlot` instance as a template (i.e., plot parameter settings carry over from the `EasyPlot` instance to the new plot)\n", + "
\n", + "`iter_plot(x, y, mode='dict', **kwargs)` : This method can be used to plot multiple line/scatter plots with a single statement as long as `x`, `y`, and any relevant plot parameters that change for each plot are passed in as either a dictionary with a common set of keys (`mode='dict'`) or a set of lists/arrays corresponding to each data set to be plotted (`mode='array'`).\n", + "
\n", + "`autoscale(enable=True, axis='both', tight=None)` : Set autoscale options for `x`, `y` or `both` axes\n", + "
\n", + "`grid(**kwargs)` : Equivalent to matplotlib axes.grid() method and accepts same set of keyword arguments. Enables more custom control over the plot grid.\n", + "
\n", + "`get_figure()` : Returns reference to the Figure object for the instance\n", + "
\n", + "`get_axes()` : Returns reference to the axes object for the instance\n", + "
\n", + "`redraw()` : Forces a redraw of the plot canvas. Must be used if custom modifications are made to the plot external to easyplot via direct access to the figure and axes handles. **Note: `redraw()` is only supported when plotting in interactive mode (i.e. `plt.isinteractive() == True`)**.\n", + "
\n", + "`set_fontsize(font_size)` : Modify global setting for plot font-size\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### EasyPlot instance variables\n", + "The main instance variables for `EasyPlot` objects are:\n", + "
\n", + "`self.kwargs` : A dictionary containing a list of all existing plot parameters for an `EasyPlot` object. The user should not modify this directly. This variable should be accessed in a read-only fashion for the purposes of examining the state of an `EasyPlot` object.\n", + "
\n", + "`self.line_list` : A list of [`Line2D`](http://matplotlib.org/1.3.1/api/artist_api.html#matplotlib.lines.Line2D) items corresponding to all plots of the `EasyPlot` object. These can be manipulated using standard matplotlib methods outside of `easyplot` for [advaced plotting](#advanced_plotting)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### List of supported plot parameters\n", + "```\n", + "Plot Parameters:\n", + "----------------\n", + "fig : figure instance for drawing plots\n", + "ax : axes instance for drawing plots (If user wants to supply axes,\n", + " figure externally, both ax and fig must be supplied together)\n", + "figSize : tuple of integers ~ width & height in inches\n", + "label : Label for line plot as determined by *args, string\n", + "color / c : Color of line plot, overrides format string in *args if\n", + " supplied. Accepts any valid matplotlib color\n", + "linewidth / lw : Plot linewidth\n", + "linestyle / ls : Plot linestyle ['-','--','-.',':','None',' ','']\n", + "marker : '+', 'o', '*', 's', 'D', ',', '.', '<', '>', '^', '1', '2'\n", + "markerfacecolor / mfc : Face color of marker\n", + "markeredgewidth / mew :\n", + "markeredgecolor / mec : \n", + "markersize / ms : Size of markers\n", + "markevery / mev : Mark every Nth marker \n", + " [None|integer|(startind, stride)]\n", + "alpha : Opacity of line plot (0 - 1.0), default = 1.0\n", + "title : Plot title, string\n", + "xlabel : X-axis label, string\n", + "ylabel : Y-axis label, string\n", + "xlim : X-axis limits - tuple. eg: xlim=(0,10). Set to None for auto\n", + "ylim : Y-axis limits - tuple. eg: ylim=(0,10). Set to None for auto\n", + "xscale : Set x axis scale ['linear'|'log'|'symlog']\n", + "yscale : Set y axis scale ['linear'|'log'|'symlog']\n", + " Only supports basic xscale/yscale functionality. Use \n", + " get_axes().set_xscale() if further customization is required\n", + "grid : Display axes grid. ['on'|'off']. See grid() for more options\n", + "colorcycle / cs: Set plot colorcycle to list of valid matplotlib\n", + " colors\n", + "fontsize : Global fontsize for all plots\n", + "\n", + "Legend Parameters:\n", + "------------------\n", + "showlegend : set to True to display legend\n", + "fancybox : True by default. Enables rounded corners for legend box\n", + "framealpha : Legend box opacity (0 - 1.0), default = 1.0\n", + "loc : Location of legend box in plot, default = 'best'\n", + "numpoints : number of markers in legend, default = 1.0\n", + "ncol : number of columns for legend. default is 1\n", + "markerscale : The relative size of legend markers vs. original. \n", + " If None, use rc settings.\n", + "mode : if mode is “expand”, the legend will be horizontally \n", + " expanded to fill the axes area (or bbox_to_anchor)\n", + "bbox_to_anchor : The bbox that the legend will be anchored. Tuple of\n", + " 2 or 4 floats\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##A note about interactive plotting mode in matplotlib\n", + "It is strongly recommended that `EasyPlot` be used in a Python/IPython shell with interactive matplotlib plotting turned on (`matplotlib.pyplot.ion()` turns this on). This provides the maximum flexibility when plotting with `EasyPlot` and also makes it easier to learn and understand the use of this matplotlib wrapper. In the case of non-interactive plotting, the blocking nature of `plt.show()` prevents dynamic modification and further additions to an existing plot. This issue can be partially alleviated by the `plt.show(block=False)` method call (this is experimental in Matplotlib).\n", + "\n", + "Note that if non-interactive plotting is the default, the user must call `plt.show()` after setting up the plot as desired using `EasyPlot`. `add_plot()` method calls on the EasyPlot instance are not supported after the figure is closed in non-interactive mode, and the `redraw()` instance method is also not supported in this mode (user can call `plt.draw()` instead)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Usage and Examples\n", + "[[back to section overview](#sections)]" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Import modules and setup\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from easyplot import EasyPlot\n", + "\n", + "x = np.linspace(0, 10, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple plot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eplot = EasyPlot(x, x**2, 'g--o', label=r\"$y = x^2$\", showlegend=True, xlabel='x',\n", + " ylabel='y', title='title', fontsize=14, markersize=10, alpha=0.6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Multiple plots in same figure (Interactive)\n", + "[[back to section overview](#sections)]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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w2GPw3HPwyiswZgxs2vT5ZEI5OcE/V322jSdW9bRP0A80Gwp8F/g+cBTvrMEc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\ndO4MP/uZd/+SS+C1104cbrh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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Note the use of plot parameter aliases and the figsize parameter\n", + "eplot = EasyPlot(x, x**2, label=r\"$y = x^2$\", figsize=(8,4), showlegend=True, \n", + " ncol=2, ms=10, ls=':', markeredgewidth=1.5, xlabel='x',\n", + " ylabel='y', title='title', color='b', marker='s')\n", + "eplot.add_plot(x, 0.15*x**3, label='$y = 0.15x^3$', c='c', ls='-', marker='D')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Using EasyPlot object as template for new plots\n", + "[[back to section overview](#sections)]\n", + "\n", + "The previous example defined an `EasyPlot` object `eplot` with various plot parameters set - `xlabel`, `ylabel`, `title`, `alpha`, `ncol`, `markersize` and `markeredgewidth` . We can examine the current set plot parameters for an `EasyPlot` object by accessing its `kwargs` instance variable" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ax': ,\n", + " 'dpi': 100.0,\n", + " 'fancybox': True,\n", + " 'fig': ,\n", + " 'figsize': (8, 4),\n", + " 'gs': None,\n", + " 'loc': 'best',\n", + " 'markeredgewidth': 1.5,\n", + " 'markersize': 10,\n", + " 'ncol': 2,\n", + " 'numpoints': 1,\n", + " 'showlegend': True,\n", + " 'title': 'title',\n", + " 'xlabel': 'x',\n", + " 'ylabel': 'y'}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Examine set plot parameters for eplot\n", + "eplot.kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that certain plot parameters such as `linestyle`, `marker`, `label` and `color` are considered **unique parameters** and do not carry over from one plot to another as they are typically unique to a specific plot. \n", + "\n", + "It is easy to use `eplot` as a template to generate a new plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eplot.new_plot(x, 1/(1+x), '-s', label=r\"$y = \\frac{1}{1+x}$\", c='#fdb462')\n", + "# Note that the plot reuses the axis labels, title and marker \n", + "# formatting from the previous eplot template" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Autoscaling plots\n", + "EasyPlot objects have an `autoscale()` instance method that can be called on the instance to reset the `xlim` and `ylim` properties to `None` and autoscale the axes. The method signature is listed below along with the default parameter values.\n", + "```python\n", + "def autoscale(self, enable=True, axis='both', tight=None):\n", + " \"\"\"Autoscale the axis view to the data (toggle).\n", + "\n", + " Convenience method for simple axis view autoscaling. It turns \n", + " autoscaling on or off, and then, if autoscaling for either axis is on,\n", + " it performs the autoscaling on the specified axis or axes.\n", + "\n", + " Arguments\n", + " =========\n", + " enable: [True | False | None]\n", + " axis: ['x' | 'y' | 'both']\n", + " tight: [True | False | None]\n", + " \"\"\"\n", + "``` " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting background grid\n", + "[[back to section overview](#sections)]\n", + "\n", + "easyplot provides two ways to display the background grid for the axes. \n", + "- To display a simple grid without any custom formatting, the plot parameter `grid='on'` can be passed to the easyplot object. Setting `grid='off'` turns the grid off." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ZuVjZuVB2DpZXEnZUIiJK/iIJk18WXP1v+wP+198B4IUjo42Bc6DsXCgeo+cN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\n7B7gI8AlwGHgt0A+cKW7t4c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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eplot.new_plot(x, 1/(1+x), '-s', label=r\"$y = \\frac{1}{1+x}$\", c='#fdb462', grid='on')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- For more advanced control of the grid, the easyplot `grid()` instance method is provided with a call signature of `grid(self, b=None, which='major', axis='both', **kwargs)` where `**kwargs` are passed to linespec of grid lines (eg: linewidth=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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SFsszz0Tv58W4Tb+/5iz8qR8xyrZidSekJ7A8ko4cFjvlMD7lMD7lML7QOczmkX8T0A7U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\np4m+i6cRfR//YGbVmQxKo/3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eplot.new_plot(x, 1/(1+x), '-s', label=r\"$y = \\frac{1}{1+x}$\", c='#fdb462')\n", + "eplot.grid(which='major', axis='x', linewidth=2, linestyle='--', color='b', alpha=0.5)\n", + "eplot.grid(which='major', axis='y', linewidth=2, linestyle='-', color='0.85', alpha=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Log/Linear/Symlog plots\n", + "[[back to section overview](#sections)]\n", + "\n", + "Plot parameters `xscale` and `yscale` can be passed to easyplot instances with any of the following values: `['linear'|'log'|'symlog']`" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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lOQoikj2KKgHHV/0BVv0e8vvi/cdj/SdA/9Ox/JKwKxRJOwoKIpI9iqqITPky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\niIiISEwKCiIiIhLT/wcCj2KlK+yMDAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eplot.new_plot(x, 1/(1+x), '-s', label=r\"$y = \\frac{1}{1+x}$\", c='#fdb462', yscale='log')\n", + "eplot.grid(which='minor', axis='both')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Modifying Plot color cycle\n", + "[[back to section overview](#sections)]\n", + "\n", + "easyplot provides the `colorcycle` plot parameter to specify the plot color cycle. If the `colorcycle` parameter is passed with every `add_plot` command, it will result in all plots using the first color of `colorcycle`. To avoid this, the `EasyPlot` object should be initialized with the `colorcycle` and other plot parameters without passing any x, y data as shown below. Subsequently, plots can be added using the `add_plot()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/cbook.py:136: MatplotlibDeprecationWarning: The set_color_cycle attribute was deprecated in version 1.5. Use set_prop_cycle instead.\n", + " warnings.warn(message, mplDeprecation, stacklevel=1)\n" + ] + }, + { + "data": { + "image/png": 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YtaEc0zzVGyLxGE+3neW17ssYVZX1w328q6eN4tB1aQi161HufAyR5ZqX/bSfRUoujHTz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85pc2R2oOGT6QGk5AymYcaUpBZ/XEZk+ibtDzIXKXmkWehzSgkgMROafMJ9/07gTfKnbb1fWfTG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\nFYTmyRj4nXfLY1lO4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BidmUkth3dXprbMsRMK6TXkYmYZ2Rs5scPV+4CeAeJfM8fEgs2c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bK/IGDRmRD3a2laFWHugSKcWr709srpZwm1FM/IrICYQQ7Nq1S9FJCoVCGBpiN9kFAI7GY4q2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\ncTVrNKOu1kOHDhGPx7Neq6iooK6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Setup\n", + "colors = [\"#66c2a5\",\"#fc8d62\",\"#8da0cb\",\"#e78ac3\",\"#a6d854\",\"#ffd92f\",\"#e5c494\",\"#b3b3b3\"]\n", + "x = np.linspace(0,10,200)\n", + "\n", + "# Demo of color cycle\n", + "# Note the use of EasyPlot constructor with no x,y data to initialize colorcycle prior to\n", + "# adding plots to the figure\n", + "sinplot = EasyPlot(xlabel=r'$\\sin (3\\pi x/L)$', ylabel='$Amplitude$', fontsize=16, colorcycle=colors,\n", + " figsize=(10,5), linewidth=3)\n", + "\n", + "for index in range(8):\n", + " sinplot.add_plot(x, np.sin(3*np.pi*x/10 + index*np.pi/7))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Multiple plots in same figure iteratively using iter_plot\n", + "[[back to section overview](#sections)]\n", + "\n", + "The `EasyPlot` class has a very useful `iter_plot()` method to iterate through `x`, `y` data stored in dictionaries or 2D arrays and plot them in a figure using a single method call to `iter_plot`. The method signature of `iter_plot` is as follows:\n", + "``` python\n", + "def iter_plot(self, x, y, mode='dict', **kwargs):\n", + " \"\"\"\n", + " Plot multiple plots by iterating through x, y and parameter lists\n", + "\n", + " Arguments:\n", + " ==========\n", + " x : x values. 1D List/Array, Dictionary or Numpy 2D Array\n", + " y : y values. Dictionary or 2D Python array (List of Lists where each\n", + " sub-list is one set of y-data) or Numpy 2D Array\n", + " mode : y, labels and other parameters should either be a Dictionary\n", + " or a 2D Numpy array/2D List where each row corresponds to a \n", + " single plot ['dict'|'array']\n", + " **kwargs : Plot params as defined in __init__ documentation.\n", + " Params can either be:\n", + " scalars (same value applied to all plots),\n", + " dictionaries (mode='dict', key[val] value applies to each plot)\n", + " 1D Lists/1D Numpy Arrays (mode='array', param[index] applies to each\n", + " plot)\n", + " \"\"\"\n", + "```\n", + "The examples below demonstrate the use of `iter_plot` to generate multiple plots from a dataset using both `mode` settings, i.e., `mode='dict'` and `mode='array'`. Note that single value plot parameters that are passed to `iter_plot` (such as `linewidth`) are broadcast and applied to all plots in the figure." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup the x, y data and plot parameters for both modes\n", + "x = np.linspace(0, 10, 11)\n", + "dict_keys = ['x2', 'x3', 'x4']\n", + "labels_list = ['$y = x^2$', '$y = 0.1x^3$', '$y = 0.01x^4$']\n", + "markers_list = ['s', 'o', 'D']\n", + "linestyle_list = ['-', '--', ':']\n", + "\n", + "y_dict, marker_dict, labels_dict, linestyle_dict = {}, {}, {}, {}\n", + "x_list, y_list = [], [] # List of x and y data sets for mode='array'\n", + "\n", + "# Populate dict and list variables with data set\n", + "for ind, key in enumerate(dict_keys):\n", + " marker_dict[key] = markers_list[ind]\n", + " labels_dict[key] = labels_list[ind]\n", + " linestyle_dict[key] = linestyle_list[ind]\n", + " y_dict[key] = (0.1**ind)*x**(ind+2)\n", + " y_list.append((0.1**ind)*x**(ind+2)) \n", + " x_list.append(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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wVdZJDOrU2fEVuqB2jYXdGXls2XmSN9bsZc+BvHrPuVxJkiCEC7Kzs3n99deJ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\na9fAmIRETyIiYInh6OGDnDvt3DzbeyxyvG4sAbfVM+T/f1qcxawcwRK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Demonstrate iter_plot using mode='dict'\n", + "eplot = EasyPlot(xlabel=r'$x$', ylabel='$y$', fontsize=16,\n", + " colorcycle=[\"#66c2a5\",\"#fc8d62\",\"#8da0cb\"], figsize=(8,5))\n", + "eplot.iter_plot(x, y_dict, linestyle=linestyle_dict, marker=marker_dict,\n", + " label=labels_dict, linewidth=3, ms=10, showlegend=True, grid='on')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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wVdZJDOrU2fEVuqB2jYXdGXls2XmSN9bsZc+BvHrPuVxJkiCEC7Kzs3n99deJ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\npRwaEtQ1rcvfy7GmGg42VnG4sZrOK20+FAMGb8N8k6+bwJF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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Demonstrate iter_plot using mode='array'. Both x_list and y_list are 2D arrays\n", + "eplot = EasyPlot(xlabel=r'$x$', ylabel='$y$', fontsize=16, \n", + " colorcycle=[\"#66c2a5\",\"#fc8d62\",\"#8da0cb\"], figsize=(8,5))\n", + "eplot.iter_plot(x_list, y_list, mode='array', linestyle=linestyle_list, marker=markers_list, \n", + " label=labels_list, linewidth=3, ms=10, showlegend=True, grid='on')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Advanced plotting\n", + "[[back to section overview](#sections)]\n", + "\n", + "`EasyPlot` objects provide access to their `figure` and `axes` objects via the `get_figure()` and `get_axes()` methods. These methods can be used in conjunction with regular object oriented matplotlib plotting methods (for example the set and get methods on the axes object) to build more complex and elaborate plots as shown in the examples below. The `line_list` instance variable can also be accessed to obtain a list of [`Line2D`](http://matplotlib.org/1.3.1/api/artist_api.html#matplotlib.lines.Line2D) items corresponding to the plots in the figure. These can also then be manipulated using standard matplotlib methods. **When plotting interactively, the `redraw()` method must be called on the easyplot object after any manipulation of the axes and figure objects in order to update the plot display with the latest changes.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Using easyplot with subplots\n", + "[[back to section overview](#sections)]\n", + "\n", + "To create figures with subplots while taking advantage of an easyplot instance, the axes and figure can be created by the user using `pyplot.subplots`, `gridspec` or other common methods. The reference to the figure and and one of the subplot axes can be passed to the easyplot instance method - `new_plot()` to obtain the desired results as demonstrated below. The example below reuses the `sinplot` object from [this example](#colorcycle) as a template to easily generate new plots with desired axis labels and plot parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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J1DPfuYVw0p1Qtcl+fvOo0W3bOd5gIg3g0ONss2JM1fBqM7P7gpn91MyuMbMh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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Reuses sinplot template from one of the previous examples for labels, linewidth and fontsize\n", + "x = np.linspace(0, 10, 200)\n", + "fig, axes = plt.subplots(2, 1, figsize=(10,6)) # Create fig and axes for subplots externally\n", + "\n", + "# Supply fig and axes instance to EasyPlot object\n", + "sinplot.new_plot(x, np.sin(3*np.pi*x/10 + np.pi/8), fig=fig, ax=axes[0], color=\"#fc8d62\") \n", + "sinplot.new_plot(x, np.sin(3*np.pi*x/10 + 9*np.pi/8), fig=fig, ax=axes[1], color=\"#66c2a5\")\n", + "fig.set_tight_layout(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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V5gvdQLgM+zwFfA94S0T+EXd+7D8D+3EvEIaj3+M+Mf4VqBeRi0/62oFwG/5R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\nondimt9xzAVIjY3nnqGTKWu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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Alternately you can now use the convenience methods of make_subplots and set_subplot to get the same effect as follows\n", + "esub = EasyPlot()\n", + "esub.make_subplots(2,1)\n", + "esub.set_subplot(0,0)\n", + "esub.add_plot(x, np.sin(3*np.pi*x/10 + np.pi/8), color=\"#fc8d62\")\n", + "esub.set_subplot(1,0)\n", + "esub.add_plot(x, np.sin(3*np.pi*x/10 + 9*np.pi/8), color=\"#66c2a5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/ipykernel/__main__.py:9: RuntimeWarning: divide by zero encountered in reciprocal\n" + ] + }, + { + "data": { + "image/png": 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dCd5WJ3hmZmZWZ7mcgyepsFr8MmADyUUWl5Dck/HL1f68PqdoneCZmZlZneUy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\nyp8TxPsbM5thZhOAfwamR15vadP2FwSHdL8E3Bh5LgZKIkn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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Another example\n", + "fig, axes = plt.subplots(figsize=(9,9), nrows=2, ncols=2) # Create fig and axes\n", + "\n", + "# Setup EasyPlot object with common plot parameters\n", + "eplot = EasyPlot(xlabel='x', ylabel='y(x)', title='Plot title', fs=14, lw=3)\n", + "\n", + "# Loop through subplot axes and use eplot template to create new plots\n", + "for (x_ind,y_ind), ax in np.ndenumerate(axes):\n", + " eplot.new_plot(x, x**(x_ind+y_ind-1), fig=fig, ax=ax, c=\"#8da0cb\") \n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Custom plot modifications via axes instance\n", + "[[back to section overview](#sections)]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Setup EasyPlot object with common plot parameters\n", + "x = np.linspace(0, 10, 11)\n", + "eplot = EasyPlot(x, x**2, xlabel='x', ylabel='y(x)', figsize=(6,5),\n", + " title='Plot title', fs=14, lw=3, xlim=(0,11), c=\"#fc8d62\")\n", + "ax = eplot.get_axes()\n", + "\n", + "# Modify plot by direct manipulation of axes instance\n", + "ax.bar(x, x**2, align=\"center\", width=0.5, alpha=0.6, color=\"#a6d854\")\n", + "ax.set_xticks([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n", + "ax.set_xticklabels([r'$\\alpha$', r'$\\beta$', r'$\\gamma$', r'$\\delta$', r'$\\epsilon$',\n", + " r'$\\chi$', r'$\\nu$', r'$\\mu$', r'$\\omega$', r'$\\phi$'], fontsize=18)\n", + "eplot.redraw() # Update plot canvas with axes modifications" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/easyplot_docs.ipynb b/docs/easyplot_docs.ipynb index 3e5f73c..a9fe61d 100644 --- a/docs/easyplot_docs.ipynb +++ b/docs/easyplot_docs.ipynb @@ -1,986 +1,1075 @@ { - "metadata": { - "name": "", - "signature": "sha256:1a783162107daa18c2cd89074e4af7f2dd6fe51dcd087651c91ae656ff3537a6" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#easyplot\n", - "**easyplot:** A matplotlib wrapper written in Python to enable fast and easy creation of reusable plots.\n", - "\n", - "by [Sudeep Mandal](http://www.sudeepmandal.com)\n", - "\n", - "The EasyPlot module provides a thin wrapper to matplotlib, enabling fast and easy generation of beautiful, annotated plots with minimal code. It also enables the reuse of EasyPlot instances to generate new plots that maintain state from previous plots allowing for quick and easy generation of multiple plots of a similar type. EasyPlot supports all commonly used plot parameters and allows access to the underlying figure and axes instances to allow the user to further customize the generated plots if necessary.\n", - "
\n", - "I'd love to hear your comments and/or suggestions. You can get in touch with me via [twitter](https://twitter.com/hamsterhuey), [email](mailto:sudeepmandal@gmail.com) or [google+](https://plus.google.com/u/0/105292596991480463202/)\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Sections\n", - "- [Requirements](#requirements)\n", - "- [Installation](#installation)\n", - "- [Motivation and Background](#motivation)\n", - "- [Features](#features)\n", - "- [Documentation](#documentation)\n", - " - [EasyPlot instance methods](#methods)\n", - " - [EasyPlot instance variables](#variables)\n", - " - [List of supported plot parameters](#plot_params)\n", - " - [Interactive/Non-Interactive plotting mode](#interactive_mode)\n", - "- [Usage and Examples](#usage)\n", - " - [Simple Plot](#simple_plot)\n", - " - [Multiple plots in the same figure (Interactive)](#multiple_plots)\n", - " - [Using EasyPlot object as template for new plots](#easyplot_template)\n", - " - [Autoscaling Plots](#autoscale)\n", - " - [Setting background grid](#grid)\n", - " - [Log/Linear/Symlog plots](#log_linear_scale)\n", - " - [Modifying Plot color cycle](#colorcycle)\n", - " - [Multiple plots in same figure iteratively using iter_plot](#iter_plot)\n", - "- [Advanced plotting](#advanced_plotting)\n", - " - [Using easyplot with subplots](#subplots)\n", - " - [Custom plot modifications via axes instance](#ax_mods)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Requirements\n", - "- Python 2.7.2+\n", - "- matplotlib\n", - "\n", - "Use of the [IPython](http://ipython.org/) shell is strongly recommended with this library (and matplotlib plotting in general). The `%matplotlib` magic command in IPython (or starting IPython using `ipython --matplotlib`) implements a number of backend tweaks to enable robust, interactive plotting using matplotlib." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Installation\n", - "[[back to section overview](#sections)]\n", - "\n", - "You can use the following commands to install EasyPlot:\n", - "\n", - "`pip install easyplot`\n", - "\n", - "or\n", - "\n", - "`easy_install easyplot`\n", - "\n", - "Alternatively, you could download the package manually from the Python Package Index: https://pypi.python.org/pypi/EasyPlot, unzip it, navigate into the package, and use the command:\n", - "\n", - "`python setup.py install`\n", - "\n", - "or\n", - "\n", - "`pip install .` " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Motivation and background\n", - "[[back to section overview](#sections)]\n", - "\n", - "Setting up aesthetically pleasing plots with plot titles, axes labels, etc requires several lines of boilerplate code in vanilla matplotlib. As an example, creating a basic plot in matplotlib requires the following lines of code: \n", - "```python\n", - "fig, ax = plt.subplots()\n", - "ax.plot(x, x**2, 'b-o', label=\"y = x**2\")\n", - "ax.plot(x, x**3, 'r--s', label=\"y = x**3\")\n", - "ax.legend(loc='best')\n", - "ax.grid()\n", - "ax.set_xlabel('x')\n", - "ax.set_ylabel('y')\n", - "ax.set_title('title')\n", - "```\n", - "\n", - "Pylab alleviates some of this, but still requires calls to a number of different functions that are commonly used (such as xlabel, xlim, etc.). More complicated plots can require several more lines of code. Typing all this code every time to generate plots gets tedious very quickly. This situation is further exacerbated when working in an IPython Notebook where all plots typically need to be labeled, annotated and looking their best. Having several lines of code preceeding every plot in a notebook can break the flow of the document and distract from the code/concepts being presented by the author. Furthermore, oftentimes, plots with similar labels and formatting need to be generated repeatedly with different datasets. Generating these sets of plots would require retyping these same lines of boilerplate code across different sections of your code/notebook.\n", - "\n", - "Easyplot is my attempt to address these issues and make generating quick, pleasant looking, annotated plots a bit easier. In keeping with [DRY philosophy](http://en.wikipedia.org/wiki/Don%27t_repeat_yourself), `easyplot` enables the creation of an `EasyPlot` object that maintains state information of all plot parameters passed to it in order to generate a plot. This can then be easily reused to generate new plots with the user only having to supply any additional plot parameters, or those parameters he or she wishes to override from the previous plot.\n", - "\n", - "Easyplot supports a large number of standard plot parameters that most users typically deal with when plotting in matplotlib. Additionally, it provides methods to access the figure and axes instance for the latest plot, enabling users to perform more custom plot modifications that are not directly supported by easyplot. It also supports interactive plotting where additional plot parameters can be passed to the current plot using the `update_plot` method. The plot above can be generated using `easyplot` as follows:\n", - "```python\n", - "eplot = EasyPlot(x, x**2, 'b-o', label='y = x**2', showlegend=True,\n", - " xlabel='x', ylabel='y', title='title', grid='on')\n", - "eplot.add_plot(x, x**3, 'r--s', label='y = x**3')\n", - "```\n", - "Along with the reduced typing, easyplot enables the consolidation and passing of all plot parameters into a single plot call. This is already quite handy, but the real benefit is evident when one needs to generate a new plot with the same plot parameters (such as axis labels and title) but with new data:\n", - "```python\n", - "eplot.new_plot(x, 1/x, 'g-D', label='y = 1/x')\n", - "```\n", - "\n", - "\n", - "`EasyPlot` also provides an `iter_plot()` method that iterates through x, y data and plot parameters that are provided in a list or dictionary format to automatically generate an annotated, multi-line plot with a single statement:\n", - "```python\n", - "eplot = EasyPlot(xlabel=r'$x$', ylabel='$y$', fontsize=16,\n", - " colorcycle=[\"#66c2a5\",\"#fc8d62\",\"#8da0cb\"], figsize=(8,5))\n", - "eplot.iter_plot(x, y_dict, linestyle=linestyle_dict, marker=marker_dict,\n", - " label=labels_dict, linewidth=3, ms=10, showlegend=True, grid='on')\n", - "```\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Features\n", - "[[back to section overview](#sections)]\n", - "- Access to a large number of the most used matplotlib plot parameters under a unified wrapper class\n", - "- Plot parameter aliases supported. Can be extended by user for arbitrary alias definitions for various plot parameters\n", - "- Ability to use `EasyPlot` objects as templates to rapidly generate annotated plots of a similar type\n", - "- `iter_plot()` method to easily iterate through x, y datasets and plot multiple plots with a single method call\n", - "- Draggable legend when using GUI backends (eg: qt, wx, etc.)\n", - "- Provides access to underlying figure, axes and line2D objects for advanced plot customization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Documentation\n", - "[[back to section overview](#sections)]\n", - "\n", - "EasyPlot consists of an `EasyPlot` class to create `EasyPlot()` objects in order to generate matplotlib plots. The object constructor allows for passing all commonly used plot parameters (such as `xlabel`, `ylabel`, `title`, `markersize`, `linewidth`, etc.) and the `x`, `y` data and an optional format string as arguments in order to generate an annotated plot via a single statement such as:\n", - "```python\n", - "fftplot = EasyPlot(freq, amplitude, 'g-o', markersize=9, linewidth=3, xlabel='Frequency (Hz)',\n", - " ylabel='Amplitude', label='FFT Data', showlegend=True)\n", - "```\n", - "If no `x`, `y` data is provided to the `EasyPlot` constuctor, creation of a figure and axes instance for the `EasyPlot` object is deferred and the plot parameters that are passed to the constructor are used to initialize the `EasyPlot` object. \n", - "\n", - "Apart from the usual plot parameters, `EasyPlot` constructor also takes the following special keyword arguments:\n", - "\n", - "`fig` : an optional reference to a figure object supplied by the user
\n", - "`ax` : an optional reference to an axes object (linked to `fig`) to use for displaying the plots. The [subplots example](#subplots) demonstrates the use of `fig` and `ax` plot parameters for custom plotting routines
\n", - "`figsize` : the size of the plot figure
\n", - "`showlegend` : set to True or False to display the plot legend\n", - "\n", - "The `EasyPlot` object also retains state information of most plot parameters passed to it, thus allowing the object to be reused as a template for generating new plots with similar formatting/labeling. The only parameters whose values are not persistent are `color`, `marker`, `linestyle` and `label` as they are assumed to be unique to a single plot.\n", - "\n", - "`EasyPlot` objects also provide access to their `figure`, `axes` and `Line2D` instances via the `get_figure()`, `get_axes()` methods and `line_list` instance variable respectively." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### EasyPlot instance methods\n", - "The main instance methods for `EasyPlot` objects are:\n", - "
\n", - "`add_plot(*args, **kwargs)` : This is the main instance method to add additional plots and plot parameters to an existing plot. `*args` is a variable length argument, allowing for `x`, `y` pairs with an optional format string of the form `'b-o'`. The optional format string is a shorthand for specifying the plot color, linestyle and marker type. The [matplotlib docs](http://matplotlib.org/1.3.1/api/pyplot_api.html#matplotlib.pyplot.plot) provide more information regarding the format string notation. `**kwargs` are a list of named plot parameters as explained in the [next section](#plot_params).\n", - "
\n", - "`update_plot(**kwargs)` : Instance method to update plot parameters only (example: `xlabel`, `title`, etc.). This method is typically only useful when plotting interactively using an interactive matplotlib backend.\n", - "
\n", - "`new_plot(*args, **kwargs)` : Create a new plot using the plot parameters of the existing `EasyPlot` instance as a template (i.e., plot parameter settings carry over from the `EasyPlot` instance to the new plot)\n", - "
\n", - "`iter_plot(x, y, mode='dict', **kwargs)` : This method can be used to plot multiple line/scatter plots with a single statement as long as `x`, `y`, and any relevant plot parameters that change for each plot are passed in as either a dictionary with a common set of keys (`mode='dict'`) or a set of lists/arrays corresponding to each data set to be plotted (`mode='array'`).\n", - "
\n", - "`autoscale(enable=True, axis='both', tight=None)` : Set autoscale options for `x`, `y` or `both` axes\n", - "
\n", - "`grid(**kwargs)` : Equivalent to matplotlib axes.grid() method and accepts same set of keyword arguments. Enables more custom control over the plot grid.\n", - "
\n", - "`get_figure()` : Returns reference to the Figure object for the instance\n", - "
\n", - "`get_axes()` : Returns reference to the axes object for the instance\n", - "
\n", - "`redraw()` : Forces a redraw of the plot canvas. Must be used if custom modifications are made to the plot external to easyplot via direct access to the figure and axes handles. **Note: `redraw()` is only supported when plotting in interactive mode (i.e. `plt.isinteractive() == True`)**.\n", - "
\n", - "`set_fontsize(font_size)` : Modify global setting for plot font-size\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### EasyPlot instance variables\n", - "The main instance variables for `EasyPlot` objects are:\n", - "
\n", - "`self.kwargs` : A dictionary containing a list of all existing plot parameters for an `EasyPlot` object. The user should not modify this directly. This variable should be accessed in a read-only fashion for the purposes of examining the state of an `EasyPlot` object.\n", - "
\n", - "`self.line_list` : A list of [`Line2D`](http://matplotlib.org/1.3.1/api/artist_api.html#matplotlib.lines.Line2D) items corresponding to all plots of the `EasyPlot` object. These can be manipulated using standard matplotlib methods outside of `easyplot` for [advaced plotting](#advanced_plotting)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### List of supported plot parameters\n", - "```\n", - "Plot Parameters:\n", - "----------------\n", - "fig : figure instance for drawing plots\n", - "ax : axes instance for drawing plots (If user wants to supply axes,\n", - " figure externally, both ax and fig must be supplied together)\n", - "figSize : tuple of integers ~ width & height in inches\n", - "label : Label for line plot as determined by *args, string\n", - "color / c : Color of line plot, overrides format string in *args if\n", - " supplied. Accepts any valid matplotlib color\n", - "linewidth / lw : Plot linewidth\n", - "linestyle / ls : Plot linestyle ['-','--','-.',':','None',' ','']\n", - "marker : '+', 'o', '*', 's', 'D', ',', '.', '<', '>', '^', '1', '2'\n", - "markerfacecolor / mfc : Face color of marker\n", - "markeredgewidth / mew :\n", - "markeredgecolor / mec : \n", - "markersize / ms : Size of markers\n", - "markevery / mev : Mark every Nth marker \n", - " [None|integer|(startind, stride)]\n", - "alpha : Opacity of line plot (0 - 1.0), default = 1.0\n", - "title : Plot title, string\n", - "xlabel : X-axis label, string\n", - "ylabel : Y-axis label, string\n", - "xlim : X-axis limits - tuple. eg: xlim=(0,10). Set to None for auto\n", - "ylim : Y-axis limits - tuple. eg: ylim=(0,10). Set to None for auto\n", - "xscale : Set x axis scale ['linear'|'log'|'symlog']\n", - "yscale : Set y axis scale ['linear'|'log'|'symlog']\n", - " Only supports basic xscale/yscale functionality. Use \n", - " get_axes().set_xscale() if further customization is required\n", - "grid : Display axes grid. ['on'|'off']. See grid() for more options\n", - "colorcycle / cs: Set plot colorcycle to list of valid matplotlib\n", - " colors\n", - "fontsize : Global fontsize for all plots\n", - "\n", - "Legend Parameters:\n", - "------------------\n", - "showlegend : set to True to display legend\n", - "fancybox : True by default. Enables rounded corners for legend box\n", - "framealpha : Legend box opacity (0 - 1.0), default = 1.0\n", - "loc : Location of legend box in plot, default = 'best'\n", - "numpoints : number of markers in legend, default = 1.0\n", - "ncol : number of columns for legend. default is 1\n", - "markerscale : The relative size of legend markers vs. original. \n", - " If None, use rc settings.\n", - "mode : if mode is \u201cexpand\u201d, the legend will be horizontally \n", - " expanded to fill the axes area (or bbox_to_anchor)\n", - "bbox_to_anchor : The bbox that the legend will be anchored. Tuple of\n", - " 2 or 4 floats\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##A note about interactive plotting mode in matplotlib\n", - "It is strongly recommended that `EasyPlot` be used in a Python/IPython shell with interactive matplotlib plotting turned on (`matplotlib.pyplot.ion()` turns this on). This provides the maximum flexibility when plotting with `EasyPlot` and also makes it easier to learn and understand the use of this matplotlib wrapper. In the case of non-interactive plotting, the blocking nature of `plt.show()` prevents dynamic modification and further additions to an existing plot. This issue can be partially alleviated by the `plt.show(block=False)` method call (this is experimental in Matplotlib).\n", - "\n", - "Note that if non-interactive plotting is the default, the user must call `plt.show()` after setting up the plot as desired using `EasyPlot`. `add_plot()` method calls on the EasyPlot instance are not supported after the figure is closed in non-interactive mode, and the `redraw()` instance method is also not supported in this mode (user can call `plt.draw()` instead)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Usage and Examples\n", - "[[back to section overview](#sections)]" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Import modules and setup\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "from easyplot import EasyPlot\n", - "\n", - "x = np.linspace(0, 10, 10)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simple plot" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "eplot = EasyPlot(x, x**2, 'g--o', label=r\"$y = x^2$\", showlegend=True, xlabel='x',\n", - " ylabel='y', title='title', fontsize=14, markersize=10, alpha=0.6)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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1zapVqwSlUin8/e9/F/Ly8oTJkycLQUFBwq1bt2z25WQ5SUTUpNLqUuH/ff//hHM3zola\nR2uPnU51NdfEiRPRo0cPbNmyxbpt+vTp+PXXX/HNN99AEAQEBQVh7ty5SElJAQDU1NRApVJh9erV\nSExMtD6PVxcRkTNqbJ2/oQOGYnfpbowOH40xYWNErc8l7mcyYcIEpKenQ6fTISoqCvn5+Thw4AAW\nLVoEALh48SL0er3N8iJeXl6Ij49Hbm6uTZgQETmbBiuQB8lwyXQJf937V3Sq7IQuUV1ED5PWcqow\nefXVV3HlyhX0798fHh4eqK2txeLFi/HKK68AAEpKSgAAAQG2N4RRqVS4du2aw+slImouo9GIvNI8\nyKJk8MXdm+3dNN6EpYcFUdFRyDuXB6PR2KYrkTuKU4XJunXrsGXLFuzYsQPR0dE4ceIEkpOTERoa\nihkzZjT53MaWZE5NTbV+n5CQgISEhDaumIioeXQFOhiUBpsgAQB/b3909eoKqUSKKmUVdAU6xETH\nOKyu7OxsZGdn270fpxozCQgIwOLFi21uArVixQps3boVBQUFKCwsRN++fXHs2DEMHTrU2ubxxx+H\nSqWyGWvhmAkROZNNOzbhB88fIJM3cddWkxkPGh/ErD/NcmBltlxiBrwgCA1mUkulUusbCwsLQ2Bg\nILKysqyP19TUICcnByNGjHBorURELVFcUdxkkACATC5DcUWxgypqW051muvpp5/GqlWrEBYWhgED\nBuDEiRN4//33MX36dAB1iTlv3jykpaWhX79+iIiIwPLly6FUKjF16lSRqyciuje1nxpFpqL79kzU\nfmoHVtV2nCpM3n//fXTu3BlJSUnQ6/VQq9VITEzE22+/bW2zcOFCGAwGJCUloaysDBqNBllZWfDx\n8RGxciKipmliNDiQcwDlvuUIUgZBJmkYKoZSAzQj2/fmV+3FqcZM2hLHTIjImdy5cwcTlk2AIcyA\ngaqBkEoajjKYdWZkzM0Q9Woul5hnQkTkqrIvZ6OTtBN6lfXCbfPtunkmchnMJjMMpQYobikw0H9g\nh7wsGGCYEBG1u6OXj+LgpYPY/uZ2eEu9786AL62bAa8ZqUFURFSHDRKAp7mIiNpVYVkhNhzbgAUP\nLYBa6fyD6609djJMiIjakdliRuntUgT6BopdSrMwTOphmBARtZxLTFokIqKOiWFCRER2Y5gQEbWR\nWkstvsz/EjW1NWKX4nC8NJiIqA0IgoAtJ7bAIljgKeu4l/i2FnsmRER2EgQBX+R/gco7lZgRO6PR\n2e2uzv3eMRFRG8u6kIWzN85i9vDZkMvkYpcjCoYJEZEddDd0OHjpIOY+OBfecm+xyxEN55kQEdnB\nIlhQeacSXby6iF1Km+CkxXoYJkRELcdJi0REJBqGCRER2Y1hQkTUTDW1Ndh6cqtbTkq8H4YJEVEz\n1FpqseHYBnSSdUInWSexy3E6DBMiovv4bXa7t9wbUwZOgUQiEbskp8MwISJqgiAI+PzM5249u705\n+KkQETUh73oedDd1bj27vTk4z4SIqAmCIMBQa3Cb2e2ctFgPw4SIqOU4aZGIiETDMCEiIrsxTIiI\n/qvyTiU++vdHMJqNYpfS4TBMiIhQN7t93Q/rEN413C3vlGgvhgkRub3fZrf36doHE/pOELucDolh\nQkRujbPb2wbDhIjc2smSk5zd3gY4z4SI3JogCKi11HJ2+39x0mI9DBMiopbjpEUiIhKN04VJcXEx\npk+fDpVKBYVCgejoaBw6dMimTWpqKoKDg+Ht7Y0xY8YgPz9fpGqJyJkZjUacPnMam3ZswrKNy7Bp\nxyb8nPczjEbOI2lrTnWaq7y8HEOGDEF8fDxee+01+Pv7o7CwEGq1Gv369QMApKenY8WKFcjMzERk\nZCSWLVuGnJwc6HQ6+Pr6WvfF01xE7m1t5lrklebBoDRA4a+ATC5DlaEK+efyEesVi0H+gzBv+jyx\ny3Q6rT12erRDLa327rvvIjg4GFu3brVu6927t/V7QRCwdu1apKSk4JlnngEAZGZmQqVSYdu2bUhM\nTHR0yUTkhIxGI/JK8yCLksEXdX9kGi1G5Jflo1efXpD7ypGny4PRaISnJycotgWnOs21a9cuxMXF\nYcqUKQgICEBsbCzWr19vffzixYvQ6/UYN26cdZuXlxfi4+ORm5srRslE5IR0BToYlAbrz2bBjLzr\neQj0DUSgbyAAwKA0QFegE6tEl+NUYVJYWIiMjAz07dsXWVlZSE5OxhtvvGENlJKSEgBAQECAzfNU\nKpX1MSIi7WktFP4KAHVBcqb0DDp7dkbPzj2tbRT+CmhPa8Uq0eU41Wkui8WCuLg4rFixAgAwePBg\nFBQUYP369UhKSmryuZy1SkS/Ka4ohixIBgC4cfsGvGRe6NOtDyS4e5yQyWUoLi0Wq0SX41RhEhQU\nhAEDBths69evH4qKigAAgYF13VO9Xo+QkBBrG71eb33s91JTU63fJyQkICEhoe2LJiKno/ZTo8hU\nBJlchgCfAKh8VDZBAgBmkxlqP7VIFTqP7OxsZGdn270fpwqThx9+GGfPnrXZdu7cOYSGhgIAwsLC\nEBgYiKysLAwdOhQAUFNTg5ycHKxevbrB/n4fJkTkPjQxGmTnZMM3qG7wvX6QAICh1ADNSI2jS3M6\n9f/QXrp0aav241RjJvPnz4dWq0VaWhrOnz+PL774Ah9++KH1FJdEIsG8efOQnp6Or776Cnl5eXjp\npZegVCoxdepUkasnImcRFREFxS1Fk20UtxSIiohyUEWuz6l6JsOGDcOuXbuwaNEivPPOO+jduzeW\nL1+O2bNnW9ssXLgQBoMBSUlJKCsrg0ajQVZWFnx8fESsnIicQZmhDCaLCSofFQb6D0Seznaeidlk\nhqHUAMUtBQb6D+RlwW3IqSYttiVOWiRyLzdu38D7R9/H+L7jMar3KAB18010BTpoT2tRXFEMtZ8a\nmhgNoiKiGCT3wIUe62GYELkPfZUea7VrMb7veIwOHS12OR2aS8yAJyJqqauVV/HBDx/gmX7P4KGe\nD4ldjttimBBRh1VlrMIHP3yA5wY8h+HBw8Uux63xNBcRdWjXq69D5aMSuwyXwTGTehgmREQtx5tj\nERGRaBgmRNRhmMwmsUuge2CYEFGHcPzacaQfSYdFsIhdCjWCYUJETk97RYudZ3bipQdeglTCw5Yz\n4qXBROTUDl86jL0FezFfMx9qJVf5dVYMEyJyWvsv7sc/C/+JBQ8t4OW/To6XBhOR0zp6+Sgiu0ei\nu3d3sUtxG5xnUg/DhIio5TjPhIiIRMMwISIiuzUrTJ566il88803sFh4fTcRtT2LYMEXZ75ASVWJ\n2KVQKzUrTHx9ffGnP/0JISEhSElJQUFBQXvXRURuwiJYsOXEFlyuvIyuXl3FLodaqdkD8BUVFdi2\nbRs++eQTHD9+HCNHjsTMmTMxefJkKBRN32tZDByAJ3J+tZZabP5pM4xmI14Z9grkMrnYJbk9h17N\nlZeXh82bN2PDhg3w8vLClClTkJycjAEDBrS4gPbCMCFybiazCRuPb4RMIsOsobPgIeW0N2fgsKu5\nrl27hq+//hp79uyBXC7Hs88+i6KiIgwaNAjvvfdeiwsgIvd09sZZKDwUSByayCBxAc3qmRiNRnz9\n9df45JNPsG/fPgwZMgSzZs3C888/D19fXwDA7t27MW3aNJSXl7d70c3BngmR8xMEARKJROwy6Hfa\n9R7wQUFBsFgseOGFF5Ceno5BgwY1aDNq1Ch06dKlxQUQkftikLiOZvVMPv30U0yePBleXl6OqKlN\nsGdCRNRyXE6lHoYJkfOoqKlA5Z1K9PTrKXYpdB9cToWInNKvhl+xOnc1dDd1YpdC7YiXUBCR3YxG\nI3QFOmhPa1FcUQy1nxqaGA26B3fH+uPrMSZsDMaGjxW7TGpHPM1FRHZZm7kWeaV5MCgNUPgrIJPL\nYDaZ8Wvxr7hw4QJG9RyFj+Z/JHaZ1Ew8zUVEDmc0GpFXmgdZlAy+Qb6QyWV1D3gAhbJChMWFocZc\nA6PRKG6h1O4YJkTUaroCHQxKQ4PtMokMgwMGI8AnAAalAboCjpe4OoYJEbWa9rQWCv/G1+bz8qib\nSqDwV0B7WuvIskgEDBMiarXiiuK7p7buQSaXobii2EEVkVgYJkTUamo/NUxGE6qMVfdsYzaZofZT\nO7AqEgPDhIhaLToyGj/l/4Tiqnv3PAylBmhiNA6sisTAeSZE1CqFZYX4e8nfEVgbiF7det2zneKW\nAlERUQ6sjMTgtD2TlStXQiqVYs6cOTbbU1NTERwcDG9vb4wZMwb5+fkiVUjkvnIv5yLjWAamDZ2G\n8eHjYdFZUHWtCmaTGUDdqa2qa1Uw68wY6D8Qnp6eIldM7c0peyZarRabNm3CoEGDbFYVTU9Px5o1\na5CZmYnIyEgsW7YMjz76KHQ6nXUpfCJqXzdu38C+C/vw+kOvQ61UY9D0QbYz4Ev/OwN+pAZREVEM\nEjfhdDPgKyoqMHToUGzevBmpqamIiYnBunXrIAgCgoKCMHfuXKSkpAAAampqoFKpsHr1aiQmJtrs\nhzPgidqPRbBAKnHaExtkB5eZAZ+YmIjnnnsOo0ePtnlDFy9ehF6vx7hx46zbvLy8EB8fj9zcXDFK\nJXJbDBKqz6lOc23atAmFhYXYtm0bANsb55SUlAAAAgICbJ6jUqlw7do1xxVJREQNOE2Y6HQ6vPnm\nm8jJyYFMVjcJShCEZnW3eLc2orYnCAK+LfgWIZ1DMDhwsNjlkJNzmjA5evQobty4gejoaOs2s9mM\nw4cPY+PGjcjLywMA6PV6hISEWNvo9XoEBgY2us/U1FTr9wkJCUhISGiX2olczZ3aO9h6civKa8ox\nstdIscuhdpSdnY3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- "text": [ - "" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Multiple plots in same figure (Interactive)\n", - "[[back to section overview](#sections)]" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#Note the use of plot parameter aliases and the figsize parameter\n", - "eplot = EasyPlot(x, x**2, label=r\"$y = x^2$\", figsize=(8,4), showlegend=True, \n", - " ncol=2, ms=10, ls=':', markeredgewidth=1.5, xlabel='x',\n", - " ylabel='y', title='title', color='b', marker='s')\n", - "eplot.add_plot(x, 0.15*x**3, label='$y = 0.15x^3$', c='c', ls='-', marker='D')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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0Wi1nzpwhOTmZFi1a0KZNGyIiIli4cCFdunShY8eOLFiwAFdXV8aNGweAm5sb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\n6tSJp1q1uqfXKwq89hq8/z5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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Using EasyPlot object as template for new plots\n", - "[[back to section overview](#sections)]\n", - "\n", - "The previous example defined an `EasyPlot` object `eplot` with various plot parameters set - `xlabel`, `ylabel`, `title`, `alpha`, `ncol`, `markersize` and `markeredgewidth` . We can examine the current set plot parameters for an `EasyPlot` object by accessing its `kwargs` instance variable" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#Examine set plot parameters for eplot\n", - "eplot.kwargs" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "{'ax': ,\n", - " 'dpi': 80,\n", - " 'fancybox': True,\n", - " 'fig': ,\n", - " 'figsize': (8, 4),\n", - " 'loc': 'best',\n", - " 'markeredgewidth': 1.5,\n", - " 'markersize': 10,\n", - " 'ncol': 2,\n", - " 'numpoints': 1,\n", - " 'showlegend': True,\n", - " 'title': 'title',\n", - " 'xlabel': 'x',\n", - " 'ylabel': 'y'}" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that certain plot parameters such as `linestyle`, `marker`, `label` and `color` are considered **unique parameters** and do not carry over from one plot to another as they are typically unique to a specific plot. \n", - "\n", - "It is easy to use `eplot` as a template to generate a new plot:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "eplot.new_plot(x, 1/(1+x), '-s', label=r\"$y = \\frac{1}{1+x}$\", c='#fdb462')\n", - "# Note that the plot reuses the axis labels, title and marker \n", - "# formatting from the previous eplot template" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Autoscaling plots\n", - "EasyPlot objects have an `autoscale()` instance method that can be called on the instance to reset the `xlim` and `ylim` properties to `None` and autoscale the axes. The method signature is listed below along with the default parameter values.\n", - "```python\n", - "def autoscale(self, enable=True, axis='both', tight=None):\n", - " \"\"\"Autoscale the axis view to the data (toggle).\n", - "\n", - " Convenience method for simple axis view autoscaling. It turns \n", - " autoscaling on or off, and then, if autoscaling for either axis is on,\n", - " it performs the autoscaling on the specified axis or axes.\n", - "\n", - " Arguments\n", - " =========\n", - " enable: [True | False | None]\n", - " axis: ['x' | 'y' | 'both']\n", - " tight: [True | False | None]\n", - " \"\"\"\n", - "``` " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting background grid\n", - "[[back to section overview](#sections)]\n", - "\n", - "easyplot provides two ways to display the background grid for the axes. \n", - "- To display a simple grid without any custom formatting, the plot parameter `grid='on'` can be passed to the easyplot object. Setting `grid='off'` turns the grid off." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "eplot.new_plot(x, 1/(1+x), '-s', label=r\"$y = \\frac{1}{1+x}$\", c='#fdb462', grid='on')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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g4eGBgoICi/UFBQUICwurdZv09HTs3LkTixYtAmCa3c5oNMLLywspKSn4wx/+\nUGObqVOnIjIyEgAQFBQEvV5v7n1Wjz+55fKwl5D+7gtA6pcYMeM1CL/WjdqfwWDAnDlztP99XHi5\nep2jxOOqy0lJSfz7oPAy/14o8/chIyMDubm5aApVb/UbNGgQ+vbtizVr1pjXdevWDePGjcOSJUtq\ntM/OtjxV/dlnn2HJkiXYu3cvwsPDERQUZPFzd77VzxpSGoGcHZA5/4HoPRkitJ/N+8jIyDB/GEkZ\nzLE6mGflMcfKc4rpfbdu3YrJkycjOTkZsbGxePvtt7Fu3TocPnwYERERSExMxN69e/H111/Xuv36\n9evx7LPP8j7/JpJXciD3rwGC+0DcNR7Cw0vrkIiIqBEaW/dUHfMfP348Ll26hFdffRXnz59H7969\nsX37dkRERAAA8vPzkZNT//3pvM+/6UTLTqYnBB7aALlnCRA9EyIwVOuwiIhIJXywjxuTUgJnd0Ie\n+xTirvFAu9gGO1c8jac85lgdzLPymGPlOcWRPzkWIQTQYTjQsrPpCYGF2cDd/wfC00/r0IiISEE8\n8icAgKwsgzy8Gbh8DCL6jxAtOmodEhERNcApLvhTGot/08m8HyAPb4LoMgaIvI/XWBAROTCneLAP\nOT4RPtA0NfAv35meD1B+zeLnt99rSspgjtXBPCuPOXZcLP5Ug/BvCxH7PBAYapoa+NIxrUMiIiI7\n4ml/qpe8cAjy4DqIjnFAlzEQgv1FIiJHwTF/sPgrRZZegTT8P0BKiOinIHxbah0SERGBY/6kIOHb\nEuKev0K0uQvpq5+CLDigdUgujeOk6mCelcccOy7e509WEUIHdH0Iomsx5OEPIC8dhegxFkLHjxAR\nkbPhaX+ymSy/BnlwHVBahLkfnsWBwz9btZ1er0dSUpLC0RERuQ/O8EeqEd6BQP8/A7lpOLDrSRhO\nXoQ+qlW92xhOXVYpOiIiagiLP9mkeq5uIQQQdR/QrD30UZVIe+2Bercb+fx2lSJ0fpwPXR3Ms/KY\nY8fFC/6oaTx9tY6AiIhsxOJPNmEvXnnMsTqYZ+Uxx46LxZ+IiMjNsPiTTZp036402i0OV8Z7o9XB\nPCuPOXZcLP6knisnYdz/v5AXDkIaK7SOhojIbfFqf7JJk8bwWnaCaNkF8sQ24MC7kGEDINoNBoKi\n+Ojg23CcVB3Ms/KYY8fF4k/qER4QkSMhIkdCXr8A5H0PecD0zADZbhBEu0EQASFaR0lE5PJY/Mkm\ntd23aziawMpdAAAWJUlEQVR1ucH7+A2nLkPf+tayCAgGuj4EdBkDFJ+G/CULMus1SL/WEOGDgfAB\nED7NFfgNHB/vjVYH86w85thxsfhTk+j1euvata69rRACCIqECIqEvGs8cOkI5C9ZwM//hGzZBaLd\nICAkGsLTx96hExG5Lc7tTw5JVpQBBfshf/kOuHICCOlruj6g9V0QOg+twyMicgiNrXss/uTwZNlV\nIG8vZF4WUHIJCBto6gi06MgLBYnIrTW27vFWP7KJFvftCp/mEFH3QTdkAcTg5wEvf8j9ayAzX4Q8\n/m/IGxdVj0lJvDdaHcyz8phjx8Uxf3IqIiAEotsjkF0fBopOma4P2L0E0j/YdH1A+AAI72Zah0lE\n5NB42p+cnjRWAIXZpusDLhwEWnWrulCwL4QHLxQkItfFMX+w+BMgK0qA/KoLBYtOAaH6qgsFe0CI\nuke55syZA4PBYNV76PV6JCUl2StkIqJGa2zd42l/somj37crPP2A9rEQ7WMhS4uA83shj34MlBZB\nht9jOiPQvEONCwUNBgMMP2ZBH9Wq3v0bTl1WMnwAjp9jV8E8K485dlws/uSyhG8QEHU/RNT9kNfO\nQ/7yHeR/kwEPb6DdICD8Hgj/Nub2+qhWSHvtgXr32dBkRkREzoCn/cmtSCmBKydMwwLnfwSahUGE\nD8KIKf8DXD5hXfFv3Z1XMRORQ+BpfyIrCCGAVl0hWnWF7DUBuPhT1URCOVqHRkSkGt7nTzZxpSNe\nofOECNFD128m0LKz1uGYuVKOHRnzrDzm2HGx+BMBQD13AhARuRqe9ieb8MpdAJePw5j1OtCyE0SL\nKCAoCvBtabephpljdTDPymOOHZcmhzvJycmIioqCn58fYmJisGvXrjrbZmRk4JFHHkF4eDgCAgLQ\nt29frFu3TsVoie7QshNElwcgPLwhz+2C3LUI8pu/wvjjPyBPbIMszIa8eUPrKImI6qR68d+yZQvm\nzJmDBQsWwGAwIDY2FqNHj8bZs2drbZ+VlYW+ffvik08+weHDhzFr1iw8/fTT+PDDD1WOnACO4QEA\nhAdE27shuj4M3YDZEKOSIGITIcIHQpZfg/z5X5DfzIMxcwGMB9ZC5qZBFuWaZiK0AnOsDuZZecyx\n41L9tP/KlSsxbdo0zJgxAwDw1ltv4auvvkJKSgqWLl1ao31iYqLF8syZM5Geno5PPvkEEyZMUCVm\ncg+GU5cbvI/fcOoy9K0t1wkhAP+2gH9biPCBAKqmHP41Dyg+BVl0CjiTAdy4CNmsPRAUBRHUyTRc\n4B/MJxMSkepULf7l5eXYt28fnnvuOYv18fHx2LNnj9X7KS4uRocOHewdHlnBVcfw9Hq9de1aW9dW\n6DyBFh2AFh0gOgwHUDX1cPEZ0wOJ8vcBRz8BKssgg0zXDZiuH+jU6BxzimLbuOpn2ZEwx45L1eJf\nWFiIyspKhISEWKwPDg5Gfn6+Vfv44osvkJaWZlNngaghahRC4ekHtO4OtO6O6mN9WVoEFOdCXsmB\nzP3a1DHwCqg6O2DqDKBFB6seUORIUxQTkWNzqqv9d+/ejUmTJmHVqlWIiYmptc3UqVMRGRkJAAgK\nCoJerzf3PqvHn7jc+GWDwYA5c+Y4TDyusixC9MjIyID0jgZKixAXHIr01G3Ata2I6+oNGRCCjONl\nEAFhiHvgMSAwHJmZOy32V1RUhMi2AeZZCjMOnjf9vE+YxfIrm/Zr/vs6wnJSUhL/Pii8zL8X9l+u\n/j43NxdNoer0vuXl5QgICMDmzZsxduxY8/pnnnkG2dnZSE9Pr3PbXbt24cEHH8TixYvxl7/8pdY2\nnN5XeRl8UIfi7syxrLwJXD1rOitQnGN6WmFZMdC8o8UZghGjHwMuHeMUxVbiZ1l5zLHynGJ6X29v\nb/Tv3x87duywKP6pqakYN25cndvt3LkTY8aMwSuvvFJn4Sd18D+y8u7MsfDwAlp2Mt1iiPsAALL8\nGlCca+oQnNsD/LQRuHJS/WCdGD/LymOOHZfqp/0TEhIwefJkDBw4ELGxsXj77beRn5+PmTNnAjBd\n3b937158/fXXAEw9xwcffBB//vOfMWHCBPO1AR4eHmjbtq3a4RM5BOEdCLS9G2h7NwSqHljU4gc+\no4CIrKL6ff7jx49HUlISXn31VURHR2PPnj3Yvn07IiIiAAD5+fnIybn1B2zDhg0oLS3FihUrEBYW\nhvDwcISHh+Oee+5RO3QC79tVQ2NyLIQAdF7Wb3D5OIzfLoTxv8kwHv0Y8sxOyEtHIUsuQ0qjze/v\njPhZVh5z7Lg0ueBv1qxZmDVrVq0/u3P2vnXr1nFGPyJ7a9kZos9U4PoF4PoFyCsngHN7gBsXgJsl\nkP5tgIBg0zwEVV8REAz4tYZQ+DkIvGWRSHlOdbU/aY9jeMpTJcdCB9EiEmgRaVq87UeyohS4cdHU\nMbhxAbL4DHD+R9Ny+VVIv9bmzoDwDwEC2pqW/duY5jdoIrVuWeRnWXnMseNi8SciC8LTF2geYXrh\njo5B5U1Tx+BG1RmD6+eBCwdMy6VXIH2CLM8YBITcmv3Qw9vqGPRRray7a4GIGoXFn2zCW3eU15Qc\nN3aKYmsJDy+gWbjphTs6BsYKoOTSrTMG1y8Al46alksKIb2b3TpjUD2U4B8MBLQ1TYCkMn6Wlccc\nOy4WfyIXYe8pim0ldJ6mI/0A0wyeFh0DaQRKLt86Y3DjgunOhBsXgOsXIb18b11XUHLJ7rERkSVV\nJ/lRGif5IXI+UhpNkxZVnTEYMXYmUHLZutP+QZFI/2YH4BXoUA9I4kWLpBanmOSHiOhOQugA35am\nV+vupmsESqy8mO/XPMiMF4DKckif5oBPkOnl2wKi6qtpuWq9d4DidysAfM4COT4Wf7IJx/CUxxzb\nICgKuvhVpgsRy4qA0mLzV1lWBFz+2WIdKkpMnQTfIGT8dAFxsQMgfG91GMxfvZs1uZPAixb5WXZk\nLP5E5PSEh5f5rgLzulramToJps6AyP8Golk705MVrxw3rS8tMnUUblZ1EnxaVJ01aFHVSbjjTIJP\n0zsJjozDF66LxZ9swl688phj5Zg6CW0A/zYYMbaLaV0t7aSxoqozUH3WoAiyrNh0kWLZbWcSbl6H\n9A681Rmo7iCUFan7iymkqcMX/Cw7LhZ/InI4St+y2BCh8wT8Wpte1etqaWfqJFy1GHKQpcVARan1\nb1Z6BfJcFuAdCHgHAF6Bpu89/RziIkYOX7gmFn+yCcfwlOfuOVbrlkV75NnUSWhlelWvA4CAJFNn\nwBqVNyEvHgJuXgfKr5leN6+bLmL08q/qDASYOgRepq/C646Owm3f22OWRXtxlM8yhy9qcpxPCRER\n4BZ/eC0EBEMX/XSN1dJYUdUhuA7cvHZbx+AaZPl14HpB1frrt329DqnzrOooVHcamtXsNNz+c69A\nwNPXIc4yKIV3X9TE4k82cYRevKtjjtXh6HkWOs+qCwxb1PxZHdtIKYGKkppnEcqvQd68Bnk9Hyiq\n7kjc1qkwVkCazx7cdkbhxsUm/Q6OlGMOX1hi8ScichFCCMDL3/S67c4HoO4OA1B1F8TN284gVJ1h\ngPCw/s2Lc2HMeh3w9AW8/ABPf9MZBfP3flU/q/rey8+8zpXvmLAXW4YurMHiTzZxlDE8V8Ycq0Pp\nPGt90aIthIcX4FF1t8Lt/FpZf/QfEArR7WHgZknV2YcSZOz+AcP7dwFuFAI3b5guhKwoqfq+pGq5\nFNLTp6oj4HdHx8D0vfCs6kB4+Vatv/N7P4e61kEJ1g5dWMu1s0VEpAGtn7OgCU9fiNY9LFaJ0x7Q\n3RVX72ZSGm91Cqo6DRZfK0ogb5YAZfl3tLlR9dW0rRTC3BEwn2WoXr5+QcFfXD21DV14jHm3Ufti\n8Seb8IhUecyxOpTMs9tdtFgHa3IshO7WUEVdbRrYh5QSMJbf1hm4YdF5gIeX9UFfOgbjV38CPH0A\nD1/Aw9vUkfDwqbFOePqY1nv43NHmtrbVX3WeDnVRJYs/ERHVyRmGL4QQt4pwbXxbWn/037o7xKj/\nC1SUAZVlt30tNX01ryuFrCgHbl6ptw0qyk1fjZWQ9XYQbnUiRG1tbt6wX8LA4k824ni08phjdTDP\nDWvq8IWz5lhUX2/QUDsb9imNFUBl+R0dgzs7CqZlWVEGlF+99fPKMrs/6prFn4iIasXhC/sROk9A\n51nv8Ia5bW0rm/8TuHTMbvGw+JNNnLEX72yYY3Uwz8pzpBw7w/CFmlj8iYjIpbnl3RcNYPEnmzjr\nGJ4zYY7VwTwrz1FyzOGLmlj8iYiInIA1QxfWElJKaZc9OQAhBFzo1yEiIgJQ9/S+mZmZjap7LP5E\nREROqrF1j09TIJtkZGRoHYLLY47VwTwrjzl2XCz+REREboan/YmIiJwUT/sTERGRVVj8ySYcw1Me\nc6wO5ll5zLHjYvEnIiJyMxzzJyIiclJOM+afnJyMqKgo+Pn5ISYmBrt27aq3/aFDhzB8+HD4+/uj\nffv2WLx4sUqREhERuSZVi/+WLVswZ84cLFiwAAaDAbGxsRg9ejTOnj1ba/urV6/i/vvvR1hYGH78\n8Ue8+eabWLFiBVauXKlm2HQbjuEpjzlWB/OsPObYcala/FeuXIlp06ZhxowZ6N69O9566y2EhYUh\nJSWl1vYbN25EaWkpNmzYgJ49e2Ls2LGYP38+i7+GaptekuyLOVYH86w85thxqVb8y8vLsW/fPsTH\nx1usj4+Px549e2rdJisrC8OGDYOPj49F+7y8PJw+fVrReKl2RUVFWofg8phjdTDPymOOHZdqxb+w\nsBCVlZUICQmxWB8cHIz8/Pxat8nPz6/Rvnq5rm2IiIiofg59q58QQusQ6A65ublah+DymGN1MM/K\nY44dl6dab9SmTRt4eHigoKDAYn1BQQHCwsJq3SY0NLTGEX719qGhoTXad+7cmR0GFWzYsEHrEFwe\nc6wO5ll5zLGyOnfu3KjtVCv+3t7e6N+/P3bs2IGxY8ea16empmLcuHG1bjN48GDMnz8fZWVl5nH/\n1NRUtGvXDh07dqzR/sSJE8oET0RE5EJUPe2fkJCA9evXY+3atThy5Ahmz56N/Px8zJw5EwCQmJiI\nUaNGmdtPnDgR/v7+mDp1Kg4fPoxPP/0Uy5cvR0JCgpphExERuRTVjvwBYPz48bh06RJeffVVnD9/\nHr1798b27dsREREBwHQRX05Ojrl98+bNkZqaimeeeQYxMTFo1aoV5s2bh7lz56oZNhERkUtxqel9\niYiIqGEOfbW/tWydMphss2zZMgwYMAAtWrRAcHAwHn74YRw+fFjrsFzasmXLoNPp8Oyzz2odiks5\nf/48nnzySQQHB8PPzw+9evXCzp07tQ7LZVRUVOCFF15Ap06d4Ofnh06dOuF//ud/UFlZqXVoTm3n\nzp14+OGH0b59e+h0ulovoly4cCHatWsHf39/jBgxAtnZ2fXu0+mLv61TBpPtMjMz8ec//xlZWVlI\nS0uDp6cnRo0ahStXrmgdmkv67rvv8M4776BPnz68e8WOioqKMGTIEAghsH37dhw9ehT/+Mc/EBwc\nrHVoLmPp0qVYs2YNVq1ahWPHjuHNN99EcnIyli1bpnVoTu369evo06cP3nzzTfj5+dX4u7B8+XKs\nXLkS//jHP7B3714EBwfj/vvvx7Vr1+reqXRyAwcOlE8//bTFuq5du8rExESNInJ9165dkx4eHvKL\nL77QOhSXU1RUJDt37iwzMjJkXFycfPbZZ7UOyWUkJibKoUOHah2GSxszZoycOnWqxbopU6bIhx56\nSKOIXE9gYKDcsGGDedloNMrQ0FC5dOlS87qSkhLZrFkzuWbNmjr349RH/o2ZMpia7urVqzAajWjZ\nsqXWobicp59+GuPGjcPw4cP5eGo7++yzzzBw4EA8/vjjCAkJQXR0NFavXq11WC5l9OjRSEtLw7Fj\nxwAA2dnZSE9PxwMPPKBxZK7r1KlTKCgosKiDvr6+uPfee+utg6pe7W9vjZkymJpu9uzZiI6OxuDB\ng7UOxaW88847yMnJwaZNmwBwhkt7y8nJQXJyMhISEvDCCy9g//795msqnnnmGY2jcw1/+tOfcO7c\nOdx1113w9PRERUUFFixYYL6dm+yvutbVVgfz8vLq3M6piz+pLyEhAXv27MGuXbtYnOzo2LFjePHF\nF7Fr1y54eHgAAKSUPPq3I6PRiIEDB2LJkiUAgL59++L48eNYvXo1i7+dvPXWW1i3bh02b96MXr16\nYf/+/Zg9ezYiIyMxffp0rcNzO/X9jXbq4t+YKYOp8ebOnYutW7ciPT0dkZGRWofjUrKyslBYWIhe\nvXqZ11VWVuLbb7/FmjVrcP36dXh5eWkYofMLDw9Hz549Ldb16NEDZ86c0Sgi17NkyRIsWLAA48eP\nBwD06tULp0+fxrJly1j8FVI91X1BQQHat29vXl9QUFDrNPjVnHrM//Ypg2+XmpqK2NhYjaJyTbNn\nz8aWLVuQlpaGbt26aR2Oy/nd736Hn376CQcOHMCBAwdgMBgQExODCRMmwGAwsPDbwZAhQ3D06FGL\ndT///DM7snYkpYROZ1lWdDodz2ApKCoqCqGhoRZ1sLS0FLt27aq3Djr1kT9gOg09efJkDBw4ELGx\nsXj77bctpgympnvmmWfwwQcf4LPPPkOLFi3MY0zNmjVDQECAxtG5hhYtWqBFixYW6/z9/dGyZcsa\nR6vUOHPnzkVsbCyWLl2K8ePHY//+/Vi1ahVvQ7OjRx99FK+99hqioqLQs2dP7N+/H2+88QaefPJJ\nrUNzatevX8fx48cBmIavTp8+DYPBgNatWyMiIgJz5szB0qVL0aNHD3Tt2hWvvvoqmjVrhokTJ9a9\nU6VuR1BTcnKyjIyMlD4+PjImJkZ+++23WofkUoQQUqfTSSGExWvRokVah+bSeKuf/W3btk327dtX\n+vr6yu7du8tVq1ZpHZJLuXbtmvzrX/8qIyMjpZ+fn+zUqZN88cUXZVlZmdahObX09HTz393b/xZP\nmzbN3GbhwoUyLCxM+vr6yri4OHn48OF698npfYmIiNyMU4/5ExERke1Y/ImIiNwMiz8REZGbYfEn\nIiJyMyz+REREbobFn4iIyM2w+BMREbkZFn8iIiI3w+JPRETkZlj8iYiI3AyLPxHZ7OLFiwgLC8Mr\nr7xiXnfw4EH4+vrik08+0TAyIrIG5/YnokbZsWMHHnroIWRmZqJv376IiYnBoEGDsHbtWq1DI6IG\nsPgTUaPNnTsX//rXv3Dvvfdi9+7dMBgM8Pf31zosImoAiz8RNVp5eTn69OmDEydOICsrCwMGDNA6\nJCKyAsf8iajRTp06hbNnz0Kn0+HkyZNah0NEVuKRPxE1ys2bNzFo0CD06NEDAwcOxKJFi3DgwAFE\nRERoHRoRNYDFn4ga5fnnn8fmzZtx8OBBNG/eHKNHj0ZpaSnS0tIghNA6PCKqB0/7E5HNMjMzsXLl\nSrz33nto3rw5AGD9+vXIzs7G66+/rnF0RNQQHvkTERG5GR75ExERuRkWfyIiIjfD4k9ERORmWPyJ\niIjcDIs/ERGRm2HxJyIicjMs/kRERG6GxZ+IiMjNsPgTERG5mf8PQBhcWDlLiEgAAAAASUVORK5C\nYII=\n", - "text": [ - "" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- For more advanced control of the grid, the easyplot `grid()` instance method is provided with a call signature of `grid(self, b=None, which='major', axis='both', **kwargs)` where `**kwargs` are passed to linespec of grid lines (eg: linewidth=2)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "eplot.new_plot(x, 1/(1+x), '-s', label=r\"$y = \\frac{1}{1+x}$\", c='#fdb462')\n", - "eplot.grid(which='major', axis='x', linewidth=2, linestyle='--', color='b', alpha=0.5)\n", - "eplot.grid(which='major', axis='y', linewidth=2, linestyle='-', color='0.85', alpha=0.5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Log/Linear/Symlog plots\n", - "[[back to section overview](#sections)]\n", - "\n", - "Plot parameters `xscale` and `yscale` can be passed to easyplot instances with any of the following values: `['linear'|'log'|'symlog']`" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "eplot.new_plot(x, 1/(1+x), '-s', label=r\"$y = \\frac{1}{1+x}$\", c='#fdb462', yscale='log')\n", - "eplot.grid(which='minor', axis='both')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#easyplot\n", + "**easyplot:** A matplotlib wrapper written in Python to enable fast and easy creation of reusable plots.\n", + "\n", + "by [Sudeep Mandal](http://www.sudeepmandal.com)\n", + "\n", + "The EasyPlot module provides a thin wrapper to matplotlib, enabling fast and easy generation of beautiful, annotated plots with minimal code. It also enables the reuse of EasyPlot instances to generate new plots that maintain state from previous plots allowing for quick and easy generation of multiple plots of a similar type. EasyPlot supports all commonly used plot parameters and allows access to the underlying figure and axes instances to allow the user to further customize the generated plots if necessary.\n", + "
\n", + "I'd love to hear your comments and/or suggestions. You can get in touch with me via [twitter](https://twitter.com/hamsterhuey), [email](mailto:sudeepmandal@gmail.com) or [google+](https://plus.google.com/u/0/105292596991480463202/)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Sections\n", + "- [Requirements](#requirements)\n", + "- [Installation](#installation)\n", + "- [Motivation and Background](#motivation)\n", + "- [Features](#features)\n", + "- [Documentation](#documentation)\n", + " - [EasyPlot instance methods](#methods)\n", + " - [EasyPlot instance variables](#variables)\n", + " - [List of supported plot parameters](#plot_params)\n", + " - [Interactive/Non-Interactive plotting mode](#interactive_mode)\n", + "- [Usage and Examples](#usage)\n", + " - [Simple Plot](#simple_plot)\n", + " - [Multiple plots in the same figure (Interactive)](#multiple_plots)\n", + " - [Using EasyPlot object as template for new plots](#easyplot_template)\n", + " - [Autoscaling Plots](#autoscale)\n", + " - [Setting background grid](#grid)\n", + " - [Log/Linear/Symlog plots](#log_linear_scale)\n", + " - [Modifying Plot color cycle](#colorcycle)\n", + " - [Multiple plots in same figure iteratively using iter_plot](#iter_plot)\n", + "- [Advanced plotting](#advanced_plotting)\n", + " - [Using easyplot with subplots](#subplots)\n", + " - [Custom plot modifications via axes instance](#ax_mods)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Requirements\n", + "- Python 2.7.2+\n", + "- matplotlib\n", + "\n", + "Use of the [IPython](http://ipython.org/) shell is strongly recommended with this library (and matplotlib plotting in general). The `%matplotlib` magic command in IPython (or starting IPython using `ipython --matplotlib`) implements a number of backend tweaks to enable robust, interactive plotting using matplotlib." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Installation\n", + "[[back to section overview](#sections)]\n", + "\n", + "You can use the following commands to install EasyPlot:\n", + "\n", + "`pip install easyplot`\n", + "\n", + "or\n", + "\n", + "`easy_install easyplot`\n", + "\n", + "Alternatively, you could download the package manually from the Python Package Index: https://pypi.python.org/pypi/EasyPlot, unzip it, navigate into the package, and use the command:\n", + "\n", + "`python setup.py install`\n", + "\n", + "or\n", + "\n", + "`pip install .` " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Motivation and background\n", + "[[back to section overview](#sections)]\n", + "\n", + "Setting up aesthetically pleasing plots with plot titles, axes labels, etc requires several lines of boilerplate code in vanilla matplotlib. As an example, creating a basic plot in matplotlib requires the following lines of code: \n", + "```python\n", + "fig, ax = plt.subplots()\n", + "ax.plot(x, x**2, 'b-o', label=\"y = x**2\")\n", + "ax.plot(x, x**3, 'r--s', label=\"y = x**3\")\n", + "ax.legend(loc='best')\n", + "ax.grid()\n", + "ax.set_xlabel('x')\n", + "ax.set_ylabel('y')\n", + "ax.set_title('title')\n", + "```\n", + "\n", + "Pylab alleviates some of this, but still requires calls to a number of different functions that are commonly used (such as xlabel, xlim, etc.). More complicated plots can require several more lines of code. Typing all this code every time to generate plots gets tedious very quickly. This situation is further exacerbated when working in an IPython Notebook where all plots typically need to be labeled, annotated and looking their best. Having several lines of code preceeding every plot in a notebook can break the flow of the document and distract from the code/concepts being presented by the author. Furthermore, oftentimes, plots with similar labels and formatting need to be generated repeatedly with different datasets. Generating these sets of plots would require retyping these same lines of boilerplate code across different sections of your code/notebook.\n", + "\n", + "Easyplot is my attempt to address these issues and make generating quick, pleasant looking, annotated plots a bit easier. In keeping with [DRY philosophy](http://en.wikipedia.org/wiki/Don%27t_repeat_yourself), `easyplot` enables the creation of an `EasyPlot` object that maintains state information of all plot parameters passed to it in order to generate a plot. This can then be easily reused to generate new plots with the user only having to supply any additional plot parameters, or those parameters he or she wishes to override from the previous plot.\n", + "\n", + "Easyplot supports a large number of standard plot parameters that most users typically deal with when plotting in matplotlib. Additionally, it provides methods to access the figure and axes instance for the latest plot, enabling users to perform more custom plot modifications that are not directly supported by easyplot. It also supports interactive plotting where additional plot parameters can be passed to the current plot using the `update_plot` method. The plot above can be generated using `easyplot` as follows:\n", + "```python\n", + "eplot = EasyPlot(x, x**2, 'b-o', label='y = x**2', showlegend=True,\n", + " xlabel='x', ylabel='y', title='title', grid='on')\n", + "eplot.add_plot(x, x**3, 'r--s', label='y = x**3')\n", + "```\n", + "Along with the reduced typing, easyplot enables the consolidation and passing of all plot parameters into a single plot call. This is already quite handy, but the real benefit is evident when one needs to generate a new plot with the same plot parameters (such as axis labels and title) but with new data:\n", + "```python\n", + "eplot.new_plot(x, 1/x, 'g-D', label='y = 1/x')\n", + "```\n", + "\n", + "\n", + "`EasyPlot` also provides an `iter_plot()` method that iterates through x, y data and plot parameters that are provided in a list or dictionary format to automatically generate an annotated, multi-line plot with a single statement:\n", + "```python\n", + "eplot = EasyPlot(xlabel=r'$x$', ylabel='$y$', fontsize=16,\n", + " colorcycle=[\"#66c2a5\",\"#fc8d62\",\"#8da0cb\"], figsize=(8,5))\n", + "eplot.iter_plot(x, y_dict, linestyle=linestyle_dict, marker=marker_dict,\n", + " label=labels_dict, linewidth=3, ms=10, showlegend=True, grid='on')\n", + "```\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Features\n", + "[[back to section overview](#sections)]\n", + "- Access to a large number of the most used matplotlib plot parameters under a unified wrapper class\n", + "- Plot parameter aliases supported. Can be extended by user for arbitrary alias definitions for various plot parameters\n", + "- Ability to use `EasyPlot` objects as templates to rapidly generate annotated plots of a similar type\n", + "- `iter_plot()` method to easily iterate through x, y datasets and plot multiple plots with a single method call\n", + "- Draggable legend when using GUI backends (eg: qt, wx, etc.)\n", + "- Provides access to underlying figure, axes and line2D objects for advanced plot customization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Documentation\n", + "[[back to section overview](#sections)]\n", + "\n", + "EasyPlot consists of an `EasyPlot` class to create `EasyPlot()` objects in order to generate matplotlib plots. The object constructor allows for passing all commonly used plot parameters (such as `xlabel`, `ylabel`, `title`, `markersize`, `linewidth`, etc.) and the `x`, `y` data and an optional format string as arguments in order to generate an annotated plot via a single statement such as:\n", + "```python\n", + "fftplot = EasyPlot(freq, amplitude, 'g-o', markersize=9, linewidth=3, xlabel='Frequency (Hz)',\n", + " ylabel='Amplitude', label='FFT Data', showlegend=True)\n", + "```\n", + "If no `x`, `y` data is provided to the `EasyPlot` constuctor, creation of a figure and axes instance for the `EasyPlot` object is deferred and the plot parameters that are passed to the constructor are used to initialize the `EasyPlot` object. \n", + "\n", + "Apart from the usual plot parameters, `EasyPlot` constructor also takes the following special keyword arguments:\n", + "\n", + "`fig` : an optional reference to a figure object supplied by the user
\n", + "`ax` : an optional reference to an axes object (linked to `fig`) to use for displaying the plots. The [subplots example](#subplots) demonstrates the use of `fig` and `ax` plot parameters for custom plotting routines
\n", + "`figsize` : the size of the plot figure
\n", + "`showlegend` : set to True or False to display the plot legend\n", + "\n", + "The `EasyPlot` object also retains state information of most plot parameters passed to it, thus allowing the object to be reused as a template for generating new plots with similar formatting/labeling. The only parameters whose values are not persistent are `color`, `marker`, `linestyle` and `label` as they are assumed to be unique to a single plot.\n", + "\n", + "`EasyPlot` objects also provide access to their `figure`, `axes` and `Line2D` instances via the `get_figure()`, `get_axes()` methods and `line_list` instance variable respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### EasyPlot instance methods\n", + "The main instance methods for `EasyPlot` objects are:\n", + "
\n", + "`add_plot(*args, **kwargs)` : This is the main instance method to add additional plots and plot parameters to an existing plot. `*args` is a variable length argument, allowing for `x`, `y` pairs with an optional format string of the form `'b-o'`. The optional format string is a shorthand for specifying the plot color, linestyle and marker type. The [matplotlib docs](http://matplotlib.org/1.3.1/api/pyplot_api.html#matplotlib.pyplot.plot) provide more information regarding the format string notation. `**kwargs` are a list of named plot parameters as explained in the [next section](#plot_params).\n", + "
\n", + "`update_plot(**kwargs)` : Instance method to update plot parameters only (example: `xlabel`, `title`, etc.). This method is typically only useful when plotting interactively using an interactive matplotlib backend.\n", + "
\n", + "`new_plot(*args, **kwargs)` : Create a new plot using the plot parameters of the existing `EasyPlot` instance as a template (i.e., plot parameter settings carry over from the `EasyPlot` instance to the new plot)\n", + "
\n", + "`iter_plot(x, y, mode='dict', **kwargs)` : This method can be used to plot multiple line/scatter plots with a single statement as long as `x`, `y`, and any relevant plot parameters that change for each plot are passed in as either a dictionary with a common set of keys (`mode='dict'`) or a set of lists/arrays corresponding to each data set to be plotted (`mode='array'`).\n", + "
\n", + "`autoscale(enable=True, axis='both', tight=None)` : Set autoscale options for `x`, `y` or `both` axes\n", + "
\n", + "`grid(**kwargs)` : Equivalent to matplotlib axes.grid() method and accepts same set of keyword arguments. Enables more custom control over the plot grid.\n", + "
\n", + "`get_figure()` : Returns reference to the Figure object for the instance\n", + "
\n", + "`get_axes()` : Returns reference to the axes object for the instance\n", + "
\n", + "`redraw()` : Forces a redraw of the plot canvas. Must be used if custom modifications are made to the plot external to easyplot via direct access to the figure and axes handles. **Note: `redraw()` is only supported when plotting in interactive mode (i.e. `plt.isinteractive() == True`)**.\n", + "
\n", + "`set_fontsize(font_size)` : Modify global setting for plot font-size\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### EasyPlot instance variables\n", + "The main instance variables for `EasyPlot` objects are:\n", + "
\n", + "`self.kwargs` : A dictionary containing a list of all existing plot parameters for an `EasyPlot` object. The user should not modify this directly. This variable should be accessed in a read-only fashion for the purposes of examining the state of an `EasyPlot` object.\n", + "
\n", + "`self.line_list` : A list of [`Line2D`](http://matplotlib.org/1.3.1/api/artist_api.html#matplotlib.lines.Line2D) items corresponding to all plots of the `EasyPlot` object. These can be manipulated using standard matplotlib methods outside of `easyplot` for [advaced plotting](#advanced_plotting)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### List of supported plot parameters\n", + "```\n", + "Plot Parameters:\n", + "----------------\n", + "fig : figure instance for drawing plots\n", + "ax : axes instance for drawing plots (If user wants to supply axes,\n", + " figure externally, both ax and fig must be supplied together)\n", + "figSize : tuple of integers ~ width & height in inches\n", + "label : Label for line plot as determined by *args, string\n", + "color / c : Color of line plot, overrides format string in *args if\n", + " supplied. Accepts any valid matplotlib color\n", + "linewidth / lw : Plot linewidth\n", + "linestyle / ls : Plot linestyle ['-','--','-.',':','None',' ','']\n", + "marker : '+', 'o', '*', 's', 'D', ',', '.', '<', '>', '^', '1', '2'\n", + "markerfacecolor / mfc : Face color of marker\n", + "markeredgewidth / mew :\n", + "markeredgecolor / mec : \n", + "markersize / ms : Size of markers\n", + "markevery / mev : Mark every Nth marker \n", + " [None|integer|(startind, stride)]\n", + "alpha : Opacity of line plot (0 - 1.0), default = 1.0\n", + "title : Plot title, string\n", + "xlabel : X-axis label, string\n", + "ylabel : Y-axis label, string\n", + "xlim : X-axis limits - tuple. eg: xlim=(0,10). Set to None for auto\n", + "ylim : Y-axis limits - tuple. eg: ylim=(0,10). Set to None for auto\n", + "xscale : Set x axis scale ['linear'|'log'|'symlog']\n", + "yscale : Set y axis scale ['linear'|'log'|'symlog']\n", + " Only supports basic xscale/yscale functionality. Use \n", + " get_axes().set_xscale() if further customization is required\n", + "grid : Display axes grid. ['on'|'off']. See grid() for more options\n", + "colorcycle / cs: Set plot colorcycle to list of valid matplotlib\n", + " colors\n", + "fontsize : Global fontsize for all plots\n", + "\n", + "Legend Parameters:\n", + "------------------\n", + "showlegend : set to True to display legend\n", + "fancybox : True by default. Enables rounded corners for legend box\n", + "framealpha : Legend box opacity (0 - 1.0), default = 1.0\n", + "loc : Location of legend box in plot, default = 'best'\n", + "numpoints : number of markers in legend, default = 1.0\n", + "ncol : number of columns for legend. default is 1\n", + "markerscale : The relative size of legend markers vs. original. \n", + " If None, use rc settings.\n", + "mode : if mode is “expand”, the legend will be horizontally \n", + " expanded to fill the axes area (or bbox_to_anchor)\n", + "bbox_to_anchor : The bbox that the legend will be anchored. Tuple of\n", + " 2 or 4 floats\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##A note about interactive plotting mode in matplotlib\n", + "It is strongly recommended that `EasyPlot` be used in a Python/IPython shell with interactive matplotlib plotting turned on (`matplotlib.pyplot.ion()` turns this on). This provides the maximum flexibility when plotting with `EasyPlot` and also makes it easier to learn and understand the use of this matplotlib wrapper. In the case of non-interactive plotting, the blocking nature of `plt.show()` prevents dynamic modification and further additions to an existing plot. This issue can be partially alleviated by the `plt.show(block=False)` method call (this is experimental in Matplotlib).\n", + "\n", + "Note that if non-interactive plotting is the default, the user must call `plt.show()` after setting up the plot as desired using `EasyPlot`. `add_plot()` method calls on the EasyPlot instance are not supported after the figure is closed in non-interactive mode, and the `redraw()` instance method is also not supported in this mode (user can call `plt.draw()` instead)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Usage and Examples\n", + "[[back to section overview](#sections)]" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Import modules and setup\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from easyplot import EasyPlot\n", + "\n", + "x = np.linspace(0, 10, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple plot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "##Modifying Plot color cycle\n", - "[[back to section overview](#sections)]\n", - "\n", - "easyplot provides the `colorcycle` plot parameter to specify the plot color cycle. If the `colorcycle` parameter is passed with every `add_plot` command, it will result in all plots using the first color of `colorcycle`. To avoid this, the `EasyPlot` object should be initialized with the `colorcycle` and other plot parameters without passing any x, y data as shown below. Subsequently, plots can be added using the `add_plot()` method." - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "eplot = EasyPlot(x, x**2, 'g--o', label=r\"$y = x^2$\", showlegend=True, xlabel='x',\n", + " ylabel='y', title='title', fontsize=14, markersize=10, alpha=0.6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Multiple plots in same figure (Interactive)\n", + "[[back to section overview](#sections)]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Setup\n", - "colors = [\"#66c2a5\",\"#fc8d62\",\"#8da0cb\",\"#e78ac3\",\"#a6d854\",\"#ffd92f\",\"#e5c494\",\"#b3b3b3\"]\n", - "x = np.linspace(0,10,200)\n", - "\n", - "# Demo of color cycle\n", - "# Note the use of EasyPlot constructor with no x,y data to initialize colorcycle prior to\n", - "# adding plots to the figure\n", - "sinplot = EasyPlot(xlabel=r'$\\sin (3\\pi x/L)$', ylabel='$Amplitude$', fontsize=16, colorcycle=colors,\n", - " figsize=(10,5), linewidth=3)\n", - "\n", - "for index in range(8):\n", - " sinplot.add_plot(x, np.sin(3*np.pi*x/10 + index*np.pi/7))" - ], - "language": "python", + "data": { + "image/png": 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w2GPw3HPwyiswZgxs2vT5ZEI5OcE/V322jSdW9bRP0A80Gwp8F/g+cBTvrMEc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\ndO4MP/uZd/+SS+C1104cbrh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YflPcjUEAyXOGJbZvUpxyNHqsdXA+VbV9cf07F0Lh9OD/cMZxAq8c/vHXoSOl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\ntLm8HDxWz8FjgUisLEvsKajhjQ8KkWUJCfD5FRxOL20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- "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, + "output_type": "display_data" + } + ], + "source": [ + "#Note the use of plot parameter aliases and the figsize parameter\n", + "eplot = EasyPlot(x, x**2, label=r\"$y = x^2$\", figsize=(8,4), showlegend=True, \n", + " ncol=2, ms=10, ls=':', markeredgewidth=1.5, xlabel='x',\n", + " ylabel='y', title='title', color='b', marker='s')\n", + "eplot.add_plot(x, 0.15*x**3, label='$y = 0.15x^3$', c='c', ls='-', marker='D')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Using EasyPlot object as template for new plots\n", + "[[back to section overview](#sections)]\n", + "\n", + "The previous example defined an `EasyPlot` object `eplot` with various plot parameters set - `xlabel`, `ylabel`, `title`, `alpha`, `ncol`, `markersize` and `markeredgewidth` . We can examine the current set plot parameters for an `EasyPlot` object by accessing its `kwargs` instance variable" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "{'ax': ,\n", + " 'dpi': 100.0,\n", + " 'fancybox': True,\n", + " 'fig': ,\n", + " 'figsize': (8, 4),\n", + " 'gs': None,\n", + " 'loc': 'best',\n", + " 'markeredgewidth': 1.5,\n", + " 'markersize': 10,\n", + " 'ncol': 2,\n", + " 'numpoints': 1,\n", + " 'showlegend': True,\n", + " 'title': 'title',\n", + " 'xlabel': 'x',\n", + " 'ylabel': 'y'}" + ] + }, + "execution_count": 4, "metadata": {}, - "source": [ - "" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "#Examine set plot parameters for eplot\n", + "eplot.kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that certain plot parameters such as `linestyle`, `marker`, `label` and `color` are considered **unique parameters** and do not carry over from one plot to another as they are typically unique to a specific plot. \n", + "\n", + "It is easy to use `eplot` as a template to generate a new plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "##Multiple plots in same figure iteratively using iter_plot\n", - "[[back to section overview](#sections)]\n", - "\n", - "The `EasyPlot` class has a very useful `iter_plot()` method to iterate through `x`, `y` data stored in dictionaries or 2D arrays and plot them in a figure using a single method call to `iter_plot`. The method signature of `iter_plot` is as follows:\n", - "``` python\n", - "def iter_plot(self, x, y, mode='dict', **kwargs):\n", - " \"\"\"\n", - " Plot multiple plots by iterating through x, y and parameter lists\n", - "\n", - " Arguments:\n", - " ==========\n", - " x : x values. 1D List/Array, Dictionary or Numpy 2D Array\n", - " y : y values. Dictionary or 2D Python array (List of Lists where each\n", - " sub-list is one set of y-data) or Numpy 2D Array\n", - " mode : y, labels and other parameters should either be a Dictionary\n", - " or a 2D Numpy array/2D List where each row corresponds to a \n", - " single plot ['dict'|'array']\n", - " **kwargs : Plot params as defined in __init__ documentation.\n", - " Params can either be:\n", - " scalars (same value applied to all plots),\n", - " dictionaries (mode='dict', key[val] value applies to each plot)\n", - " 1D Lists/1D Numpy Arrays (mode='array', param[index] applies to each\n", - " plot)\n", - " \"\"\"\n", - "```\n", - "The examples below demonstrate the use of `iter_plot` to generate multiple plots from a dataset using both `mode` settings, i.e., `mode='dict'` and `mode='array'`. Note that single value plot parameters that are passed to `iter_plot` (such as `linewidth`) are broadcast and applied to all plots in the figure." - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "eplot.new_plot(x, 1/(1+x), '-s', label=r\"$y = \\frac{1}{1+x}$\", c='#fdb462')\n", + "# Note that the plot reuses the axis labels, title and marker \n", + "# formatting from the previous eplot template" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Autoscaling plots\n", + "EasyPlot objects have an `autoscale()` instance method that can be called on the instance to reset the `xlim` and `ylim` properties to `None` and autoscale the axes. The method signature is listed below along with the default parameter values.\n", + "```python\n", + "def autoscale(self, enable=True, axis='both', tight=None):\n", + " \"\"\"Autoscale the axis view to the data (toggle).\n", + "\n", + " Convenience method for simple axis view autoscaling. It turns \n", + " autoscaling on or off, and then, if autoscaling for either axis is on,\n", + " it performs the autoscaling on the specified axis or axes.\n", + "\n", + " Arguments\n", + " =========\n", + " enable: [True | False | None]\n", + " axis: ['x' | 'y' | 'both']\n", + " tight: [True | False | None]\n", + " \"\"\"\n", + "``` " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting background grid\n", + "[[back to section overview](#sections)]\n", + "\n", + "easyplot provides two ways to display the background grid for the axes. \n", + "- To display a simple grid without any custom formatting, the plot parameter `grid='on'` can be passed to the easyplot object. Setting `grid='off'` turns the grid off." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Setup the x, y data and plot parameters for both modes\n", - "x = np.linspace(0, 10, 11)\n", - "dict_keys = ['x2', 'x3', 'x4']\n", - "labels_list = ['$y = x^2$', '$y = 0.1x^3$', '$y = 0.01x^4$']\n", - "markers_list = ['s', 'o', 'D']\n", - "linestyle_list = ['-', '--', ':']\n", - "\n", - "y_dict, marker_dict, labels_dict, linestyle_dict = {}, {}, {}, {}\n", - "x_list, y_list = [], [] # List of x and y data sets for mode='array'\n", - "\n", - "# Populate dict and list variables with data set\n", - "for ind, key in enumerate(dict_keys):\n", - " marker_dict[key] = markers_list[ind]\n", - " labels_dict[key] = labels_list[ind]\n", - " linestyle_dict[key] = linestyle_list[ind]\n", - " y_dict[key] = (0.1**ind)*x**(ind+2)\n", - " y_list.append((0.1**ind)*x**(ind+2)) \n", - " x_list.append(x)" - ], - "language": "python", + "data": { + "image/png": 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ZuVjZuVB2DpZXEnZUIiJK/iIJk18WXP1v+wP+198B4IUjo42Bc6DsXCgeo+cN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\nyV35j+AWAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, + "output_type": "display_data" + } + ], + "source": [ + "eplot.new_plot(x, 1/(1+x), '-s', label=r\"$y = \\frac{1}{1+x}$\", c='#fdb462', grid='on')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- For more advanced control of the grid, the easyplot `grid()` instance method is provided with a call signature of `grid(self, b=None, which='major', axis='both', **kwargs)` where `**kwargs` are passed to linespec of grid lines (eg: linewidth=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Demonstrate iter_plot using mode='dict'\n", - "eplot = EasyPlot(xlabel=r'$x$', ylabel='$y$', fontsize=16,\n", - " colorcycle=[\"#66c2a5\",\"#fc8d62\",\"#8da0cb\"], figsize=(8,5))\n", - "eplot.iter_plot(x, y_dict, linestyle=linestyle_dict, marker=marker_dict,\n", - " label=labels_dict, linewidth=3, ms=10, showlegend=True, grid='on')" - ], - "language": "python", + "data": { + "image/png": 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SFsszz0Tv58W4Tb+/5iz8qR8xyrZidSekJ7A8ko4cFjvlMD7lMD7lML7QOczmkX8T0A7U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\np4m+i6cRfR//YGbVmQxKo/3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H0s7wSdwuihU9bg296eT/NI1TDvLj528w+vH/VLt82Sq6CnmEqCq5v4QQphKddoZP/w4Q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\nDTNrgFBeGyvXMtD/uAjSUtF/Pw8lOb7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sNJd/nqIoXDW+Dx9vPIxBryM2MsiRnJhMJpYv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\nVVUdS0YLiqrJ2FvAzdMGAZCSYGLbzuPUNzQ5hhF+PX3wBbHEVAhPIHM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- "text": [ - "" - ] - } - ], - "prompt_number": 11 - }, + "output_type": "display_data" + } + ], + "source": [ + "eplot.new_plot(x, 1/(1+x), '-s', label=r\"$y = \\frac{1}{1+x}$\", c='#fdb462')\n", + "eplot.grid(which='major', axis='x', linewidth=2, linestyle='--', color='b', alpha=0.5)\n", + "eplot.grid(which='major', axis='y', linewidth=2, linestyle='-', color='0.85', alpha=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Log/Linear/Symlog plots\n", + "[[back to section overview](#sections)]\n", + "\n", + "Plot parameters `xscale` and `yscale` can be passed to easyplot instances with any of the following values: `['linear'|'log'|'symlog']`" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Demonstrate iter_plot using mode='array'. Both x_list and y_list are 2D arrays\n", - "eplot = EasyPlot(xlabel=r'$x$', ylabel='$y$', fontsize=16, \n", - " colorcycle=[\"#66c2a5\",\"#fc8d62\",\"#8da0cb\"], figsize=(8,5))\n", - "eplot.iter_plot(x_list, y_list, mode='array', linestyle=linestyle_list, marker=markers_list, \n", - " label=labels_list, linewidth=3, ms=10, showlegend=True, grid='on')" - ], - "language": "python", + "data": { + "image/png": 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lOQoikj2KKgHHV/0BVv0e8vvi/cdj/SdA/9Ox/JKwKxRJOwoKIpI9iqqITPky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\nj4KCiITCzAqA14BTganuPjf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H0s7wSdwuihU9bg296eT/NI1TDvLj528w+vH/VLt82Sq6CnmEqCq5v4QQphKddoZP/w4Q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\nDTNrgFBeGyvXMtD/uAjSUtF/Pw8lOb7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sNJd/nqIoXDW+Dx9vPIxBryM2MsiRnJhMJpYv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\nVVUdS0YLiqrJ2FvAzdMGAZCSYGLbzuPUNzQ5hhF+PX3wBbHEVAhPIHM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- "text": [ - "" - ] - } - ], - "prompt_number": 12 - }, + "output_type": "display_data" + } + ], + "source": [ + "eplot.new_plot(x, 1/(1+x), '-s', label=r\"$y = \\frac{1}{1+x}$\", c='#fdb462', yscale='log')\n", + "eplot.grid(which='minor', axis='both')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Modifying Plot color cycle\n", + "[[back to section overview](#sections)]\n", + "\n", + "easyplot provides the `colorcycle` plot parameter to specify the plot color cycle. If the `colorcycle` parameter is passed with every `add_plot` command, it will result in all plots using the first color of `colorcycle`. To avoid this, the `EasyPlot` object should be initialized with the `colorcycle` and other plot parameters without passing any x, y data as shown below. Subsequently, plots can be added using the `add_plot()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/cbook.py:136: MatplotlibDeprecationWarning: The set_color_cycle attribute was deprecated in version 1.5. Use set_prop_cycle instead.\n", + " warnings.warn(message, mplDeprecation, stacklevel=1)\n" ] }, { - "cell_type": "markdown", + "data": { + "image/png": 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YtaEc0zzVGyLxGE+3neW17ssYVZX1w328q6eN4tB1aQi161HufAyR5ZqX/bSfRUoujHTz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85pc2R2oOGT6QGk5AymYcaUpBZ/XEZk+ibtDzIXKXmkWehzSgkgMROafMJ9/07gTfKnbb1fWfTG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\nFYTmyRj4nXfLY1lO4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BidmUkth3dXprbMsRMK6TXkYmYZ2Rs5scPV+4CeAeJfM8fEgs2c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bK/IGDRmRD3a2laFWHugSKcWr709srpZwm1FM/IrICYQQ7Nq1S9FJCoVCGBpiN9kFAI7GY4q2\nH00nEBh9h23Jt+TjgCkjHJ0GFv5PQGRnc1Rt3avR9ru4+A1c77+q9PLodLq8ShOroXOZYTuuJjbh\n96cQGr6MZCCTdBCUtj0MwhWW1JiMOk3Jd3DMh9nxN4HAc+pFFf87wDsKik84Dtxjv6HR9gtefg69\nS/+gXNPjegYuU+HyKp9uPQi7XtrU/ckYXrh5CYFXVWV/U1cZjC3sSrCiSPGL9ycgCNL7tNxlxqFd\na8vnFAr61veBiNw6YC0Buf9XNr4hT/xiegCTYcngXkc4fKHjGDiGAyNFaNHV1aXR9mNd8rVUdsPg\nyBwuKfzDb7At+VoOASVZ70HPfwXS7LTrrPpyjS3mDe8LGBi7rBmM27VrlzIsky84ow4lj2dp+91c\nRuj6ACLz6gG9pPkEeGNh0/iEEDxyrBE8p9pinu9bKCjWdqH46S4iZ1gsFo3Y8OjoKHw+H7P4nM6A\n0raHlHXcN47YEsPkknBA1R8DRGZjksOAj60m1b7yz8OikybA0iELpidVgdGenh6YzVszuLceqoW+\nWtqQqD6O8OJ55Xu22r3ravblitaGUnTLiZGBj8IR+ZOsF39Q2ytZAEhZnSTxAkl+5XzqRQhUSr5L\nDA3YsY5mX66wG0z4bJtahnafD0CUha+JSQf7wy1bir8al/oXMb8ky+dwBE/c18xWs2/4Mmi/OmHI\nPfRZEBO7EuxsxK8Rvn66cVdRs2+bwfO8puTr8XgwNcVuEIMQgtL2h0E4ib1Px1YQnGI30AYAKP/D\nLLWEALD4H5mWfDucTypqCUiZMXZTLYk3NTXB7d54ynYzGJudMO+StPcoJyDkUaegjaUN62r25Qp3\nqRnH96nJd3D2eXiWg1uKyRLFxK+IvNDU1KQRyuzt7UU6ze40aSyth6VS7ScMjJ1COs7wA2NoAsq+\nrK593wJihZmDrxmet0qaVIIepsX7lP9eXl6umY4uFIQjKHmyA1RHQNunAE6WJjE7YW84vMndueHk\n4QZYzXo81PR92A1SYk+5Elmzb+t9a+Twh4HKZtxsp5+yuwAAIABJREFUSWLZJb13CHgcrf598NzW\n+9b2lzXgYFkDunxm7FtWT+0lj7WBt+bXW7kRPL4oTmeVeI/urka5i11SRqNBiK//vbImXUclKzxG\nSIkCvjl4GkKmxGt349G6W11kimCPsrIyNDU1Kev+/n7FzYcFdCYHHM1qyTcy14tEYGPturzAWaVD\ntPIC7wKB59e9PO/whMeRqt8DD6O0j4rSNLHVal3T6agQ2E82g7MbQJvnAKN8OOQNKGk7yaQ/90BP\nJWrKrdhb9SYeb/smMPNFpON3hr5fMfErIi8QQrBnzx7odNIHMRqNMrchcjQfB2+SyolUSEqlCoaa\nUSj9NGDOsEIisPDvmdoQ1dj2oTzwDLi0zMzxCbT11DBr9te5LTAcjwMOuWmYAtaSI4oe4lZhNurw\nscOT2FGhiqhOiX8g9UkyAOF18D/yKK53qnqBPZEuuEzs2Lhfrd6Dj42prMBUjQhjx9ZYgmyk0gJe\nOTUGQR6wqXRbmJZ4KZVkbxCTpY1sTpCHP7vhPfnixxO9mI1K09t6jsdvdBxV9BCL2H50d3drBPJ7\ne3uZCjtbKnfAWKoeNleGXldkSti8wBGtQP7SV5h6otsNVWhMfha6mMQsUoio77QVXOJdDc6kg/mk\nBahUq1Zmfjd0RjbT/hxH8OEjaTzQKLXKVFiGsTDybSaxt4rip7yIvGE2mzVTvlNTUxrPxK2C4w1w\ndjyKjJJ7MjiH8OyVjW/KB4QDqv6jdsp38T9vfE8emJ2dRdyrMj/x8tO4vPI1iJTNMEwyuIBEWpU5\nINNViLzmgxhnxLym5lGV/oqyHFg6gp9caMFKkM1DIy3GcSbyXVB593Gt8Oh+Zxp0ks0BglKK9KuT\nsKRlr2lDGt+tmcWp+ZFN7swdpy7OwBeQfh86HYcn729hW+LtfRMYU5lo7vHfBDFZN7gjP/SvzOON\nWfU99Mnmfai2FEu8txM8z2Pv3r3KgXBlZUVj57hVECL3/MpTvmIyDP8IYy/fsi8DBvnARuPA/L9V\n7Ry3CL/fj+Up9WdNOq/jevTrSKTZVICEZASRoNoqg+USxN5LI+1lNIUrxuAI/V/QcdK+vJJogq3+\ny5vcdHtQTPyKKAh1dXWoqVHFXfv6+ph6+RrsVRrhzNDkObZevvoaoOL/UNehl4DgS1sOGw6Hce2a\n6j6Sso8ibZ/Ccuwmrnuf2+DO3CCmk1gZeg0ZoWaEbcBMBcRQAoFfDG99U6dpafMWpc01nHLjzfHP\nIZUW8cqpMaSFrTMSlz3fUoSadQKHo71mcBQQf/4N0OjWN/XI+VlFqJkCeLHVi7iO4rnxyzl5+W6G\n0Wk/rt5Up4RPHqqHq4ThFK9nCvTUD5U12fchkMYdG9yRH4LJOL41dFZZ73LV4MHq4hTvB4HS0lJ0\ndKjae8PDw1heZjcowRttKG1TB87i3lHEPAPM4oMzAVV/CkXYOXEDWP7LLYdNp9O4fPmysp+JJh+S\n7l7EhBWcW/yfW97nKBWxMvQaxJT8zEoZQMbqQNIU/p8MgqYYHNKXvgokpYlhEUaUtH8VpSWFDcax\nRjHxK6IgZKZ8M8MKqVQKly9fZlqqsNcdgN6eMb+mWLn5KsQUw1KF40nA8VF1vfj/AMnJ9a/fBIIg\n4MqVK4rkgNVqRVu3qul0w/sC5iOF9xNmpFuERJbkQONDIDIzmhjyInZ1i9Nj3q8B8V55wSPp/hOk\nqMQ0LXqjOHVxZkvhxwPvYDSgamUdKPsCbCiVFpEAxJ//7ZbK+snZIMLvTihry+FaxKulpCwlCvja\nwHuIC4UzEoFwAj9/T7WLaq0vxa72tc3hCwFNJSC+/DUgM4VZXs90ilekIv725mkEktIDz6434vPt\nR5hqDhaRH9ra2jTDCleuXEEisY5tYgEwu1tgqVS9bANj7yIV8TKLD1O3tm965e+B8Kn1r98EGekW\njRrCni6ASMnebPgihvyvbOlHDk9fylJDAEqaT4JA6v9NL0cRfHNtW8mcEXpVo4bAVfxrcAxbWbaK\nYuJXRMHQ6/XYt2+fplSRLbC5VRCOh7PjMVWQNBnGyvAbbEsVFf8G0MsSKTQKzP0RIBbGXA4MDGhU\n5ffv348d5c+g0qLalZ2Z/wtE04VNQkfmryHuVX1oS9tOwtrVAMte1QA++OYYUgsFWt5FTgO+b6hr\n9/8CV9UxjSBp76AHQxOF/fyBxDQuZHnxNtrvQ3P5k+Ce+KJ60UQf6PnCNnUxmoL/pZsKGaqvscNx\nohG/1XUCBllCZjEWxHeHzxf0HhIEES+9PYpEUkrs7VYDHjvexCxpopSCvvZtYEVO3vVGcE/9LlOh\n5penbmDAL8UnAP5Z53E4DFubNC9iayCEYN++fYqoeyKRQG9vL9N9ztF8Ajqz5IRDxTRWbv4cYpqd\nlBWcvwZY71fXC/8BSBV2CJ2amsLsrJqU7dy5E83lh9FR+qTy3654voOlWGEOUonArEbY2lZ/ENbG\ndjiyJv5j1xYQG1ha6/bNkZwAFv5YXdsekaTE7iAUE78itgSXy6WxHxsbG2Pa76czOeBs+5CyTqxM\nIDx7eYM78gRnAar/DCDytGdyRLJ0y3PTnZmZwcTEhLLu7u5GSUkJOMLjWPWXYeIlVishBPH+3FcV\nNfpckQwtaIzDLVW7FC9e+8km6MrlnkKBYuWfBiHG8mS1UrNSiTeTNVmOAC7JPmlfVwXaGkqVS189\nPan0t+UcXozhvbn/FwKVmAy7oQaHqn4HhBCQpp0gB59QrqXvvwg6cX2dSGuDihT+l26q0i1GHiUf\n6QThOdRYSzQSL+eXJnFqIf9+v3cuzijuHBwheOqBFphNDL14r7wBOqjKbpCHPwviYjcwMrCygJez\npFuerN+BHmf1BncUcbtgMpmwd+9eZb20tMRUJ5Xj9XB2Pa4MgKVjfvhH3mKXXBIOqPq/AV2FtBb9\nwNy/BMT8mEu/348bN1QHmfr6esWLd2/5r8NlkkSbKQS8P/fVW6wxN4OQCGPl5i+Q2ecMjhrYZa94\n8+5KmLpU9j74i2GklvIc+hNjwNz/JpEIAKCvkwSv7zBGvZj4FbFltLa2orKyUllfu3YN4XB4gzvy\ng8ndDGvNHmUdmjyL+ErhJdlbX6ALqPh36jr0MhD4Qc63BwIBTV9fVVWVRqrBrCvF8Zp/oZRkl2M3\ncdnzrZzjC8kIfIM/B+QSqN5WgZJmVSqG6HmUfqwbxCixWmIwAf9LNxVLt00hxmWmU95E+XKpb0d2\nzyCE4LH7mlBik5LjZErAT94aUZivzUCpiHPz/wPBpHSK54kBJ2r+CHpOZZrIiY8DihsFhfjK10GD\nufc6hd+bVPr6AKDkqU7osvrujlW24L5K9UT/g9FLGAnkfqK/MbKM3kFVQPb+A7WoqShM4HUt0Nlh\nbV/fzvvB7TjBLP5SLIyvD76XSevRUVKBpxt3bXhPEbcXFRUVGjeK4eFhLC6yc93QW9woaT2prOPe\nEUTm2ElZgXcCVf8Far9fv2T1mGNymUwmcenSJaVdyOFwYOdO1c2J5/Q4UfNHMHBS60ks7cPp+T9X\nrB43AxXT8A3+TOnr4/RmqaIkT7ITQuB4rA28U9o3aEqE/8WB3IfmKAUW/5NEHgASmVD9lYIF77cT\nxcSviC2DEIK9e/dq1OjPnz+PZJJdKcHReAwGhzpMsnLzNaRjW2/UV1DyjORBm4HnK0B0c5/LZDKJ\nixcvKpuVzWbTTOplUGnZid1lqvTBiP9VjPrfwGZQNivZvo7wRjg7H7/FnUPnNKPkw2qTeHLCj/Cp\niU3jg1LA8ydAIlOi1wE1XwF02r41k0GHj5xsBc9L/1++/7+9M4+P6yrv/veZfZFGo9VabMm2ZMt7\n7NjxljjOSlYIKW2BlvV9SYBAy1Je2gAtoVDK0hdIoVCg9IUCLWsSCCFkI3EWZ/MWL7JlWZZkW9Yu\nzUia0az3vH/ckWbGlrVYE8uJzvfz0Ue+Z+4998gzc+5zzvM8vycY4eFnjk9px+BQ3685OZzeydow\n7w78zuqsc8RixXLL+2FUPDgyjPGbb6Fik+8sRhp7Cb2Yjj30blmAq/bs6hxvq93AAq/p7koqg+8e\nfoaB6OQZfB09wzz+fHqhUVft59IV8ya4YnqooX6M330HjJQhPW8hcs1f5qz/SDLOtxt2EEqMCmW7\ned+yy3V1jouQZcuWZemk7t27N6eLaE9ZfZZO6mDrTiIDuZNgwXMplH48fTz4QHbln3NgGAa7du0a\nSxC02WysX7/+LOkWr72ULRXpeMKu8EH2dE8ukaKUItj8NPHh0cWbUFh/A1Zndqa8xWnD/+bliN38\nbiQDkakvogd+CEMZYSpld5ubChch+puvyQl2uz3rixoOh3Oa7CEWK4X1N2BxpLTxklH6D/8eI5G7\nIGjK/haco0HQCXMXbAJdqmQyedZktWHDhjGNwzNZXnQb1flbxo53dX2f7vC5M+yUUgSadxAfq7Up\nFNa/AZtr/BWkq64Y7+Z0PF7o5XbCBybZMRj4z+xs5rJPgvuScU+dV+zlDVsXjh0fPxXkub0Ti8Ke\nHHqRA33pnayl/ptYXHDVuOeKtwDLrR9Ml3TrOYnx8H9MmOwR7xgi8Pu0S8yxqDCrrF0mDquND664\nkrxUzOhgPMJ3Gp4mNkE5q+FwjN8+2Tym11fsd3HjFYtyF9cXi2A88K/pkmyuPCxv/GDO4voMpfhh\n4wucDpv928TCB5dvo0DH9V2UiAiXXnrpWNLcq7GILli8DXv+aAiBYqDxkdwuov1vh/xb0sfdX4HQ\nC+c8fTSZI7MK1Lp16/B6x5cvqsy7lNXFfz523BT4A8cCj004pNDpVwhnZDP7Fl2Os2D8mun2Ei8F\nN2UsolsGGNrROmH/DD9l1jQfpeBPoOD2c54+22jDT5MzCgoKsuJUent7OXToUM7iSKwOD0XLbhpz\nQSZGBug/8jDKyI0+HhYXVH7ddHWCKWly+iOQPFtiRCnF/v37syartWvXjgmyjoeIsKn8LvxOsx6u\nQZJn2r/CYGz8mMjh9j2MdKeTZXyLLsdVOL5RM0re5TU469K7XYOPHiN26hxxMEOPZksv+G7PrsE5\nDssXF7N+ZXq366UDnRxsGt8l2zfSxPMd6f7neVazruzdE/YvVUuyd7ua96KevW/cc5ODUQbub4CE\naRhaC134b1mKWM5tlBW7vNy5/AosKbd723A/P2jciTGOcRmLJ7n/iSZCqXhJp8PKbVfX4bDnRkBW\nGQbGw9+HnpSav8VqGn2+3GUJ39eyl7196WoBf7lkI4ty2L8m9zgcDjZs2DBWFz0cDrNr164xtYCZ\nIhYrRctuzFhEx+hr+B3JeI7kuETMKj/O0d2uBHR8AqLN457e0tLCyZPpz+iyZcuyQofGY2Xxn7Ig\naxH9AzpDB8Y9d6TvOIOt6ZJs7tJ6vBVrxj13FFd9Cd5N6UV0eFc74b3nkBOLNkLHpxiLj3avN3f7\nLmK04afJKRUVFVm6VG1tbTQ3j/+FPx8c+WX4l6R1qWLBdgLNORQltc+Dyq+BOFM3aIHTHzXj4DJo\namrKyjxbvnw55eWTB+LbLC62VX0Sp9V0acaMYXac+uJZQcrh7kaG2tKrZHfZskknK0iVdLtlaTrZ\nw1AM3H+YeO8ZQcojr0Dn36eP3ZfBvE9NKQh526XzWViV3nV87PlWWtuzxz8U62RH+5fGkjny7PO4\nvPJjWGRyo8myZjtyabomsHr5YYxXnso6x4gkGLjvEEbINMrEZaPwLSuxuCffKav3z+NttWmNyH19\np/jF8T1Zn6GkYfDgU8309JsPQxG4dXstfl9u9PqUUqgdP4PmfWNtcu07kQW5cw39sb2Rx9rTC4dr\nKuvZOu/ikZTQnJuCggLWrVs3dtzf38/+/ftzuIj2UrT85rFkj2QkSH/DQxgzkDrKwuKGynszFtHD\n0P5XkMiWkTl9+nRW5aeqqqqsOMdzISJsLr+LQuciwEz2eOb0VxmIZMuwxIa6CBxN7wba88vxT7Ek\nW962GpxL0jI7g080Ez1+hqJBvB1OfSidzGGrNENlJHeZ+K8G2vDT5JwlS5ZkiTsfOXIka0U3Uzyl\nS8mv3jR2PNJ9JMtImjHu1TDvc+njkT3Q+SlIVd5obW3Nyrirrq5m8eKpP1Dz7GVsr/o7rKlM4uF4\nF0+3f4l4SkYmEjhJ4Ngfx853FFThr516/UiLw0bh7SuweMzJR0USDPzyEMnRyhvRJmj/MKSMMuw1\nUPl/pzxZWSzCrdtrKS0y3VFKwe92NNPVZxqXkUSQHae+SDS1U+qw5LF9/t04rVMvhSRX/jksShu6\n6omfoI7uMv8dTzJwfwOJntRkaxH8ty3DVjh19+X2yiVcX5Wu+fnk6aM82n449fcoHn++jbbT6Z3e\n67cspKYyd0Ha6qWHUHvTMZ6y/gYsq7dNcMX02NN7kl8c3z12vLZ4Pn+2eN0EV2guNioqKrLq0ra3\nt9PQ0JAz48+RV4p/6XVjx/HhLgYaH81deUz7PKj6Jkjqe5k4De13jXlQent72bcvvfApLCxkzZo1\nU57nRhfRbpvp4UgYIzx16osMx804vnh4gP6G36EMM5TD6vJlGbuTISL4b1mKvTzlxVEQ+O0RYu2p\neSEZMI2+ZMrjYcmDqn81k1wucqz33HPPbI/houR73/vePXfeeedsD+M1iYhQVlZGIBAYE+Hs7u7G\n5/NN6AqdDg5fBUZ0mHjI/NLFhjrAYsWZkQAyI5x15oQVThmUsRZI9tIeqM3K4C0pKWHdunVjbpmp\n4rEXUeCo5sSQKdEykuind+Qo5WoRgcMPjxmZNk8xxSvfiMU6vRWkxWXDUeMn0tADhkLFkkRbBnDX\nxZDOD4AxYJ5oLYT53wN72bT6t1ot1C7w09jaTyxukDQUTW0B5lfYeKH3nxmMmckWVrFz1YLPUORa\nNK3+RQSpXYtqOwypahuqeR+ULya4oy87g/fGJbjOQ0R5mb+czvAgHan4t8OBTvLtTlqPhNl/NO2+\n3nxJBetX5k5Wxdj/FGpHOmtclm5ArntXzuIGD/af5ruHn8VIuZ4W5RfzoRXbsVly46LWXDgKCwuJ\nRqNj+qCBQAARyRJ8ngl2TxEWu4toKsEjGQmQjAzhKspRHKutBJxLYSgloZLsg8g+AsktvPTynjH3\ndV5eHps3b8Zun94857B6KPeuoW3wWQwVJ6EinB7eQ5VjNcGGdAav2JyUrLp92nV4xWrBWVtE5Ggv\nKpoEQxE52otzoQPrwEchltoAELtp5LpnN1P+c5/7XMc999zzvcnO0zt+mlcFq9XK+vXr8fnMXRKl\nFLt3786ZPIGIUFC7HWfhwrG2obYXCHWMH+dxXhS9GwrfOXbYceow+15J1wz2+/1ZsTjTZX7+ZWwo\nS4sXjwyeoK/hwbEVqsWRR/GKW7GkkhGmi31eHv7bl0MqE1eNtKNa7oBkSsbE4oWqb4Nj4rjBc5Hn\ncfAn1y3F6TANimgixBNtnycQbQVAELZUfIRSd/0EvZwbcbiw3P4RKDTjfVQiSfDX+4g2p90t+Vct\nwr3q/DJsLSK8t34LS3wpo1fBYy+3svdwWrZlZV0xWy7J0WICMA4+i3r8J+mG6uXIje9DclTntzHQ\nxb8ffoZkatemzJ3PXSu247DmTm9Qc+EYrZBUUZHWWzx69CjHjx/P2T28FWvIq0rvBo/0NBLMZfhM\n3pUw77Njh8GBTl58cSeJhDnPOZ1ONm3aNCZgPV38zhq2VX0Si5if8Vikn+4Dv0orIVhsFK+4FZvb\nP1E358Sa56Doz1aNeVBIhlBtd0FkVGtUTPkrz2Xn7ONiQxt+mlcNu93Oxo0bx7Kzcm78WawULbsB\nR0Z2VvD40wznUpuq5GOQfyunh1ayp+NtKGUaUfn5+WzcuPGcGbxTZUnhDVxS8g7y4vmsHlyDNdW/\nxe6mZOWbsDpntkPqrPHjv6Ueq6ePosvvxeo2jT6Fw4zBcS2fpIeJKSl085brl+J0xiiq/yVWbzpR\nZWP5XSzI3zTB1ZMjnnwsb/k4ylvEoLGdqFEz9pp303y8l42fmTdV7BYrH1q5nYXeIkoGiikOpBNj\nltYUcv2W3FXmMA4+g3r0h4wFgc+rwfKmD+Usg/dIoJN/O7SDeCrZqdjp5WOrr8HnyF0dYc2FZ1Qu\nK1PmpaGhgWPHpi9Cfi7ya7ZklXULdzUQPL4jd8ZfwW1Q+gmCkXJeOPVe4knzM2+329i0adNYFvP5\nMs+zkssrPoYr6WZtcB2uZMqIFAtFy2/GkT+zHXtbkZvCP12JxROncNN3cPgz4tbL7ob8N8yo/wuN\nNvw0ryoul4vNmzePafyN6jXlqrqHWGwULbsZe15612ew5VmGTu2e4Kpp3YD25J3s7XgbCnNny2vv\nZdOi53DYc/P1qbVtYN3geuzKnAzjEudY8WmUKzcPbNeiMMXX/Rs2rxlYrZI2Bhs+SDKZG7eE359g\nwfpf4/Clk13CJ28kP7kxJ/3jKWKo8C+IkXYXu61H8M4/O9v6fHBZbayP1lMcSLvPhj3DeOoSWCbI\nEJ4OxitPoR79EWNGX1k1lj/5OJIjWZX9fe188+BTRFO7xQUONx9bfQ1FzvElMTSvLaxWKxs2bKCo\nKL0wOXLkCE1NTTkxzkwPylW4y9LJReHOQwSaHs+ZakK/upkX2u8ibpjPArslzOZFj+HLy81udLl9\nGRuHt+E2zO+UgUGz/yQJb27mUXtJgpIbvo+jJG30DR75c+LRm3PS/4VEG36aVx23282WLVvGVnVK\nKfbs2ZNV4mwmWGwOile+MUObynT7Bluem9GkqJTi+PHj7N23H5WS/8hzdLNlwX/givwCTv+fs7J9\np0ukr4W+Qw9iSQ0zLnH2F7zCycQrPHny89MuSXQWIwfg5LuxiOm+VEkbAy/dwUjTEvr+ez+JvskF\njCciFO/hiZOfJWSk9Q6DrdcSbL+EXzzSyKmu86wbnMKIJOj/5UGibemsZLccwqueR/32WxgNOye4\nenKSSYNHnmvl4NF0tuGwe5jTZR38vGU3vz9xcMafIeO5B1BP/Jgso+8tf4O4cxPv+mJ3C985/DSJ\nlHvX73Dz8dXXUOqeXjyT5uLGZrOxcePGrPi+xsZGDh6c2Wd0FBHBX3c17tK0KsNIz1H6j/wBNYHW\n5VTo6urihRdeIJ40TQ67ZYTN8/8fBTwBJ/83JKZepWc8YsM99O7/NRI39Q4NDBryD3HK2sTjJ/6e\nYPTUJD1MQrwLTv4vLEY6A3nw4O2EG6+k/2cHiLblUAfxAqANP80Fwe12s3Xr1qzkjoMHD3L48OGc\nTFoWm5PilW/McvuGTu9joPGR85q0lFI0NDRkSQ3k5eWxZflJXLaUkn7oSTj1AUj0n6OXifsfbt9H\n/5F0IofF4SW8sJLhVP99kSYeO/FpBqMTiySfk+EdcOoOSKYSOcRJjH8k1muWQTIGo/T9z/5z6/xN\nQu/IUR5tu3usFJsgLPW8l2Sf6d6NxpL8+tGjNDT3TdTNOUkGI/T/z37ip9I7e57Vfry+Y6bqjJFE\n/eEHGC89dF6foZFogvseb8oa3+LqAhx1UVTKEv9N235+1PQiifPY9VDJBOqxH6FefDDdOG9hzow+\npRQPtu3nPxufx0j9/SUuL59Ycz3lnoIZ96+5+Bg1/jLdvm1tbTnT+ROx4F9ybVZ1j+hAK70H7ycZ\nm2bd2hStra1Z1Y0cDgebV0UocKV08aKH4cQ7z6nzNxmR/lb6DtyHER/N8reiFi1lwGXOa+FEH4+f\n+Aydof0T9DLRDQ7DyXdBbDSuUkg6P87IKdO9q2JJBn51iJGDuSuv92ojOfPhv87YsGGD2rVr12wP\n43VHLBbjpZdeIhBIr5DKyspYt27dtDO6xkMlEwwcfZRIf1rPyZ5XRmH9DeeseDHeGPfu3UtPT7qW\na1FRERs2bMBht0PvN2Ago0yQrdwUfp5ivJwykgSPP024K21UWl0+ilfehs3l4+jAw+zu/n+M7hDZ\nLV62VnyEyrwpynEoA/q/D33/PtYHFr8pNeBeQ7RlgMBvDqPiKdkGi+C7ZjHuteVTjmdrHXyGFzu/\ng6FM3S8LVrZUfoTq/C109YW4//Emwhk1LjeuLmfr2qopu06jbQECDx5BjaT7yN++EM9lVRAKYNz3\nDehNr+Jl6WXIDe9F7FNLhOkPjvDAH48RGExXflm1pITrNtcQMxJ8u+FpGoPpiXyJr4w7l1+Ob4qu\nWTUcMMuwnc6Iw1q4CsutH0RyEHMXTSb4cdOLvNyTLiVX4SngI6uuptDpmXH/moubZDLJvn376OhI\niwr7fD7Wr19/zooX00EpxVDbCwy37xlrszi8FC27GUf+1BQADMPg4MGDnDiR9gZ4PB42bdpkjjF4\nH3T9E5AyWC15UP6PkHfN+B2OM8bh9r0pKS9znhOrk6LlN+EsqKIztJ9n2r9CIiVbJVi4tOw9LPHf\nOPW43aFHoPOzoEY9OzYo/wL4biTeHWLg14cwhtNVVTwbqsi/sgaxzs6emojsVkptmPQ8bfiNjzb8\nXj0SiQR79uyhuzudPen1elm3bh1+//llXmWilMFgy05CHekkD7E5KVxyHa6ihRNeGwgE2L1791gZ\nNjD1tNauXZtdN3Lgx9DzNcYMK3FC6SfNUj0TTCqJkQADjY8SD6WNSnt+OUXLbsLqSD+wTw2/zM7T\n944JIAOsKLqd1SVvnVgEORk0hZlDT6fb7FWp7N10YkSsY4jAfQ0Y4bRgq3tVGfnX1mJxnLv/hBFl\nT/cPaQ4+PtbmsOSxreoTlHnSuwTB4SgPPNFEXyDtCp9fns/N2xaR5zl39p5SitBL7Qw/0zr2X4tF\nKLhpCe4V6QeOioQxfvstONWYvrhkPpZb3o8UT5yF29DcxxMvtBFPpPXKLl9XxcbVacM3YST56bGX\n2dmVzp702V28b9nl1PsnziJWpxoxHvpuugwbICu2Ite/G8lBdm17KMD3Dz9Lx0h6J3S5v5w7l1+B\nx3Z+mZGa1x5KKQ4fPpyV4Wu321m7du2klS8QYVUvAAAcn0lEQVSmSqhjP8Hjz5Ke56wULNqKp3z1\nhMZTOBxm7969DAwMjLUVFBRw2WWX4cqMXQ49a4bMqIyqIYXvgZIPg5z7u2IkogSaHifS3zrWZnXm\nU7TiVuyedBxkX6SZZ9q/zEgiPY6a/MvZMO8OHNYJDGQjai7wA/+TbrPkQcVXwLt1rCk5FGXg14fS\nmqKAfb4P/xvrseadnxrDTNCG3wzRht+ri1KKI0eOZFX1EBHq6+upra3NSSZlqGM/wZbnzB2wFN6K\nNeTXbD5LF88wDI4dO3ZWsHRdXR319fXjj2f4Gei821SlH7vB1VD+D2eJeCqlUjIJT6OMDGOrtN5U\nkh9HVLQ/0szT7V9hJMOVXOKuZ1P5Xfgc4xg3oWeh83NpuRYwK3JUfBlsRWednhyMMvBAA4mutAvH\n6ndRcMtSHOOIFQ9EWnm+418JxtJi3D5HFVdW/R35jrOz5qKxBL/bcTxLCNnjsnHdlhrqqs8WOU0E\nIwQfPkr8ZPp8i9eO/03Lccw/ezwqmUA99TPUK0+mG6125Mo/Q9Zec9Z7FokmeOrlk1muXatVuOmK\nRSxdePb/j1KKR08d5v7WfWM2qCDcuGAFt1Svwn6GLp5KxFE7H0DtSmmWAYgg2/4UWX/DjD/ThlI8\n3dHEr1r2jmXuAlxZXsfbajdgzZEkjOa1RVtbG4cOHcqqi15TU8Py5ctnrDoAEA2cpP/II6hkehHq\nLFyIv+7qrMUqmN+Z9vZ2Dh48OCbXAlBZWckll1ySvXgeJXIETn/cFHgeu8FKKP88OM8Wxo+mBO6T\n0fS8O97ieZRwop9n2r9CfyT9rPHay9hS8dfjS01Fj0HH3RBrSrfZF0LVN8Cx8KzTjWiC4EONRJvT\nxqW4bfiuq8W9rPTs/l9FtOE3Q7Thd2Fob29n//79WfEpfr+f1atXU1Aw8zil6GAHA42PjGk6AVid\nPvx1V+H0LwDMXb4DBw6MiaSCGUuzbt26yVfOsVZz0opl6GpZC00ZGN+tIBYSI0GCx3cQDWRULxEL\nvoVb8VZMrFQfSQR5vuObdIbTu5dWsbO65K3UF95ialcl+s2au4P3Z19c+G4o+asJV84qniT46DFT\n6HlsbOC5tJK8y6uxOG0kjCgH+37Jkf4HUaQfLtX5W7hs3vsnXDkbhuLF/R08/0p2FvfSmkKu3lSN\n121HGYrwvg6Gn2lDxdKfA3tlPv7blk26cjYOPoN64ieQGcu5YBmWa/4SKa5EKUVT2wB/fPFElvu5\n0OdMVSCZ2DXaMNDBfzbuZCiefvCVu328c8km6grMiV2dbMT440+hLyMe05Vn7kDWrDizy2nTGQ7y\n46aXODaYsVNssfIXdZfpMmwaBgYG2L17N5FIeofd4/GwatUqysqmJ84+HomRAP2Nj5AIpZMwxObE\nV7MVz7zliAihUIhDhw5leXJEhGXLlrF48eKJFz7JIHR+2ly8jl3sgOIPgP8dYHFgJCIMtj6fFSID\n4K28BF/NFmQCgfKEEWV39w84HsxYJCIs8b+BNSVvN+cwIwL9/wH9PwQy5hLvdtMItZ47VEgpRejF\nUww/25b2VADOpcX4rl18wXb/tOE3Q7Thd+EIhULs3bs3K+4PzFXrkiVLsl0D50EyPkKg6QmiA21Z\n7RZfDaejZZzu7M5qLywsZO3atVOPlTFGoOcbEPx5drN9PcPJNzHc0wEZOzRWVwGF9TfgyJvaalAp\ng0P993Ow9xdZhleBvZyt7nIKQg8jmbuO1iKY9w+Qd9UU+1dEGnoYfLw5y/ASj5WBq3podP2BcCJt\ncFjFzqVl/4vagmunvIt1omOQh59pITSS3u102IQrKvxUtQZI9mW4egS8mxaQt3XBlGNlVM8pjIe/\nnxX3h8VK94qbeTa5lJPd2dnLyxcXc+3mahz2qVWzCETD/KBxJ0eD2Z+Vq71FvOlUM85je7IvqFmJ\n5Q3vRfJnVr5pMBbh9ycP8nTHsTFRZjDj+e5cdjmV3pmHRmheH0SjUQ4cOEBnZ2dWe1lZGStWrJhx\n1SRlJBls3UmoIztJQrzlDFhqaD3VnbXr6PF4WLduHYWFU/wOKMOMne77Nqj0PKFs1YR4F0M9AVQi\nvfgSmxN/7dW4Syav7TvKicGdvNT1XeJGej5wWXxsyV/GvPCTSOauozih9G+g4M+mVMMcIHoiQPD3\nTRhDGeO0W4htNUjWKRYW5a4s43how2+GaMPvwjLqaj127FjW5GGxWKipqWHRokVjWoDnw5ir9fiz\njMSSdEd89MXyxmRaRu9VX18/+er0XAw/A91fIBkdJBRZTmhkFUplrvQk5WreiMU6/Vis/kgLL3Z+\nm+HocWoJsowBPHJGJl/edVD2qXFdu5ORDEYI/qGJkVN99FQ2caJuNyFfdkZuqXs5G8vfj88xfeHk\nSDTBjl0nOdTUS2XMYGU4QWk8e/6xFroouHl8V/NkjLpajd2Pcto6j72uNTSf4Sryuu1cs6maJTXT\nN8gMpdjR0cT9rfsoGA5yQ2cbG/s6sWbOoTaH6Wq+5OoZuXb7IyGe7DjKjo4mohk7mRYRbpy/gpvH\ncTVrNKOu1kOHDhGPx7Neq6iooK6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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "#Advanced plotting\n", - "[[back to section overview](#sections)]\n", - "\n", - "`EasyPlot` objects provide access to their `figure` and `axes` objects via the `get_figure()` and `get_axes()` methods. These methods can be used in conjunction with regular object oriented matplotlib plotting methods (for example the set and get methods on the axes object) to build more complex and elaborate plots as shown in the examples below. The `line_list` instance variable can also be accessed to obtain a list of [`Line2D`](http://matplotlib.org/1.3.1/api/artist_api.html#matplotlib.lines.Line2D) items corresponding to the plots in the figure. These can also then be manipulated using standard matplotlib methods. **When plotting interactively, the `redraw()` method must be called on the easyplot object after any manipulation of the axes and figure objects in order to update the plot display with the latest changes.**" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "# Setup\n", + "colors = [\"#66c2a5\",\"#fc8d62\",\"#8da0cb\",\"#e78ac3\",\"#a6d854\",\"#ffd92f\",\"#e5c494\",\"#b3b3b3\"]\n", + "x = np.linspace(0,10,200)\n", + "\n", + "# Demo of color cycle\n", + "# Note the use of EasyPlot constructor with no x,y data to initialize colorcycle prior to\n", + "# adding plots to the figure\n", + "sinplot = EasyPlot(xlabel=r'$\\sin (3\\pi x/L)$', ylabel='$Amplitude$', fontsize=16, colorcycle=colors,\n", + " figsize=(10,5), linewidth=3)\n", + "\n", + "for index in range(8):\n", + " sinplot.add_plot(x, np.sin(3*np.pi*x/10 + index*np.pi/7))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Multiple plots in same figure iteratively using iter_plot\n", + "[[back to section overview](#sections)]\n", + "\n", + "The `EasyPlot` class has a very useful `iter_plot()` method to iterate through `x`, `y` data stored in dictionaries or 2D arrays and plot them in a figure using a single method call to `iter_plot`. The method signature of `iter_plot` is as follows:\n", + "``` python\n", + "def iter_plot(self, x, y, mode='dict', **kwargs):\n", + " \"\"\"\n", + " Plot multiple plots by iterating through x, y and parameter lists\n", + "\n", + " Arguments:\n", + " ==========\n", + " x : x values. 1D List/Array, Dictionary or Numpy 2D Array\n", + " y : y values. Dictionary or 2D Python array (List of Lists where each\n", + " sub-list is one set of y-data) or Numpy 2D Array\n", + " mode : y, labels and other parameters should either be a Dictionary\n", + " or a 2D Numpy array/2D List where each row corresponds to a \n", + " single plot ['dict'|'array']\n", + " **kwargs : Plot params as defined in __init__ documentation.\n", + " Params can either be:\n", + " scalars (same value applied to all plots),\n", + " dictionaries (mode='dict', key[val] value applies to each plot)\n", + " 1D Lists/1D Numpy Arrays (mode='array', param[index] applies to each\n", + " plot)\n", + " \"\"\"\n", + "```\n", + "The examples below demonstrate the use of `iter_plot` to generate multiple plots from a dataset using both `mode` settings, i.e., `mode='dict'` and `mode='array'`. Note that single value plot parameters that are passed to `iter_plot` (such as `linewidth`) are broadcast and applied to all plots in the figure." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup the x, y data and plot parameters for both modes\n", + "x = np.linspace(0, 10, 11)\n", + "dict_keys = ['x2', 'x3', 'x4']\n", + "labels_list = ['$y = x^2$', '$y = 0.1x^3$', '$y = 0.01x^4$']\n", + "markers_list = ['s', 'o', 'D']\n", + "linestyle_list = ['-', '--', ':']\n", + "\n", + "y_dict, marker_dict, labels_dict, linestyle_dict = {}, {}, {}, {}\n", + "x_list, y_list = [], [] # List of x and y data sets for mode='array'\n", + "\n", + "# Populate dict and list variables with data set\n", + "for ind, key in enumerate(dict_keys):\n", + " marker_dict[key] = markers_list[ind]\n", + " labels_dict[key] = labels_list[ind]\n", + " linestyle_dict[key] = linestyle_list[ind]\n", + " y_dict[key] = (0.1**ind)*x**(ind+2)\n", + " y_list.append((0.1**ind)*x**(ind+2)) \n", + " x_list.append(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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wVdZJDOrU2fEVuqB2jYXdGXls2XmSN9bsZc+BvHrPuVxJkiCEC7Kzs3n99deJ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\na9fAmIRETyIiYInh6OGDnDvt3DzbeyxyvG4sAbfVM+T/f1qcxawcwRK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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "# Demonstrate iter_plot using mode='dict'\n", + "eplot = EasyPlot(xlabel=r'$x$', ylabel='$y$', fontsize=16,\n", + " colorcycle=[\"#66c2a5\",\"#fc8d62\",\"#8da0cb\"], figsize=(8,5))\n", + "eplot.iter_plot(x, y_dict, linestyle=linestyle_dict, marker=marker_dict,\n", + " label=labels_dict, linewidth=3, ms=10, showlegend=True, grid='on')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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wVdZJDOrU2fEVuqB2jYXdGXls2XmSN9bsZc+BvHrPuVxJkiCEC7Kzs3n99deJ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\npRwaEtQ1rcvfy7GmGg42VnG4sZrOK20+FAMGb8N8k6+bwJF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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "##Using easyplot with subplots\n", - "[[back to section overview](#sections)]\n", - "\n", - "To create figures with subplots while taking advantage of an easyplot instance, the axes and figure can be created by the user using `pyplot.subplots`, `gridspec` or other common methods. The reference to the figure and and one of the subplot axes can be passed to the easyplot instance method - `new_plot()` to obtain the desired results as demonstrated below. The example below reuses the `sinplot` object from [this example](#colorcycle) as a template to easily generate new plots with desired axis labels and plot parameters." - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "# Demonstrate iter_plot using mode='array'. Both x_list and y_list are 2D arrays\n", + "eplot = EasyPlot(xlabel=r'$x$', ylabel='$y$', fontsize=16, \n", + " colorcycle=[\"#66c2a5\",\"#fc8d62\",\"#8da0cb\"], figsize=(8,5))\n", + "eplot.iter_plot(x_list, y_list, mode='array', linestyle=linestyle_list, marker=markers_list, \n", + " label=labels_list, linewidth=3, ms=10, showlegend=True, grid='on')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Advanced plotting\n", + "[[back to section overview](#sections)]\n", + "\n", + "`EasyPlot` objects provide access to their `figure` and `axes` objects via the `get_figure()` and `get_axes()` methods. These methods can be used in conjunction with regular object oriented matplotlib plotting methods (for example the set and get methods on the axes object) to build more complex and elaborate plots as shown in the examples below. The `line_list` instance variable can also be accessed to obtain a list of [`Line2D`](http://matplotlib.org/1.3.1/api/artist_api.html#matplotlib.lines.Line2D) items corresponding to the plots in the figure. These can also then be manipulated using standard matplotlib methods. **When plotting interactively, the `redraw()` method must be called on the easyplot object after any manipulation of the axes and figure objects in order to update the plot display with the latest changes.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Using easyplot with subplots\n", + "[[back to section overview](#sections)]\n", + "\n", + "To create figures with subplots while taking advantage of an easyplot instance, the axes and figure can be created by the user using `pyplot.subplots`, `gridspec` or other common methods. The reference to the figure and and one of the subplot axes can be passed to the easyplot instance method - `new_plot()` to obtain the desired results as demonstrated below. The example below reuses the `sinplot` object from [this example](#colorcycle) as a template to easily generate new plots with desired axis labels and plot parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Reuses sinplot template from one of the previous examples for labels, linewidth and fontsize\n", - "x = np.linspace(0, 10, 200)\n", - "fig, axes = plt.subplots(2, 1, figsize=(10,6)) # Create fig and axes for subplots externally\n", - "\n", - "# Supply fig and axes instance to EasyPlot object\n", - "sinplot.new_plot(x, np.sin(3*np.pi*x/10 + np.pi/8), fig=fig, ax=axes[0], color=\"#fc8d62\") \n", - "sinplot.new_plot(x, np.sin(3*np.pi*x/10 + 9*np.pi/8), fig=fig, ax=axes[1], color=\"#66c2a5\")\n", - "fig.set_tight_layout(True)" - ], - "language": "python", + "data": { + "image/png": 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J1DPfuYVw0p1Qtcl+fvOo0W3bOd5gIg3g0ONss2JM1fBqM7P7gpn91MyuMbMh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8ayAtSQlY20EaPAWimqe6iTFWQVDCechr5wD5jfhda0Aa8gGEvYO6iTGmB0qv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- "text": [ - "" - ] - } - ], - "prompt_number": 16 - }, + "output_type": "display_data" + } + ], + "source": [ + "# Reuses sinplot template from one of the previous examples for labels, linewidth and fontsize\n", + "x = np.linspace(0, 10, 200)\n", + "fig, axes = plt.subplots(2, 1, figsize=(10,6)) # Create fig and axes for subplots externally\n", + "\n", + "# Supply fig and axes instance to EasyPlot object\n", + "sinplot.new_plot(x, np.sin(3*np.pi*x/10 + np.pi/8), fig=fig, ax=axes[0], color=\"#fc8d62\") \n", + "sinplot.new_plot(x, np.sin(3*np.pi*x/10 + 9*np.pi/8), fig=fig, ax=axes[1], color=\"#66c2a5\")\n", + "fig.set_tight_layout(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Another example\n", - "fig, axes = plt.subplots(figsize=(9,9), nrows=2, ncols=2) # Create fig and axes\n", - "\n", - "# Setup EasyPlot object with common plot parameters\n", - "eplot = EasyPlot(xlabel='x', ylabel='y(x)', title='Plot title', fs=14, lw=3)\n", - "\n", - "# Loop through subplot axes and use eplot template to create new plots\n", - "for (x_ind,y_ind), ax in np.ndenumerate(axes):\n", - " eplot.new_plot(x, x**(x_ind+y_ind-1), fig=fig, ax=ax, c=\"#8da0cb\") \n", - "fig.tight_layout()" - ], - "language": "python", + "data": { + "image/png": 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V5gvdQLgM+zwFfA94S0T+EXd+7D8D+3EvEIaj3+M+Mf4VqBeRi0/62oFwG/5R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\nondimt9xzAVIjY3nnqGTKWu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792PIkCF4/vnnERQUhOeffx4FBQXWLqFJ1dyCR0RNsOfeRUTUkg47uESv12Pr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- "text": [ - "" - ] - } - ], - "prompt_number": 17 - }, + "output_type": "display_data" + } + ], + "source": [ + "# Alternately you can now use the convenience methods of make_subplots and set_subplot to get the same effect as follows\n", + "esub = EasyPlot()\n", + "esub.make_subplots(2,1)\n", + "esub.set_subplot(0,0)\n", + "esub.add_plot(x, np.sin(3*np.pi*x/10 + np.pi/8), color=\"#fc8d62\")\n", + "esub.set_subplot(1,0)\n", + "esub.add_plot(x, np.sin(3*np.pi*x/10 + 9*np.pi/8), color=\"#66c2a5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/ipykernel/__main__.py:9: RuntimeWarning: divide by zero encountered in reciprocal\n" ] }, { - "cell_type": "markdown", + "data": { + "image/png": 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dCd5WJ3hmZmZWZ7mcgyepsFr8MmADyUUWl5Dck/HL1f68PqdoneCZmZlZneUy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\nyp8TxPsbM5thZhOAfwamR15vadP2FwSHdL8E3Bh5LgZKIkn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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "##Custom plot modifications via axes instance\n", - "[[back to section overview](#sections)]\n", - "\n" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "# Another example\n", + "fig, axes = plt.subplots(figsize=(9,9), nrows=2, ncols=2) # Create fig and axes\n", + "\n", + "# Setup EasyPlot object with common plot parameters\n", + "eplot = EasyPlot(xlabel='x', ylabel='y(x)', title='Plot title', fs=14, lw=3)\n", + "\n", + "# Loop through subplot axes and use eplot template to create new plots\n", + "for (x_ind,y_ind), ax in np.ndenumerate(axes):\n", + " eplot.new_plot(x, x**(x_ind+y_ind-1), fig=fig, ax=ax, c=\"#8da0cb\") \n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Custom plot modifications via axes instance\n", + "[[back to section overview](#sections)]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Setup EasyPlot object with common plot parameters\n", - "x = np.linspace(0, 10, 11)\n", - "eplot = EasyPlot(x, x**2, xlabel='x', ylabel='y(x)', figsize=(6,5),\n", - " title='Plot title', fs=14, lw=3, xlim=(0,11), c=\"#fc8d62\")\n", - "ax = eplot.get_axes()\n", - "\n", - "# Modify plot by direct manipulation of axes instance\n", - "ax.bar(x, x**2, align=\"center\", width=0.5, alpha=0.6, color=\"#a6d854\")\n", - "ax.set_xticks([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n", - "ax.set_xticklabels([r'$\\alpha$', r'$\\beta$', r'$\\gamma$', r'$\\delta$', r'$\\epsilon$',\n", - " r'$\\chi$', r'$\\nu$', r'$\\mu$', r'$\\omega$', r'$\\phi$'], fontsize=18)\n", - "eplot.redraw() # Update plot canvas with axes modifications" - ], - "language": "python", + "data": { + "image/png": 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UKCJ5onjnXo56dgVFVeHU4GTCWDNrPLtG9Y+5MpFAgSKSB3psLWfCcysprAm3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\ninAB6/3AN4GvAn8gnP3XYZj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w/n14OxrfEsrrOeJo37aocDbrqDXVETd9QiIjIwPfffcdfvjhBzg5OWH48OE4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\nCqX/U3grtZBhQWQjuIdBmpH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- "text": [ - "" - ] - } - ], - "prompt_number": 15 + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "# Setup EasyPlot object with common plot parameters\n", + "x = np.linspace(0, 10, 11)\n", + "eplot = EasyPlot(x, x**2, xlabel='x', ylabel='y(x)', figsize=(6,5),\n", + " title='Plot title', fs=14, lw=3, xlim=(0,11), c=\"#fc8d62\")\n", + "ax = eplot.get_axes()\n", + "\n", + "# Modify plot by direct manipulation of axes instance\n", + "ax.bar(x, x**2, align=\"center\", width=0.5, alpha=0.6, color=\"#a6d854\")\n", + "ax.set_xticks([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n", + "ax.set_xticklabels([r'$\\alpha$', r'$\\beta$', r'$\\gamma$', r'$\\delta$', r'$\\epsilon$',\n", + " r'$\\chi$', r'$\\nu$', r'$\\mu$', r'$\\omega$', r'$\\phi$'], fontsize=18)\n", + "eplot.redraw() # Update plot canvas with axes modifications" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "2.7.13" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/easyplot/.DS_Store b/easyplot/.DS_Store new file mode 100644 index 0000000..5008ddf Binary files /dev/null and b/easyplot/.DS_Store differ diff --git a/easyplot/easyplot.py b/easyplot/easyplot.py index 2cf7b64..2b29f92 100644 --- a/easyplot/easyplot.py +++ b/easyplot/easyplot.py @@ -5,11 +5,15 @@ import matplotlib.pyplot as plt import matplotlib as mpl - +import numpy as np +import matplotlib.gridspec as gridspec +from mpl_toolkits.mplot3d.axes3d import Axes3D, get_test_data +from IPython import embed if not plt.isinteractive(): - print("\nMatplotlib interactive mode is currently OFF. It is " + print("\nMatplotlib interactive mode was currently OFF. It is " "recommended to use a suitable matplotlib backend and turn it " - "on by calling matplotlib.pyplot.ion()\n") + "on by calling matplotlib.pyplot.ion()\n. Turning it on automatically now...") + plt.ion() class EasyPlot(object): """ @@ -75,12 +79,15 @@ def __init__(self, *args, **kwargs): """ self._default_kwargs = {'fig': None, 'ax': None, + 'gs':None, 'figsize': None, 'dpi': mpl.rcParams['figure.dpi'], 'showlegend': False, 'fancybox': True, 'loc': 'best', - 'numpoints': 1 + 'numpoints': 1, + 'return_handle':False, + 'specialfunc': False } # Dictionary of plot parameter aliases self.alias_dict = {'lw': 'linewidth', 'ls': 'linestyle', @@ -101,6 +108,7 @@ def __init__(self, *args, **kwargs): # Mapping between plot parameter and corresponding axes function to call self._ax_funcs = {'xlabel': 'set_xlabel', 'ylabel': 'set_ylabel', + 'zlabel': 'set_zlabel', 'xlim': 'set_xlim', 'ylim': 'set_ylim', 'title': 'set_title', @@ -141,6 +149,10 @@ def add_plot(self, *args, **kwargs): ax, fig = self.kwargs['ax'], self.kwargs['fig'] ax.ticklabel_format(useOffset=False) # Prevent offset notation in plots + if 'fontsize' in self.kwargs: + self.set_fontsize(self.kwargs['fontsize']) + ax.yaxis.label.set_size(self.kwargs['fontsize']) + ax.xaxis.label.set_size(self.kwargs['fontsize']) # Apply axes functions if present in kwargs for kwarg in self.kwargs: @@ -155,7 +167,13 @@ def add_plot(self, *args, **kwargs): plot_kwargs = {kwarg: self.kwargs[kwarg] for kwarg in self._plot_kwargs if kwarg in self.kwargs} - line, = ax.plot(*self.args, **plot_kwargs) + if self.kwargs['specialfunc']: + if self.kwargs['specialfunc']=='scatter': + plot_kwargs.pop('markersize',None) + plot_kwargs.pop('linewidth',None) + line = ax.scatter(*self.args, **plot_kwargs) + else: + line, = ax.plot(*self.args, **plot_kwargs) self.line_list.append(line) # Display legend if required @@ -165,14 +183,13 @@ def add_plot(self, *args, **kwargs): leg = ax.legend(**legend_kwargs) if leg is not None: leg.draggable(state=True) - - if 'fontsize' in self.kwargs: - self.set_fontsize(self.kwargs['fontsize']) self._delete_uniqueparams() # Clear unique parameters from kwargs list if plt.isinteractive(): # Only redraw canvas in interactive mode self.redraw() + if self.kwargs['return_handle']: + return line def update_plot(self, **kwargs): """"Update plot parameters (keyword arguments) and replot figure @@ -250,6 +267,18 @@ def iter_plot(self, x, y, mode='dict', **kwargs): else: print('Error! Incorrect mode specification. Ignoring method call') + def make_subplots(self,*args, **kwargs): + if self.kwargs['fig'] is None: + self.kwargs['fig'] = plt.figure(figsize=self.kwargs['figsize'], + dpi=self.kwargs['dpi']) + self.kwargs['gs'] = gridspec.GridSpec(*args, **kwargs) + + def set_subplot(self,rows,cols,projection=None): + self.kwargs['ax'] = plt.subplot(self.kwargs['gs'][rows,cols]) + if not (projection is None): + self.kwargs['ax'].remove() + self.kwargs['ax'] = self.kwargs['fig'].add_subplot(self.kwargs['gs'][rows,cols],projection=projection) + def autoscale(self, enable=True, axis='both', tight=None): """Autoscale the axis view to the data (toggle). @@ -304,18 +333,22 @@ def redraw(self): def set_fontsize(self, font_size): """ Updates global font size for all plot elements""" - mpl.rcParams['font.size'] = font_size - self.redraw() + # mpl.rcParams['font.size'] = font_size + # mpl.rcParams['axes.labelsize'] = font_size + # self.redraw() #TODO: Implement individual font size setting -# params = {'font.family': 'serif', -# 'font.size': 16, -# 'axes.labelsize': 18, -# 'text.fontsize': 18, -# 'legend.fontsize': 18, -# 'xtick.labelsize': 18, -# 'ytick.labelsize': 18, -# 'text.usetex': True} -# mpl.rcParams.update(params) + params = {'font.family': 'serif', + 'font.size': font_size, + 'axes.labelsize': font_size, + # 'text.fontsize': font_size, + 'font.size': font_size, + 'legend.fontsize': font_size, + 'xtick.labelsize': font_size, + 'ytick.labelsize': font_size, + #'text.usetex': True + } + mpl.rcParams.update(params) + self.redraw() # def set_font(self, family=None, weight=None, size=None): # """ Updates global font properties for all plot elements @@ -367,4 +400,36 @@ def _reset(self, reset=False): self.kwargs['fig'] = None self.kwargs['ax'] = None if reset: - self.kwargs = self._default_kwargs.copy() \ No newline at end of file + self.kwargs = self._default_kwargs.copy() + + def savefig(self,*args,**kwargs): + (self.kwargs['fig']).savefig(*args,**kwargs) + +class EasyPlotManager(EasyPlot): + """docstring for EasyPlotManager""" + def __init__(self,*args, **kwargs): + super(EasyPlotManager, self).__init__(*args, **kwargs) + self.handles = {} + + def adddata(self,label,data,style='bo',xlabel=r'$x$',ylabel=r'$y$',showlegend=False,markersize=10,alpha=1,linewidth=2.,**kwargs): + if len(data)==3: + self.handles[label] = self.add_plot(data[0],data[1],data[2],label=label,showlegend=showlegend,markersize=markersize,alpha=alpha,xlabel=xlabel,ylabel=ylabel,linewidth=linewidth,return_handle=True,**kwargs) + else: + self.handles[label] = self.add_plot(data[0],data[1],style,label=label,showlegend=showlegend,markersize=markersize,alpha=alpha,xlabel=xlabel,ylabel=ylabel,linewidth=linewidth,return_handle=True,**kwargs) + + def remove(self,label): + self.handles[label].remove() + + def updatedata(self,label,data): + try: + (self.handles[label]).set_xdata(data[0]) + (self.handles[label]).set_ydata(data[1]) + except: + # embed() + (self.handles[label]).set_offsets(np.vstack((data[0],data[1])).T) + (self.handles[label]).set_3d_properties(data[2],'z') + + def updateplot(self): + # embed() + (self.kwargs['fig']).canvas.draw() + (self.kwargs['fig']).canvas.flush_events() \ No newline at end of file