{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%This notebook demonstrates the use of the workpackage template, replace with your own.\n",
    "\n",
    "\\documentclass[english]{workpackage}[1996/06/02]\n",
    "\n",
    "% input the common preamble content (required by the ipnb2latex converter)\n",
    "\\input{header.tex}\n",
    "\n",
    "\n",
    "% then follows the rest of the preamble to be placed before the begin document\n",
    "% this preamble content is special to the documentclass you defined above.\n",
    "\\WPproject{Computational Radiometry}           % project name\n",
    "\\WPequipment{}         % equipment name\n",
    "\\WPsubject{05b Plotting with Pyradi --- Polar and 3D}           % main heading \n",
    "\\WPconclusions{} \n",
    "\\WPclassification{} \n",
    "\\WPdocauthor{CJ Willers}\n",
    "\\WPcurrentpackdate{\\today}\n",
    "\\WPcurrentpacknumber{} % work package number\n",
    "\\WPdocnumber{}         % this doc number hosts all the work packages\n",
    "\\WPprevpackdate{}      % work package which this one supersedes\n",
    "\\WPprevpacknumber{}    % work package which this one supersedes\n",
    "\\WPsuperpackdate{}     % work package which comes after this one\n",
    "\\WPsuperpacknumber{}   % work package which comes after this one\n",
    "\\WPdocontractdetails{false}\n",
    "\\WPcontractname{}      % contract name \n",
    "\\WPorderno{}           % contract order number\n",
    "\\WPmilestonenumber{}   % contract milestone number\n",
    "\\WPmilestonetitle{}    % contract milestone title\n",
    "\\WPcontractline{}      % contract milestone line number \n",
    "\\WPdocECPnumber{}      % ecp\\ecr number\n",
    "\\WPdistribution{}\n",
    "\n",
    " \n",
    "\n",
    "% this is entered just before the end{document}\n",
    "\\newcommand{\\atendofdoc}{\n",
    "\\bibliographystyle{IEEEtran}\n",
    "\\bibliography{references}\n",
    "}\n",
    "\n",
    "%and finally the document begin.\n",
    "\\begin{document}\n",
    "\\WPlayout\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5b Plotting With Pyradi: Polar and Three-Dimensional Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook forms part of a series on [computational optical radiometry](https://github.com/NelisW/ComputationalRadiometry#computational-optical-radiometry-with-pyradi).  The notebooks can be downloaded from [Github](https://github.com/NelisW/ComputationalRadiometry#computational-optical-radiometry-with-pyradi). These notebooks are constantly revised and updated, please revisit from time to time.  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The date of this document and module versions used in this document are given at the end of the file.  \n",
    "Feedback is appreciated: neliswillers at gmail dot com."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The [`pyradi.ryplot`](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html) library remembers [John Hunter](https://en.wikipedia.org/wiki/John_D._Hunter), neurologist and the original author and main spirit behind early Matplotlib. His departure  left a void.\n",
    "\n",
    "The pyradi library has a module, [`pyradi.ryplot`](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html),\n",
    "to simplify plotting. The module is a productivity wrapper around Matplotlib's [pyplot library](https://matplotlib.org/stable/api/pyplot_summary.html), which is in turn a wrapper around [Matplotlib](https://matplotlib.org/stable/).\n",
    "\n",
    "All that can can be done `pyradi.ryplot` can also be done with raw Matplotlib or pyplot.  The productivity gained with `pyradi.ryplot` stems from the fact that plots and plot properties are all combined into a single function call. So, with just one call a complete graph can be drawn. The code is compact and there is no need to hunt through many pages of documentation to find the appropriate command for some graph attribute. You would, however, have to consult the ryplot documentation for information on the functions long list of parameters.\n",
    "\n",
    "An understanding of the [Matplotlib histroy and architecture](http://www.aosabook.org/en/matplotlib.html) is not essential to use `pyradi.ryplot` but it may be useful background reading.  `pyradi.ryplot` covers a relatively small part of the full scope of raw Matplotlib or pyplot - there are imense power and many more graphs available in Matplotlib and pyplot, so if `pyradi.ryplot` is too limiting in some area, consider reading wider.\n",
    "\n",
    "This notebook covers a general introduction to plotting and creating polar and three-dimensional plots. Other plot types are covered in the next notebook in the series.\n",
    "\n",
    "For an introduction to the `ryplot.Plotter` class, which provides the plotting functionality discussed in this notebook, please see [Plotting With Pyradi: General Plotter Functionality and Cartesian Plots](http://nbviewer.ipython.org/urls/raw.githubusercontent.com/NelisW/ComputationalRadiometry/master/05a-PlottingWithPyradi-GeneralAndCartesian.ipynb?create=1). The information given there is essential for using the functions described in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "from IPython.display import Image\n",
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pyradi.ryplot as ryplot\n",
    "from matplotlib import cm\n",
    "import matplotlib.mlab as mlab\n",
    "\n",
    "# %reload_ext autoreload\n",
    "# %autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Polar plotting routines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[ryplot.Plotter.polar](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html#pyradi.ryplot.Plotter.polar) provides a polar plot facility. \n",
    "Given an existing figure, this function plots in a specified subplot position. Note that the radial values or ordinates can be more than one column, each column representing a different line in the plot. This is convenient if large arrays of data must be plotted. If more than one column is present, the label argument can contain the legend labels for each of the columns/lines.  The scale for the radial ordinates can be set with `rscale`. The number of radial grid circles can be set with `rgrid` - this provides a somewhat better control over the built-in radial grid in matplotlib. thetagrids defines the angular grid interval.  The angular rotation direction can be set to be `clockwise` or `counterclockwise`. Likewise, the rotation offset where the plot zero angle must be, is set with `zerooffset`.\n",
    "\n",
    "[ryplot.Plotter.polar](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html#pyradi.ryplot.Plotter.polar) extends the Matplotlib [polar](http://matplotlib.org/api/pyplot_api.html) function in a critical area: on a polar plot negative values are shifted by $\\pi$ radians. Hence, negative values are shown on the opposite side of the origin as where they should be. On a polar plot negative values and angle are 'confused' with each other.  [ryplot.Plotter.polar](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html#pyradi.ryplot.Plotter.polar) provides two means to handle negative values: \n",
    "\n",
    "1. Draw the value with $\\pi$ phase shift, but highlight the negative values, so you know when it is negative.\n",
    "\n",
    "2. Draw the polar plot with a zero offset: the zero value is not at the centre of the circle, but forms a ring around the centre, where the centre is some negative value.\n",
    "\n",
    "For some obscure reason Matplitlib version 1.13 does not draw negative values on the polar plot.  We therefore force the plot by making the values positive and then highlight it as negative.\n",
    "\n",
    "The calling signature is\n",
    "\n",
    "`polar(plotnum, theta, r, ptitle=None, plotCol=None, label=[], labelLocation=[-0.1, 0.1], highlightNegative=True, highlightCol='#ffff00', highlightWidth=4, legendAlpha=0.0, rscale=None, rgrid=None, thetagrid=[30], direction='counterclockwise', zerooffset=0, titlefsize=12, drawGrid=True, zorders=None, clip_on=True, markers=[], markevery=None)`\n",
    "\n",
    "- `plotnum (int)`  subplot number, 1-based index.\n",
    "- `theta (np.array[N,] or [N,1])`  angular abscissa in radians.\n",
    "- `r (np.array[N,] or [N,M])`  radial ordinates - could be M columns.\n",
    "- `ptitle (string)`  plot title (optional).\n",
    "- `plotCol ([strings])`  plot colour and line style, list with M entries, use default if None (optional).\n",
    "- `label  ([strings])`  legend label, list with M entries (optional).\n",
    "- `labelLocation ([x,y])`  where the legend box should located, in normalized axes coordinates from the \n",
    "lower-left corner, using [bbox_to_anchor](https://matplotlib.org/stable/api/legend_api.html) (optional).\n",
    "- `highlightNegative (bool)`  indicate if negative data must be highlighted. This highlight is done with a second colour, broader than the data line itself. (optional).\n",
    "- `highlightCol (string)`  negative highlight colour string (optional).\n",
    "- `highlightWidth (int)`  negative highlight line width in points(optional).\n",
    "- `legendAlpha (float)`  transparancy for legend box, allowing you to see through the box to the data underneath. Has no effect for vector graphics graphs, only works for raster rendering (optional).\n",
    "- `rscale ([rmin, rmax])`  radial plotting limits. If `None` calculate from data sets. If rmin is negative the zero is a circle and rmin is at the centre of the graph (optional).\n",
    "- `rgrid ([rinc, numinc])`  radial grid. If rgrid is `None` use pyplot default. If rgrid is `None` don't show labels. If rinc=0 then numinc is number of intervals. If rinc is not zero then rinc is the increment and numinc is ignored (optional).\n",
    "- `thetagrids (float)`  theta grid interval [degrees], if `None` don't show labels (optional).\n",
    "- `direction (string)` direction in increasing angle, 'counterclockwise' (default) or 'clockwise' (optional).\n",
    "- `zerooffset (float)` rotation offset where scale zero should be [rad]. Positive zero-offset rotation is counterclockwise from 3'o'clock  (optional).\n",
    "- `titlefsize (int)`  title font size, default 12pt (optional).\n",
    "- `drawGrid (bool)` draw a grid on the graph (optional).\n",
    "- `zorders` ([int]) list of zorder for drawing sequence, highest is last (optional).\n",
    "- `clip_on` (bool) clips objects to drawing axes (optional).\n",
    "- `markers` ([string]) markers to be used for plotting data points (optional)\n",
    "- `markevery` (int | (startind, stride)) subsample when using markers (optional)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following example shows variation in several of the plot parameters, including direction, zero offset and theta grid intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = np.arange(0, 3.01, 0.01).reshape(-1, 1)\n",
    "theta = 2*np.pi*r\n",
    "r2 = np.hstack((r,r**2))\n",
    "P = ryplot.Plotter(3, 2, 2,'Polar plots with variations', figsize=(12,8))\n",
    "\n",
    "P.polar(1,theta, r, \"Single Polar\", label=['Single'],legendAlpha=0.5,rscale=[0,3],\n",
    "        rgrid=[0.5,3], zerooffset=0)\n",
    "P.polar(2,theta, r2, \"Array Polar\", label=['A', 'B'],legendAlpha=0.5,rscale=[2,6],\n",
    "        rgrid=[2,6], thetagrid=[15], direction=u'clockwise', zerooffset=np.pi/4)\n",
    "P.polar(3,theta, r, \"Single Polar\", label=['Single'],legendAlpha=0.5,rscale=[0,3],\n",
    "        rgrid=[0,3], direction=u'clockwise', zerooffset=np.pi/2)\n",
    "P.polar(4,theta, r2, \"Array Polar\", label=['A', 'B'],legendAlpha=0.5,rscale=[0,9]\n",
    "        ,rgrid=[0,6], thetagrid=[45], direction=u'counterclockwise', zerooffset=-np.pi/2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graphs can be overwritten with additional information once created, without losing the original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = np.arange(0, 3.01, 0.01).reshape(-1, 1)\n",
    "theta = 2*np.pi*r\n",
    "r2 = np.hstack((r,r**2))\n",
    "P = ryplot.Plotter(3, 1, 2,'Polar Plots', figsize=(10,3))\n",
    "P.polar(1,theta, r, \"Single Polar\", label=['Single 1'],legendAlpha=0.5,rscale=[0,3],\n",
    "        rgrid=[0.5,3])\n",
    "P.polar(2,theta, r2, \"Array Polar\", label=['A', 'B'],legendAlpha=0.5,rscale=[2,6],\n",
    "        rgrid=[2,6], thetagrid=[45], direction=u'clockwise', zerooffset=0)\n",
    "\n",
    "#plot again on top of existing graphs\n",
    "rr = np.arange(0.1, 3.11, 0.01).reshape(-1, 1)\n",
    "thetar = 2*np.pi*rr\n",
    "P.polar(1,thetar, 0.5 * rr, \"Single Polar\", plotCol='r',label=['Single 2'],legendAlpha=0.5,\n",
    "        rscale=[0,3], rgrid=[0.5,3])\n",
    "P.polar(2,thetar, 0.75 * rr, \"Array Polar\", plotCol='g',label=['A', 'B'],legendAlpha=0.5,\n",
    "        rscale=[0,6], rgrid=[2,6], thetagrid=[45], direction=u'clockwise', zerooffset=0);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following examples shows how negative values are handled in the polar plots.\n",
    "\n",
    "1. The first graph shows the positive values in blue and negative values as blue but superimposed on yellow. In this case zero is at the centre of the plot, and negative values has to go through zero  ($\\pi$ phase shift).\n",
    "\n",
    "2. The second graph shows exactly the same result as in the first graph. In this case zero is a circle halfway between the centre and the perimeter. Negative values move through the zero circle towards the centre of the graph.\n",
    "\n",
    "3. The third graph shows $1 + 3sin(x)$ with an asymmetric positive and negative scale.  The data set is exactly the same as in the next graph.\n",
    "\n",
    "4.  The fourth graph also shows $1 + 3sin(x)$ with an asymmetric positive and negative scale, but with different rscale limits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta=np.linspace(0,2.0*np.pi,600)\n",
    "r = np.sin(3.4*theta)\n",
    "PN = ryplot.Plotter(3, 2, 2,'Polar plots with negative values', figsize=(12,8))\n",
    "PN.polar(1,theta, r, \"sin(3.3x)\",legendAlpha=0.5,rscale=[0,1.5], rgrid=[0.5,1.5],\n",
    "         highlightNegative=True)\n",
    "PN.polar(2,theta, r, \"sin(3.3x)\", plotCol=['b'], legendAlpha=0.5, rscale=[-1.5,1.5], \n",
    "         rgrid=[0.5,1.5], highlightNegative=True)\n",
    "\n",
    "tt = np.linspace(0,2 * np.pi,360)\n",
    "rr = 1 + 3 * np.sin(tt)\n",
    "PN.polar(3,tt, rr, \"1 + 3sin(x)\", legendAlpha=0.5, rscale=[-2,4], rgrid=[1,0],\n",
    "         highlightNegative=True,  highlightCol='r',highlightWidth=4)\n",
    "\n",
    "PN.polar(4,tt,rr, \"1 + 3sin(x)\", legendAlpha=0.5, rgrid=[1,0], highlightNegative=True,\n",
    "         highlightCol='y', highlightWidth=4, rscale=[-4,4]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following example experiments with the radial grid density.  \n",
    "\n",
    "1. The first graph does not show the radial and angular grids.\n",
    "\n",
    "2. The second example states that there should be ten radial grid circles.\n",
    "\n",
    "3. The third example shows that the radial grid interval must be 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = np.arange(0, 3.01, 0.01).reshape(-1, 1)\n",
    "theta = 2*np.pi*r\n",
    "r2 = np.hstack((r,r**2))\n",
    "P = ryplot.Plotter(3, 1, 3,'Polar plots with variations', figsize=(12,4))\n",
    "P.polar(1,theta, r, \"Single Polar\", legendAlpha=0.5,\n",
    "        rscale=[0,3], zerooffset=0, rgrid=None, thetagrid=None, drawGrid=None)\n",
    "P.polar(2,theta, r, \"Single Polar\", label=['Single'], legendAlpha=0.5,\n",
    "        rscale=[0,3], zerooffset=0, rgrid=[0, 5])\n",
    "P.polar(3,theta, r, \"Single Polar\", label=['Single'], legendAlpha=0.5,\n",
    "        rscale=[0,3], zerooffset=0, rgrid=[1, 3]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Three-dimensional plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Three-dimensional line plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [ryplot.Plotter.plot3d](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html#pyradi.ryplot.Plotter.plot3d) function takes three vectors in $(x,y,z)$ and plots them on a three-dimensional grid.\n",
    "\n",
    "Given an existing figure, this function plots in a specified subplot position.\n",
    "Note that multiple 3D data sets can be plotted simultaneously by adding additional columns to\n",
    "the input coordinates of the $(x,y,z)$ arrays, each set of columns representing a different line in the plot.\n",
    "This is convenient if large arrays of data must be plotted. If more than one column is present,\n",
    "the label argument can contain the legend labels for each of the columns/lines.\n",
    "The function signature is\n",
    "\n",
    "`plot3d(plotnum, x, y, z, ptitle=None, xlabel=None, ylabel=None, zlabel=None, \n",
    "        plotCol=[], label=None, legendAlpha=0.0, titlefsize=12,\n",
    "        xylabelfsize = 12, xInvert=False, yInvert=False, zInvert=False,scatter=False,\n",
    "        markers=None, markevery=None, azim=45, elev=30, zorders=None, clip_on=True,\n",
    "        edgeCol=None)`\n",
    "               \n",
    "- `plotnum (int)`  subplot number, 1-based index.\n",
    "- `x (np.array[N,] or [N,M])`  x coordinates of each line.\n",
    "- `y (np.array[N,] or [N,M])`  y coordinates of each line.\n",
    "- `z (np.array[N,] or [N,M])`  z coordinates of each line.\n",
    "- `ptitle (string)`  plot title (optional).\n",
    "- `xlabel (string)`  x-axis label (optional).\n",
    "- `ylabel (string)`  y-axis label (optional).\n",
    "- `zlabel (string)`  z axis label (optional).\n",
    "- `plotCol ([strings])`   plot line colour and style, list with M entries (optional), use class default if not supplied. Each new plot line is rendered with the next colour in the list, repeating the sequence if necessary. Entries in this list must be of the form `['b',  'g',  'r']` defining the colour (optional).\n",
    "- `linewidths ([float])` plot line width in points, list with M entries, use default if None  (optional).\n",
    "- `pltaxis ([xmin, xmax, ymin, ymax, zmin, zmax])`  scale for x,y,z axes.  Let Matplotlib decide if None. (optional)\n",
    "- `label  ([strings])`  legend label for ordinate, list with M entries (optional).\n",
    "- `legendAlpha (float)`  transparency for legend box. This only works for bitmap files, not for eps files (optional).\n",
    "- `titlefsize (int)`  title font size, default 12pt (optional).\n",
    "- `xylabelfsize (int)`  x, y, z label font size, default 12pt (optional).\n",
    "- `xInvert (bool)`  invert the x-axis. Flip the x-axis left-right. (optional).\n",
    "- `yInvert (bool)`  invert the y-axi. Flip the y-axis left-right. (optional).\n",
    "- `zInvert (bool)`  invert the z-axis. Flip the z-axis up-down. (optional).\n",
    "- `scatter (bool)` draw only the points, no lines (optional)\n",
    "- `markers ([string])` markers to be used for plotting data points (optional)\n",
    "- `markevery (int | (startind, stride))` subsample when using markers (optional)\n",
    "- `azim (float)` graph view azimuth angle  [degrees] (optional)\n",
    "- `elev (float)` graph view evelation angle  [degrees] (optional)\n",
    "- `zorders ([int])` list of zorder for drawing sequence, highest is last (optional).\n",
    "- `clip_on (bool)` clips objects to drawing axes (optional).\n",
    "- `edgeCol ([int])` list of colour specs, value at [0] used for edge colour (optional).            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first example draws three lines consecutively in the $(x,y,z)$ grid, inverting the $z$ axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parametricCurve(z, param1 = 2, param2 = 1):\n",
    "    r = z**param1 + param2\n",
    "    theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)\n",
    "    return (r * np.sin(theta), r * np.cos(theta))\n",
    "\n",
    "z = np.linspace(-2, 2, 100)\n",
    "x, y = parametricCurve(z)\n",
    "\n",
    "P3D = ryplot.Plotter(6, 1, 1,'Plot 3D Single', figsize=(12,8))\n",
    "plabel = ['parametric curve 1', 'parametric curve 2', 'parametric curve 3']\n",
    "P3D.plot3d(1, x.T, y.T, z.T, 'Parametric Curve', 'X', 'Y', 'Z', legendAlpha=0.5, zInvert=True)\n",
    "P3D.plot3d(1, 1.3*x.T, 0.8*y.T, 0.7*z.T, 'Parametric Curve', 'X', 'Y', 'Z', legendAlpha=0.5, zInvert=True)\n",
    "P3D.plot3d(1, 0.8*x.T, 0.9*y.T, 1.2*z.T, 'Parametric Curve', 'X', 'Y', 'Z', label=plabel, legendAlpha=0.5, zInvert=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second example creates an array of three lines and plots the array in one command. The line widths for the lines differ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = ['Param1={} Param2={}'.format(2,1)]\n",
    "for i in range(2):\n",
    "    param1 = 2-i\n",
    "    param2 = i\n",
    "    label.append('Param1={} Param2={}'.format(param1, param2))\n",
    "    x1, y1 = parametricCurve(z, param1, param2)\n",
    "    x = np.vstack((x,x1))\n",
    "    y = np.vstack((y,y1))\n",
    "\n",
    "z = np.vstack((z,z,z))\n",
    "\n",
    "P3D = ryplot.Plotter(8, 1, 1,'Plot 3D Multiple', figsize=(12,8))\n",
    "P3D.plot3d(1, x.T, y.T, z.T, 'Parametric Curve', 'X', 'Y', 'Z', label=label, legendAlpha=0.5, \n",
    "           linewidths=[1,2,4],markers=['o','v','^','<'],markevery=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example plots a single line, but also shows projections on each of the axes. In this example separate subplots are used for the individual graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = np.linspace(-2, 2, 100)\n",
    "x, y = parametricCurve(z)\n",
    "\n",
    "P3D = ryplot.Plotter(7, 2, 2,'Plot 3D Projections', figsize=(12,8))\n",
    "P3D.plot(1, x.T, y.T, 'Projection along the Z direction', 'X', 'Y')\n",
    "P3D.plot(2, x.T, z.T, 'Projection along the Y direction', 'X', 'Z')\n",
    "P3D.plot(3, y.T, z.T, 'Projection along the X direction', 'Y', 'Z')\n",
    "P3D.plot3d(4, x.T, y.T, z.T, '3D View', 'X', 'Y', 'Z');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following example plots the projections along the $(x,y,z)$ axes on the same plot as the three-dimensional line. The line style and line width of each of the lines are adjusted to assist in readability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = np.linspace(-2, 2, 100)\n",
    "x, y = parametricCurve(z)\n",
    "\n",
    "P3D = ryplot.Plotter(7, 1, 1,'Plot 3D Projections', figsize=(12,8))\n",
    "P3D.plot3d(1, -5*np.ones(x.T.shape), y.T, z.T, plotCol=['r'])\n",
    "P3D.plot3d(1, x.T, -5*np.ones(y.T.shape), z.T, plotCol=['b'])\n",
    "P3D.plot3d(1, x.T, y.T, -2*np.ones(z.T.shape), plotCol=['g'])\n",
    "P3D.plot3d(1, x.T, y.T, z.T, '3D View', 'X', 'Y', 'Z', plotCol=['k'], linewidths=[2], pltaxis=[-5, 5, -5, 5, -2, 2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Three-dimensional $(x,y,z)$ mesh plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [ryplot.Plotter.mesh3D](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html?highlight=mesh3d#pyradi.ryplot.Plotter.mesh3D) function provides a three-dimensional colour mesh plot for three-dimensional $(x,y,z)$ array input sets.\n",
    "The mesh grid is defined in $(x,y)$, while the height of the mesh is the $z$ value.\n",
    "\n",
    "Given an existing figure, this function plots in a specified subplot position.\n",
    "Only one mesh is drawn at a time.  Future meshes in the same subplot\n",
    "will cover any previous meshes.\n",
    "\n",
    "The data set must have three two dimensional arrays, each for x, y, and z.  The data in x, y, and z arrays must have matching data points.  The x and y arrays each define the grid in terms of x and y values, i.e., the x array contains the x values for the data set, while the y array contains the y values.  The z array contains the z values for the corresponding x and y values in the mesh.\n",
    "\n",
    "Use wireframe=True to obtain a wireframe plot.\n",
    "\n",
    "Use surface=True to obtain a surface plot with fill colours.\n",
    "\n",
    "Z-values can be plotted on a log scale, in which case the colourbar is adjusted\n",
    "to show true values, but on the nonlinear scale.\n",
    "\n",
    "The xvals and yvals vectors may have non-constant grid-intervals, i.e., they do not\n",
    "have to be on regular intervals, but z array must correspond to the (x,y) grid.\n",
    "\n",
    "The function signature is\n",
    "\n",
    "`mesh3D(plotnum, xvals, yvals, zvals, ptitle=None, xlabel=None, ylabel=None, zlabel=None, \n",
    "        rstride=1, cstride=1, linewidth=0, plotCol=[], pltaxis=None, maxNX=0, maxNY=0, maxNZ=0,\n",
    "        xScientific=False, yScientific=False, zScientific=False, powerLimits = [-4,  2,  -4,  2, -2, 2], \n",
    "        titlefsize = 12, xylabelfsize = 12, xytickfsize = 10, wireframe=False, surface=True, \n",
    "        cmap = cm.rainbow, \n",
    "        cbarshow=False, cbarorientation = 'vertical', cbarcustomticks=[], cbarfontsize = 12,\n",
    "        drawGrid = True, xInvert=False, yInvert=False, zInvert=False, logScale=False, \n",
    "        alpha=1, alphawire=1, azim=45, elev=30, zorders=None, clip_on=True )`\n",
    "                   \n",
    "                   \n",
    "- `plotnum (int)`  subplot number, 1-based index.\n",
    "- `xvals (np.array[N,M])`  array of x values, corresponding to (x,y) grid.\n",
    "- `yvals (np.array[N,M])`  array of y values, corresponding to (x,y) grid.\n",
    "- `zvals (np.array[N,M])`  array of z values corresponding to (x,y) grid.\n",
    "- `ptitle (string)`  plot title (optional).\n",
    "- `xlabel (string)`  x axis label (optional).\n",
    "- `ylabel (string)`  y axis label (optional).\n",
    "- `zlabel (string)`  z axis label (optional).\n",
    "- `rstride (int)`  mesh line row (y axis) stride, every rstride value along y axis (optional).\n",
    "- `cstride (int)`  mesh line column (x axis)  stride, every cstride value along x axis (optional).\n",
    "- `linewidth (float)`  mesh line width in points (optional).\n",
    "- `plotCol ([strings])`  fill colour, list with M=1 entries, linetype is not used. use default if None (optional).\n",
    "- `edgeCol ([strings])`  mesh line/edge colour , list with M=1 entries, use default if None (optional).\n",
    "- `pltaxis ([xmin, xmax, ymin, ymax, zmin, zmax])`  scale for x,y axes. z scale is not settable.  Let Matplotlib decide if None (optional).\n",
    "- `maxNX (int)`  draw maxNX+1 tick labels on x axis (optional).\n",
    "- `maxNY (int)`  draw maxNY+1 tick labels on y axis (optional).\n",
    "- `maxNZ (int)`  draw maxNY+1 tick labels on z axis (optional).\n",
    "- `xScientific (bool)`  use scientific notation on x axis, use this to control the density of the x-axis grid and tick label density (optional).\n",
    "- `yScientific (bool)`  use scientific notation on y axis, use this to control the density of the x-axis grid and tick label density (optional).\n",
    "- `zScientific (bool)`  use scientific notation on z-axis, use this to control the density of the x-axis grid and tick label density (optional).\n",
    "- `powerLimits[float]`   scientific tick label power limits [x-low, x-high, y-low, y-high]  (optional).  Scientific notation is used for data less than 10$^{\\rm low}$ or data greater than 10$^{\\rm high}$. Inside this range, the notation is fixed format.  For example `powerlimits=[-3, 4, -3, 4]` defines scientific notation is used for numbers less than 1e-3 or greater than 1e4. For this to work the x or y axis must be set to scientific notation (`xScientific` and/or `yScientific` must be `True`) (optional).\n",
    "- `titlefsize (int)`  title font size, default 12pt (optional).\n",
    "- `xylabelfsize (int)`  x-axis, y-axis, z-axis label font size, default 12pt (optional).\n",
    "- `xytickfsize (int)`  x-axis, y-axis, z-axis tick font size, default 10pt (optional).\n",
    "- `wireframe (bool)` If True, do a wireframe plot,  (optional)\n",
    "- `surface (bool)` If True, do a surface plot,  (optional)\n",
    "- `cmap (cm)`  color map for the mesh (optional) (optional).\n",
    "- `cbarshow (bool)`  if true, the show a colour bar (optional).\n",
    "- `cbarorientation (string)`  'vertical' (right) or 'horizontal' (below) (optional).\n",
    "- `cbarcustomticks zip([z values/float],[tick labels/string])`  define custom colourbar ticks locations for given z values(optional).\n",
    "- `cbarfontsize (int)`  font size for colour bar (optional).\n",
    "- `drawGrid (bool)`  draw the grid on the plot (optional).\n",
    "- `xInvert (bool)`  invert the x-axis. Flip the x-axis left-right. (optional).\n",
    "- `yInvert (bool)`  invert the y-axi. Flip the y-axis left-right. (optional).\n",
    "- `zInvert (bool)`  invert the z-axis. Flip the z-axis up-down. (optional).\n",
    "- `logScale (bool)`  do Z values on log scale, recompute colourbar values (optional).\n",
    "- `alpha (float)` surface transparency (optional)\n",
    "- `alphawire (float)` mesh transparency (optional)\n",
    "- `azim (float)` graph view azimuth angle  [degrees] (optional)\n",
    "- `elev (float)` graph view evelation angle  [degrees] (optional)\n",
    "- `distance (float)` distance between viewer and plot (optional)\n",
    "- `zorders` ([int]) list of zorder for drawing sequence, highest is last (optional).\n",
    "- `clip_on` (bool) clips objects to drawing axes (optional).\n",
    "\n",
    "A two-dimensional plot has only only one sensible view, perpendicular on the plane of the graph.  A three-dimensional plot has many possible views. The view in the plot can be set by using the `elev` and `azim` values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \n",
    "[`ryplot.Plotter.meshContour`](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html#pyradi.ryplot.Plotter.meshContour) function expects a mesh input. The mesh grid is defined in two arrays, the X array and the Y array, created with `numpy.meshgrid`.  The `meshgrid` function returns two two-dimensional arrays, one\n",
    "*varying along the x direction, with fixed y values* \n",
    "and the other \n",
    "*varying along the y direction, with fixed x values*. Study the following code to see this in action:\n",
    "\n",
    "    x = numpy.array([1, 2, 3])\n",
    "    y = numpy.array([10, 20, 30]) \n",
    "    XX, YY = numpy.meshgrid(x, y)\n",
    "    ZZ = XX + YY\n",
    "\n",
    "    ZZ => array([[11, 12, 13],\n",
    "                 [21, 22, 23],\n",
    "                 [31, 32, 33]])\n",
    "\n",
    "The two arrays, taken together, provides a collection of all possible combinations of x and y values, but in a structured manner: x varying along one axis and y varying along the other axis.  The z array provides the z values for the corresponding x and y values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def myFunc(x,y):\n",
    "  scale = np.sqrt(np.exp(-(x**2 +y**2)))\n",
    "  return np.sin(2 * x) * np.cos(4 * y) * scale\n",
    "\n",
    "x = np.linspace(-2, 2, 101)\n",
    "y = np.linspace(-2, 2, 101)\n",
    "varx, vary = np.meshgrid(x, y)\n",
    "zdata = myFunc(varx.flatten(), vary.flatten()).reshape(varx.shape)\n",
    "\n",
    "p = ryplot.Plotter(1,2,2,figsize=(14,10))\n",
    "p.mesh3D(1, varx, vary, zdata, ptitle='Title', xlabel='x', ylabel='y', zlabel='z',\n",
    "  rstride=1, cstride=6, linewidth= 1, maxNX=5, maxNY=5, maxNZ=0, wireframe=True,\n",
    "  edgeCol=['r'], drawGrid=True, cbarshow=True, cmap=None, alpha=0.2)\n",
    "\n",
    "p.mesh3D(2, varx, vary, zdata, ptitle='Title', xlabel='x', ylabel='y', zlabel='z',\n",
    "  plotCol=['r'], edgeCol=['k'], rstride=3, cstride=3, linewidth= 0.1, maxNX=5, maxNY=5, maxNZ=0,\n",
    "  drawGrid=True, cbarshow=True, alpha=0.5)\n",
    "\n",
    "p.mesh3D(3, varx, vary, zdata, ptitle='Title', xlabel='x', ylabel='y', zlabel='z',\n",
    "  rstride=3, cstride=3, linewidth= 0.2, maxNX=5, maxNY=5, maxNZ=0,\n",
    "  drawGrid=True, cmap=cm.jet,  cbarshow=True, elev=70, azim=15,\n",
    "  pltaxis=[-3, 3, -3, 3, -1, 1])\n",
    "\n",
    "barticks = zip([-0.5, 0, 0.5], ['low', 'med', 'high'])\n",
    "p.mesh3D(4, varx, vary, zdata, ptitle='Title', xlabel='x', ylabel='y', zlabel='z',\n",
    "  rstride=3, cstride=3, linewidth= 0, maxNX=5, maxNY=5, maxNZ=0, drawGrid=True,\n",
    "  cmap=cm.brg, cbarshow=True, cbarcustomticks=barticks, zInvert=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contour and filled contour cartesian plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bivariate_normal(X, Y, sigmax=1.0, sigmay=1.0,\n",
    "                 mux=0.0, muy=0.0, sigmaxy=0.0):\n",
    "    \"\"\"\n",
    "    Bivariate Gaussian distribution for equal shape *X*, *Y*.\n",
    "    See `bivariate normal\n",
    "    <http://mathworld.wolfram.com/BivariateNormalDistribution.html>`_\n",
    "    at mathworld.\n",
    "    \"\"\"\n",
    "    Xmu = X-mux\n",
    "    Ymu = Y-muy\n",
    "\n",
    "    rho = sigmaxy/(sigmax*sigmay)\n",
    "    z = Xmu**2/sigmax**2 + Ymu**2/sigmay**2 - 2*rho*Xmu*Ymu/(sigmax*sigmay)\n",
    "    denom = 2*np.pi*sigmax*sigmay*np.sqrt(1-rho**2)\n",
    "    return np.exp(-z/(2*(1-rho**2))) / denom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \n",
    "[`ryplot.Plotter.meshContour`](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html#pyradi.ryplot.Plotter.meshContour) function provide a  countour plot capability, variations of countour lines and colour filling.  The data values must be given on a fixed mesh grid of three-dimensional $(x,y,z)$ array input sets.\n",
    "The mesh grid is defined in $(x,y)$, while the height of the mesh is the $z$ value.\n",
    "\n",
    "Given an existing figure, this function plots in a specified subplot position.\n",
    "Only one contour plot is drawn at a time.  Future contours in the same subplot\n",
    "will cover any previous contours.\n",
    "\n",
    "The data set must have three two dimensional arrays, each for x, y, and z.  The data in x, y, and z arrays must have matching data points.  The x and y arrays each define the grid in terms of x and y values, i.e., the x array contains the x values for the data set, while the y array contains the y values.  The z array contains the z values for the corresponding x and y values in the contour mesh.\n",
    "\n",
    "Z-values can be plotted on a log scale, in which case the colourbar is adjusted\n",
    "to show true values, but on the nonlinear scale.\n",
    "\n",
    "The current version only saves png files, since there appears to be a problem saving eps files.\n",
    "\n",
    "The function signature is\n",
    "`meshContour(plotnum, xvals, yvals, zvals, numLevels=10,\n",
    "             ptitle=None, xlabel=None, ylabel=None, shading='flat',\n",
    "             plotCol=[], pltaxis=None, maxNX=0, maxNY=0,\n",
    "             xScientific=False, yScientific=False, powerLimits=[-4,  2,  -4,  2], \n",
    "             titlefsize=12, xylabelfsize=12, xytickfsize=10,\n",
    "             meshCmap=cm.rainbow, cbarshow=False, cbarorientation='vertical', \n",
    "             cbarcustomticks=[], cbarfontsize=12, drawGrid=False, \n",
    "             yInvert=False, xInvert=False, contourFill=True, contourLine=True, logScale=False,\n",
    "             negativeSolid=False, zeroContourLine=None,\n",
    "             contLabel=False, contFmt='%.2f', contCol='k', contFonSz=8, contLinWid=0.5, \n",
    "             zorders=None, clip_on=True)`\n",
    "\n",
    "\n",
    "- `plotnum (int)` The subplot number, 1-based index, according to Matplotlib conventions.  This value must always be given, even if only a single 1,1 subplot is used. \n",
    "- `xvals (np.array[N,M])`  array of x values, corresponding to (x,y) grid.\n",
    "- `yvals (np.array[N,M])`  array of y values, corresponding to (x,y) grid.\n",
    "- `zvals (np.array[N,M])`  array of z values corresponding to (x,y) grid.\n",
    "- `levels (int or [float])` number of contour levels or a list of levels (optional). If an integer is given, it will cause as many contour lines. If a list of floats is given, the values in the list will be the contour lines.\n",
    "- `ptitle (string)` plot title (optional)\n",
    "- `xlabel (string)` x axis label (optional)\n",
    "- `ylabel (string)` y axis label (optional)\n",
    "- `shading (string)`  not used currently (optional)\n",
    "- `plotCol ([strings])`  fill colour, list with M=1 entries, linetype is not used. use default if None (optional).\n",
    "- `pltaxis ([xmin, xmax, ymin,ymax])` scale for x,y axes. Let Matplotlib decide if None. (optional)\n",
    "- `maxNX (int)` draw maxNX+1 tick labels on x axis (optional)\n",
    "- `maxNY (int)` draw maxNY+1 tick labels on y axis (optional)\n",
    "- `xScientific (bool)` use scientific notation on x axis (optional)\n",
    "- `yScientific (bool)` use scientific notation on y axis (optional)\n",
    "- `powerLimits[float]`   scientific tick label power limits [x-low, x-high, y-low, y-high]  (optional).  Scientific notation is used for data less than 10$^{\\rm low}$ or data greater than 10$^{\\rm high}$. Inside this range, the notation is fixed format.  For example `powerlimits=[-3, 4, -3, 4]` defines scientific notation is used for numbers less than 1e-3 or greater than 1e4. For this to work the x or y axis must be set to scientific notation (`xScientific` and/or `yScientific` must be `True`) (optional).\n",
    "- `titlefsize (int)` title font size, default 12pt (optional)\n",
    "- `xylabelfsize (int)` x-axis, y-axis label font size, default 12pt (optional)\n",
    "- `xytickfsize (int)` x-axis, y-axis tick font size, default 10pt (optional)\n",
    "- `meshCmap (cm)` colour map for the mesh fill (optional)\n",
    "- `cbarshow (bool)` if true, the show a colour bar (optional)\n",
    "- `cbarorientation (string)` 'vertical' (right) or 'horizontal' (below) (optional)\n",
    "- `cbarcustomticks zip([z values/float],[tick labels/string])`  define custom colourbar ticks locations for given z values(optional).\n",
    "- `cbarfontsize (int)` font size for colour bar (optional)\n",
    "- `drawGrid (bool)` draw the grid on the plot (optional)\n",
    "- `xInvert (bool)`  invert the x-axis. Flip the x-axis left-right. (optional).\n",
    "- `yInvert (bool)`  invert the y-axi. Flip the y-axis up-down. (optional).\n",
    "- `contourFill (bool)` fill contours with colour (optional).\n",
    "- `contourLine (bool)` draw a series of contour lines (optional).\n",
    "- `logScale (bool)` do Z values on log scale, recompute colourbar values (optional).\n",
    "- `negativeSolid (bool)` draw negative contours in solid lines, dashed otherwise (optional).\n",
    "- `zeroContourLine (double)` draw a single contour at given value (optional).\n",
    "- `contLabel (bool)` label the contours with values (optional).\n",
    "- `contFmt (string)` contour label c-printf format (optional).\n",
    "- `contCol (string)` contour label colour, e.g., 'k' (optional).\n",
    "- `contFonSz (float)` contour label fontsize (optional).\n",
    "- `contLinWid (float)` contour line width in points (optional).\n",
    "- `zorders` ([int]) list of zorder for drawing sequence, highest is last (optional).\n",
    "- `clip_on` (bool) clips objects to drawing axes (optional).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pyradi.ryplot as ryplot\n",
    "from matplotlib import cm\n",
    "import matplotlib.mlab as mlab\n",
    "\n",
    "\n",
    "def bivariate_normal(X, Y, sigmax=1.0, sigmay=1.0,\n",
    "                 mux=0.0, muy=0.0, sigmaxy=0.0):\n",
    "    \"\"\"\n",
    "    Bivariate Gaussian distribution for equal shape *X*, *Y*.\n",
    "    See `bivariate normal\n",
    "    <http://mathworld.wolfram.com/BivariateNormalDistribution.html>`_\n",
    "    at mathworld.\n",
    "    \"\"\"\n",
    "    Xmu = X-mux\n",
    "    Ymu = Y-muy\n",
    "\n",
    "    rho = sigmaxy/(sigmax*sigmay)\n",
    "    z = Xmu**2/sigmax**2 + Ymu**2/sigmay**2 - 2*rho*Xmu*Ymu/(sigmax*sigmay)\n",
    "    denom = 2*np.pi*sigmax*sigmay*np.sqrt(1-rho**2)\n",
    "    return np.exp(-z/(2*(1-rho**2))) / denom\n",
    "\n",
    "\n",
    "#create the input data\n",
    "delta = 0.025\n",
    "x = np.arange(-3.0, 3.0, delta)\n",
    "y = np.arange(-2.0, 2.0, delta)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)\n",
    "Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)\n",
    "# difference of Gaussians\n",
    "Z = 10.0 * (Z2 - Z1)\n",
    "\n",
    "#do the plot\n",
    "pmc = ryplot.Plotter(1,1,3, figsize=(18,6))\n",
    "\n",
    "pmc.meshContour(1, X, Y, Z, levels=15, ptitle='meshContour', xlabel='X-value', ylabel='Y-value',\n",
    "                plotCol=['k'], titlefsize=12, meshCmap=cm.rainbow, cbarshow=True,\n",
    "                cbarorientation='vertical', cbarfontsize=12, drawGrid=False, yInvert=True, \n",
    "                negativeSolid=True, contourFill=True, contourLine=True, logScale=False,\n",
    "                contLabel=True, contFmt='%.1f', contCol='k', contFonSz=8);\n",
    "\n",
    "pmc.meshContour(2, X, Y, Z, levels=[-1.5, -1, -0.5, 0, 0.5, 1.0, 1.5], ptitle='meshContour', xlabel='X-value', ylabel='Y-value',\n",
    "                plotCol=['b'], titlefsize=12, meshCmap=cm.rainbow, cbarshow=False,\n",
    "                cbarorientation='vertical', cbarfontsize=12, drawGrid=True, yInvert=True, \n",
    "                negativeSolid=False, contourFill=False, contourLine=True, logScale=False,\n",
    "                contLabel=True, contFmt='%.2f', contCol='b', contFonSz=8, contLinWid=4 );\n",
    "\n",
    "# pmc.meshContour(3, X, Y, Z, levels=30, ptitle='meshContour', xlabel='X-value', ylabel='Y-value',\n",
    "levels = np.linspace(-1.5,1.74,60)\n",
    "lablevels = levels\n",
    "pmc.meshContour(3, X, Y, Z, levels=levels, ptitle='meshContour', xlabel='X-value', ylabel='Y-value',\n",
    "                plotCol=['k'], titlefsize=12, meshCmap=ryplot.cubehelixcmap(),zeroContourLine=True,\n",
    "                cbarshow=True, cbarorientation='vertical', cbarfontsize=12, drawGrid=False, \n",
    "                negativeSolid=False, contourFill=True,contLabel=True,  contourLine=True, logScale=False,contLinWid=0.1);\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [ryplot.Plotter.polarMesh](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html?highlight=polarmesh#pyradi.ryplot.Plotter.polarMesh) function provides a contour and filled contour graph on a polar axis system.\n",
    "The data values must be given on a fixed mesh grid of three-dimensional $(\\theta,\\rho,z)$ array input sets ($\\theta$ is angle,  and $\\rho$ is radial distance).\n",
    "The mesh grid is defined in $(\\theta,\\rho)$, while the height of the mesh is the $z$ value.\n",
    "The $(\\theta,\\rho)$ arrays may have non-constant grid-intervals, i.e., they do not\n",
    "have to be on regular intervals.\n",
    "        \n",
    "Given an existing figure, this function plots in a specified subplot position.\n",
    "Only one contour plot is drawn at a time.  Future contours in the same subplot\n",
    "will cover any previous contours.\n",
    "\n",
    "The data set must have three two dimensional arrays, each for $\\theta$, $\\rho$, and z.  The data in $\\theta$, $\\rho$, and z arrays must have matching data points.  The $\\theta$ and $\\rho$ arrays each define the grid in terms of $\\theta$ and $\\rho$ values, i.e., the $\\theta$ array contains the angular values for the data set, while the $\\rho$ array contains the radial values.  The z array contains the z values for the corresponding $\\theta$ and $\\rho$ values in the contour mesh.\n",
    "\n",
    "Z-values can be plotted on a log scale, in which case the colourbar is adjusted\n",
    "to show true values, but on the nonlinear scale.\n",
    "\n",
    "The current version only saves png files, since there appears to be a problem saving eps files.\n",
    "\n",
    "The function signature is\n",
    "`polarMesh(plotnum, theta, radial, zvals, ptitle=None, shading='flat',\n",
    "           radscale=None, titlefsize=12,  meshCmap=cm.rainbow, cbarshow=False, \n",
    "           cbarorientation='vertical', cbarcustomticks=[], cbarfontsize=12,\n",
    "           rgrid=[0,5], thetagrid=[30], drawGrid=False,\n",
    "           thetagridfontsize=12, radialgridfontsize=12,\n",
    "           direction='counterclockwise', zerooffset=0, logScale=False,\n",
    "           plotCol=[], levels=10, contourFill=True, contourLine=True, \n",
    "           zeroContourLine=None, negativeSolid=False,\n",
    "           contLabel=False, contFmt='%.2f', contCol='k', contFonSz=8, contLinWid=0.5, \n",
    "           zorders=None, clip_on=True)`\n",
    "\n",
    "- `plotnum (int)` subplot number, 1-based index\n",
    "- `theta (np.array[N,M])` array of angular values [0..2pi] corresponding to (theta,rho) grid.\n",
    "- `radial (np.array[N,M])` array of radial values  corresponding to (theta,rho) grid.\n",
    "- `zvals (np.array[N,M])` array of z values  corresponding to (theta,rho) grid.\n",
    "- `ptitle (string)` plot title (optional).\n",
    "- `shading (string)` 'flat' | 'gouraud'  (optional).\n",
    "- `rscale ([rmin, rmax])`: radial plotting limits. use default setting if None. If rmin is negative the zero is a circle and rmin is at the centre of the graph (optional).\n",
    "- `rgrid ([rinc, numinc])`: radial grid. If rgrid is `None` don't show labels. If rgrid is None use pyplot default. If rinc=0 then numinc is number of intervals. If rinc is not zero then rinc is the increment and numinc is ignored (optional).\n",
    "- `thetagrids (float)`: theta grid interval [degrees], If thetagrids is `None` don't show labels. (optional).\n",
    "- `titlefsize (int)` title font size, default 12pt (optional).\n",
    "- `meshCmap (cm)` color map for the mesh (optional).\n",
    "- `cbarshow (bool)` if true, the show a color bar (optional).\n",
    "- `cbarorientation (string)` 'vertical' (right) or 'horizontal' (below) (optional).\n",
    "- `cbarcustomticks zip([z values/float],[tick labels/string])`  define custom colourbar ticks locations for given z values(optional).\n",
    "- `cbarfontsize (int)` font size for color bar (optional).\n",
    "- `drawGrid (bool)` draw the grid on the plot (optional).\n",
    "- `thetagridfontsize (float)` font size for the angular grid (optional).\n",
    "- `radialgridfontsize (float)` font size for the radial grid (optional).\n",
    "- `direction (string)` direction in increasing angle, 'counterclockwise' (default) or 'clockwise' (optional).\n",
    "- `zerooffset (float)` rotation offset where scale zero should be [rad]. Positive zero-offset rotation is counterclockwise from 3'o'clock  (optional).\n",
    "- `logScale (bool)` do Z values on log scale, recompute colourbar values  (optional).\n",
    "- `plotCol ([strings])`  contour line colour, list with M=1 entries, linetype is not used. use default if None (optional).\n",
    "- `levels (int or [float])` number of contour levels or a list of levels (optional).\n",
    "- `contourFill (bool)` fill contours with colour (optional).\n",
    "- `contourLine (bool)` draw a series of contour lines  (optional).\n",
    "- `zeroContourLine (double)` draw a contour at the stated value (optional).\n",
    "- `negativeSolid (bool)` draw negative contours in solid lines, dashed otherwise (optional).\n",
    "- `contLabel (bool)` label the contours with values (optional).\n",
    "- `contFmt (string)` contour label c-printf format (optional).\n",
    "- `contCol (string)` contour label colour, e.g., 'k' (optional).\n",
    "- `contFonSz (float)` contour label fontsize (optional).\n",
    "- `contLinWid (float)` contour line width in points (optional).\n",
    "- `zorders` ([int]) list of zorder for drawing sequence, highest is last (optional).\n",
    "- `clip_on` (bool) clips objects to drawing axes (optional).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The angle and radial values must also be given in meshgrid format.\n",
    "The order of $\\theta$ and $\\rho$ in the meshgrid is important. \n",
    "Please follow the exact p,r order given here.\n",
    "\n",
    "Comments on the graphs below:\n",
    "\n",
    "1. Graph with no angle and radial tick marks. Negative contour lines are shown in dashed form. The `levels` setting is used to display 20 contour lines.\n",
    "\n",
    "2. The `levels` setting is used to create a number of contour levels that are note evenly spaced.  The graph is not filled, only contourlines are shown. Contour lines are labelled and given a thick line.\n",
    "\n",
    "3. The `radscale[0]` setting is negative - but there are no data points for negative radial, hence the centre of the graph is empty. No contour lines are shown. Note how the appearance of the contours change, the empty centre is a form of geometric distortion of the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = np.linspace(0,2*np.pi,50)\n",
    "r = np.linspace(0,1.25,25)\n",
    "P,R = np.meshgrid(p,r)\n",
    "value = ((R**2 - 1)**2) * np.sin(2 * P)\n",
    "p3D = ryplot.Plotter(1, 1, 3,'Polar contour plot',figsize=(18,4))\n",
    "\n",
    "ax = p3D.polarMesh(1, p, r, value, meshCmap = cm.jet_r, cbarshow=True,\n",
    "          drawGrid=False, rgrid=None, thetagrid=None, levels=20,\n",
    "          thetagridfontsize=10, radialgridfontsize=8,\n",
    "          direction='counterclockwise', zerooffset=0)\n",
    "\n",
    "ax = p3D.polarMesh(2, p, r, value, meshCmap = cm.jet_r, cbarshow=False,\n",
    "          drawGrid=False, rgrid=None, thetagrid=None, plotCol=['g'],\n",
    "          levels=[-1, -0.5,  -0.25, -0.025, 0, 0.025, 0.25, 0.5, 1],\n",
    "          thetagridfontsize=10, radialgridfontsize=8, negativeSolid=True,\n",
    "          direction='counterclockwise', zerooffset=0,contourFill=False,contourLine=True,\n",
    "          contLabel=True, contFmt='%.3f', contCol='g', contFonSz=8, contLinWid=2)\n",
    "\n",
    "ax = p3D.polarMesh(3, p, r, value, meshCmap = ryplot.cubehelixcmap(), cbarshow=True,\n",
    "          drawGrid=False, rgrid=None, thetagrid=None, \n",
    "          levels=10, contCol='k',radscale=[-0.5, 1.25],\n",
    "          thetagridfontsize=10, radialgridfontsize=8, negativeSolid=False, zeroContourLine=True,\n",
    "          direction='counterclockwise', zerooffset=0,contourFill=True,contourLine=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The polar angle axis can be offset and rotating clockwise or counterclockwise as does the polar plot above. \n",
    "The radial grid and angular grid intervals can also defined to the required intervals or number of labels.\n",
    "\n",
    "1.  The first plot shows 10 radial grid lines and a counterclockwise angular offset of 45 degrees.\n",
    "\n",
    "2.  The second plot shows the default number of radial grid lines and angular grid lines.\n",
    "\n",
    "3.  The third example shows a user-defined radial grid interval, a 15-degree angular grid interval, and an angular offset at -90 degrees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = np.linspace(0,1.2,100)\n",
    "p = np.linspace(0,2*np.pi,100)\n",
    "P, R = np.meshgrid(p, r)\n",
    "value =  ((R**2 - 1)**2) * np.sin(2 * P)\n",
    "pmesh = ryplot.Plotter(1, 1, 3,'Polar plot in 3-D variations',figsize=(18,4))\n",
    "pmesh.polarMesh(1, p, r, value, meshCmap = cm.gray, cbarshow=True,\n",
    "              drawGrid=True, rgrid=[0,10],\n",
    "              thetagridfontsize=10, radialgridfontsize=8,\n",
    "              direction='clockwise', zerooffset=np.pi/4)\n",
    "pmesh.polarMesh(2, p, r, value, meshCmap = cm.hot, cbarshow=True,\n",
    "              drawGrid=True,thetagrid=[45], rgrid=[.25,1.25],\n",
    "              thetagridfontsize=10, radialgridfontsize=8,\n",
    "              direction='counterclockwise', zerooffset=0)\n",
    "pmesh.polarMesh(3, p, r, value, meshCmap = cm.jet, cbarshow=True,\n",
    "              drawGrid=True, thetagrid=[15], rgrid=[0.1, 0],\n",
    "              thetagridfontsize=10, radialgridfontsize=8,\n",
    "              direction='clockwise', zerooffset=-np.pi/2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Three-dimensional polar mesh plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [ryplot.Plotter.polar3d](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html?highlight=polar3d#pyradi.ryplot.Plotter.polar3d) displays polar information in a three-dimensional polar mesh format. \n",
    "\n",
    "The data values must be given on a fixed mesh grid of three-dimensional $(\\theta,\\rho,z)$ array input sets ($\\theta$ is angle,  and $\\rho$ is radial distance).\n",
    "The mesh grid is defined in $(\\theta,\\rho)$, while the height of the mesh is the $z$ value.\n",
    "The $(\\theta,\\rho)$ arrays may have non-constant grid-intervals, i.e., they do not\n",
    "have to be on regular intervals.\n",
    "        \n",
    "Given an existing figure, this function plots in a specified subplot position.\n",
    "Only one contour plot is drawn at a time.  Future contours in the same subplot\n",
    "will cover any previous contours.\n",
    "\n",
    "The data set must have three two dimensional arrays, each for $\\theta$, $\\rho$, and z.  The data in $\\theta$, $\\rho$, and z arrays must have matching data points.  The $\\theta$ and $\\rho$ arrays each define the grid in terms of $\\theta$ and $\\rho$ values, i.e., the $\\theta$ array contains the angular values for the data set, while the $\\rho$ array contains the radial values.  The z array contains the z values for the corresponding $\\theta$ and $\\rho$ values in the contour mesh.\n",
    "\n",
    "The function signature is \n",
    "\n",
    "    polar3d(plotnum, theta, radial, zvals, ptitle=None, \n",
    "                xlabel=None, ylabel=None, zlabel=None, zscale=None,  \n",
    "               titlefsize=12, xylabelfsize = 12,\n",
    "               thetaStride=1, radialstride=1, meshCmap = cm.rainbow,\n",
    "               linewidth=0.1, azim=45, elev=30)\n",
    "\n",
    "- `plotnum (int)` subplot number, 1-based index\n",
    "- `theta (np.array[N,M])` array of angular values [0..2pi] corresponding to (theta,rho) grid.\n",
    "- `radial (np.array[N,M])` array of radial values  corresponding to (theta,rho) grid.\n",
    "- `zvals (np.array[N,M])` array of z values  corresponding to (theta,rho) grid.\n",
    "- `ptitle (string)` plot title (optional)\n",
    "- `xlabel (string)` x-axis label (optional)\n",
    "- `ylabel (string)` y-axis label (optional)\n",
    "- `zlabel (string)` z-axis label (optional)\n",
    "- `zscale ([float])` z axis [min, max] in the plot.\n",
    "- `titlefsize (int)` title font size, default 12pt (optional)\n",
    "- `xylabelfsize (int)` x, y, z label font size, default 12pt (optional)\n",
    "- `thetaStride (int)` theta mesh line stride, every thetaStride value along angle values (optional).\n",
    "- `radialstride (int)` radial mesh line stride, every radialstride value along radial values (optional).\n",
    "- `meshCmap (cm)` color map for the mesh (optional)\n",
    "- `linewidth (float)` width of the mesh lines\n",
    "- `azim (float)` camera azimuth angle viewing the graph [degrees]\n",
    "- `elev (float)` camera evelation angle viewing the graph [degrees]\n",
    "- `zorder ([int])` list of zorder for drawing sequence, highest is last (optional)\n",
    "- `clip_on (bool)` clips objects to drawing axes (optional)\n",
    "- `facecolors ((np.array[N,M])` array of z value facecolours, corresponding to (theta,rho) grid.\n",
    "- `alpha (float)` facecolour surface transparency (optional)\n",
    "- `edgeCol ([int])` list of colour specs, value at [0] used for edge colour (optional).            \n",
    "               \n",
    "A two-dimensional plot has only only one sensible view, perpendicular on the plane of the graph.  A three-dimensional plot has many possible views. The view in the plot can be set by using the `elev` and `azim` values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \n",
    "[ryplot.Plotter.polar3d](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html?highlight=polar3d#pyradi.ryplot.Plotter.polar3d) function expects a mesh input. The mesh grid is defined in two arrays, the r (radial) array and the p (angle) array, created with `numpy.meshgrid`.  The `meshgrid` function returns two two-dimensional arrays, one\n",
    "*varying along the r direction, with fixed p values* \n",
    "and the other \n",
    "*varying along the p direction, with fixed r values*. \n",
    "The two arrays, taken together, provides a collection of all possible combinations of x and y values, but in a structured manner: \n",
    "r varying along one axis and p varying along the other axis. \n",
    "The z array provides the z values for the corresponding r and p values. Study the output of the following code - note how one matrix varies along rows and the other along columns: collectively, they describe all possible combinations of p and r. Carefully consider the size of the two arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = np.linspace(0,1.25,4) # radial\n",
    "p = np.linspace(0,2*np.pi,9) # angular\n",
    "#build a meshgrid (2-D array of values)\n",
    "R,P = np.meshgrid(r,p)\n",
    "print(R.shape)\n",
    "print(R)\n",
    "print(P.shape)\n",
    "print(P)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first example creates an angular grid of 50 points in $2\\pi$, and 25 radial values in the range [0:1.25].  The meshgrid creates the two two-dimensional arrays, which is used to calculate the z values and plot the data.  The same graph is viewed from two different camera view points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = np.linspace(0,1.25,25) # radial\n",
    "p = np.linspace(0,2*np.pi,50) # angular\n",
    "#build a meshgrid (2-D array of values)\n",
    "R,P = np.meshgrid(r,p)\n",
    "#calculate the z values on the cartesian grid\n",
    "value = ((R**2 - 1)**2)\n",
    "p3D = ryplot.Plotter(1, 1, 2, 'Polar plot in 3-D', figsize=(18,6))\n",
    "p3D.polar3d(1, p, r, value, ptitle='3-D Polar Plot',\n",
    "    xlabel='xlabel', ylabel='ylabel', zlabel='zlabel')\n",
    "p3D.polar3d(2, p, r, value, ptitle='3-D Polar Plot',\n",
    "    xlabel='xlabel', ylabel='ylabel', zlabel='zlabel',azim=135,elev=-20);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following example reads data from a file and displays the results in different views. The file is read from the internet, unzipped and the results are displayed.  The second graph also illustrates how to create a hole in the centre, which is sometimes required to show a graphic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyradi.ryfiles as ryfiles\n",
    "import pyradi.ryutils as ryutils\n",
    "\n",
    "tgzFilename = 'Intensity-max.tgz'\n",
    "destinationDir = '.'\n",
    "tarFilename = 'Intensity-max.tar'\n",
    "url = 'https://raw.githubusercontent.com/NelisW/pyradi/master/pyradi/data/'\n",
    "dlNames = ryfiles.downloadUntar(tgzFilename, url, destinationDir, tarFilename)\n",
    "\n",
    "print('filesAvailable are {}'.format(dlNames))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if dlNames:\n",
    "    with open('./Intensity-max.dat', 'rt') as fin:\n",
    "        aArray = np.loadtxt( fin, skiprows=1 , dtype=float )\n",
    "        azim = aArray[1:,0] + np.pi   # to positive angles\n",
    "        elev = aArray[0,1:] + np.pi/2 # get out of negative data on polar\n",
    "        intensity = aArray[1:,1:]\n",
    "\n",
    "        p3D = ryplot.Plotter(1, 1, 2,'Polar plot in 3-D',figsize=(18,6))\n",
    "        p3D.polar3d(1, azim, elev, intensity, zlabel='zlabel',zscale=[0, 600], azim=45, elev=30)\n",
    "        p3D.polar3d(2, azim, elev + np.pi/2, intensity, zlabel='zlabel',zscale=[0, 2000], azim=60, elev=60);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting on a sphere"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This examples plots random points on a sphere.  It covers the forming of a sphere using `Mesh3D` as well as calculating the position of the points.\n",
    "\n",
    "1. Biased: Using independently generated random elevation and azimuth angles provides a biased spherical distribution: as the longitudinal lines bundles up approach the poles, the density of samples increases.  This distribution is determined by independently calculating  $\\theta = 2\\pi u$  (azimuth) and $\\phi = (\\pi/2)(2u-1)$ (elevation) where $u$ is a uniform random number generator $u\\in[0,1)$. Ideally, $\\phi$ should be calculated over $u\\in[0,1]$, but in practice this is of little practical significance.\n",
    "\n",
    "2. Uniform: Independently generated  random elevation and azimuth angles can yield a uniform sample density if the longitudinal (elevation) variable is mapped to counter the increased density towards the poles, see [here](https://en.wikibooks.org/wiki/Mathematica/Uniform_Spherical_Distribution) and [here](http://mathworld.wolfram.com/SpherePointPicking.html). This is done by calculating $\\theta = 2\\pi u$  (azimuth) and $\\phi=\\cos^{-1}(2u-1)-\\pi/2$ (elevation) where $u$ is a uniform random number generator $u\\in[0,1)$. Ideally, $\\phi$ should be calculated over $u\\in[0,1]$, but in practice this is of little practical significance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create the wireframe for the sphere\n",
    "u = np.linspace(0, np.pi, 100)\n",
    "v = np.linspace(0, 2 * np.pi, 100)\n",
    "x = np.outer(np.sin(u), np.sin(v))\n",
    "y = np.outer(np.sin(u), np.cos(v))\n",
    "z = np.outer(np.cos(u), np.ones_like(v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next create two sets of random points on the sphere, using the two algorithms described above, and plot. Note on the left plot, how the density on the poles is higher (polar bias) than on the right plot (uniform law, bias corrected)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create the random point samples on the sphere\n",
    "samples = 500\n",
    "np.random.RandomState(200)\n",
    "theta = 2 * np.pi * np.random.uniform(0, 1, size=samples)\n",
    "#biased sampling with higher density towards the poles\n",
    "phib = np.pi * (2 * np.random.uniform(0, 1, size=samples) -1 ) / 2 \n",
    "#uniform sampling corrected for polar bias\n",
    "phiu = np.arccos(2 * np.random.uniform(0, 1, size=samples) -1 ) - np.pi/2 \n",
    "\n",
    "#create normal vectors using the pairs of random angles in a transformation \n",
    "xsb = np.cos(phib) * np.cos(theta)\n",
    "ysb = np.cos(phib) * np.sin(theta)\n",
    "zsb = np.sin(phib)\n",
    "xsu = np.cos(phiu) * np.cos(theta)\n",
    "ysu = np.cos(phiu) * np.sin(theta)\n",
    "zsu = np.sin(phiu)\n",
    "\n",
    "azim = 45 # view angle\n",
    "elev = 45 # view angle\n",
    "sph = ryplot.Plotter(1,1,2, figsize=(20,10))\n",
    "sph.mesh3D(1,x,y,z, ptitle='Biased', alpha=0.1, wireframe=False, surface=True,linewidth=0, drawGrid=False)\n",
    "sph.mesh3D(1,x,y,z, alphawire=0.4, wireframe=True, surface=False, \n",
    "    edgeCol=['b'],plotCol=['b'],linewidth=0.4,rstride=2,cstride=2, drawGrid=False)\n",
    "sph.plot3d(1, xsb, ysb, zsb,  scatter=True,markers=['o' for i in range(len(xsb))],\n",
    "           azim=azim, elev=elev)\n",
    "\n",
    "sph.mesh3D(2,x,y,z, ptitle='Uniform',alpha=0.1, wireframe=False, surface=True,linewidth=0, drawGrid=False)\n",
    "sph.mesh3D(2,x,y,z, alphawire=0.4, wireframe=True, surface=False, \n",
    "    edgeCol=['b'],plotCol=['b'],linewidth=0.4,rstride=2,cstride=2, drawGrid=False)\n",
    "sph.plot3d(2, xsu, ysu, zsu, scatter=True,markers=['o' for i in range(len(xsu))],\n",
    "           azim=azim, elev=elev);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom colours on a surface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usually the patch face colours are proportional to the z-axis values. In some instances it may be required to use face colours on an $(x,y,x)$ surface that are not derived from the $z$ value.\n",
    "\n",
    "<https://stackoverflow.com/questions/6539944/color-matplotlib-plot-surface-command-with-surface-gradient>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code creates the $(x,y,z)$ surface upon which the face colours will be draped.  The two graphs below are drawn from exactly the same data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyradi.ryplot as ryplot\n",
    "from matplotlib import cm\n",
    "\n",
    "#next line include both 0 and 360 degrees, i.e., overlap on edge\n",
    "angled = np.linspace(0.,360.,25) \n",
    "angler = np.pi * angled / 180.\n",
    "grange = np.linspace(500.,4000.,8)\n",
    "#create a 2-D meshgrid.\n",
    "grangeg, anglerg= np.meshgrid(grange,angler + np.pi * 7.5 / 180)\n",
    "\n",
    "height  = 2000.\n",
    "launch = (1 + np.cos(anglerg) ) ** .1 * (1 - np.exp(-( 500 + grangeg) / 2000.) ) \n",
    "launch *=  np.exp(-( 500 + grangeg) / (6000. -  height))\n",
    "launch = np.where(launch<0.2, 0.2, launch)\n",
    "#normalise\n",
    "launch -= np.min(launch)\n",
    "launch /= np.max(launch)\n",
    "\n",
    "pm = ryplot.Plotter(1,1,2,figsize=(16,8))\n",
    "pm.polarMesh(1,angler+np.pi, grange, launch.T,\n",
    "    ptitle='Probability of launch for height {:.0f} [m]'.format(height),\n",
    "    radscale=[0, 4000], cbarshow=True,\n",
    "    cbarorientation='vertical', cbarcustomticks=[], cbarfontsize=12,\n",
    "    rgrid=[500], thetagrid=[45], drawGrid=True,\n",
    "    direction='clockwise', zerooffset=np.pi/2, )\n",
    "pm.polar3d(2, angler, grange, launch, zlabel='zlabel',\n",
    "    linewidth=1, zscale=[0, 1], azim=135, elev=60, alpha=0.5,edgeCol=['k']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next download and plot the data to form the face colours to be draped across the shape calculated above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyradi.ryutils as ryutils\n",
    "# import pyradi.ryplot as ryplot\n",
    "# import numpy as np\n",
    "\n",
    "tgzFilename = 'missLUT-2000-e.tgz'\n",
    "destinationDir = '.'\n",
    "tarFilename = 'missLUT-2000-e.tar'\n",
    "url = 'https://raw.githubusercontent.com/NelisW/pyradi/master/pyradi/data/'\n",
    "dlNames = ryfiles.downloadUntar(tgzFilename, url, destinationDir, tarFilename)\n",
    "\n",
    "print('filesAvailable are {}'.format(dlNames))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(dlNames[0]) as f:\n",
    "    lines = f.readlines()\n",
    "    xlabel, ylabel, ptitle = lines[0].split()\n",
    "#read the lookup table, extract azim, zenith and table\n",
    "aArray = np.loadtxt(dlNames[0], skiprows=1, dtype=float)\n",
    "azim1D = aArray[1:, 0] \n",
    "height1D = aArray[0, 1:] * 180 / np.pi\n",
    "missD = aArray[1:, 1:]\n",
    "missD = np.clip(missD,0,100.)\n",
    "\n",
    "pm = ryplot.Plotter(1,1,2,figsize=(16,8))\n",
    "pm.polarMesh(1,angler+np.pi, grange, missD.T,\n",
    "    ptitle='Miss distance {:.0f} [m]'.format(height), shading='gouraud',\n",
    "    radscale=[0, 4000], cbarshow=True,  meshCmap=cm.cool_r,\n",
    "    cbarorientation='vertical', cbarcustomticks=[], cbarfontsize=12,\n",
    "    rgrid=[500], thetagrid=[45], drawGrid=True,\n",
    "    direction='clockwise', zerooffset=np.pi/2, )\n",
    "\n",
    "missD /= np.max(missD)\n",
    "pm.polar3d(2, angler, grange, launch, zlabel='zlabel',facecolors=cm.cool_r(missD),\n",
    "    linewidth=1, zscale=[0, 1], azim=135, elev=60, alpha=0.5,edgeCol=['k']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive three-dimensional plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following example provides an interactive window allowing zooming and view direction changes.\n",
    "\n",
    "The interactive window breaks the IPython notebook execution if all cells are executed. For this reason it is commented out here, please uncomment the `%matplotlib tk` and `p.getPlot().show()` lines to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def myFunc(x,y):\n",
    "  scale = np.sqrt(np.exp(-(x**2 +y**2)))\n",
    "  return np.sin(2 * x) * np.cos(4 * y) * scale\n",
    "\n",
    "x = np.linspace(-2, 2, 101)\n",
    "y = np.linspace(-2, 2, 101)\n",
    "varx, vary = np.meshgrid(x, y)\n",
    "zdata = myFunc(varx.flatten(), vary.flatten()).reshape(varx.shape)\n",
    "# %matplotlib qt\n",
    "# %matplotlib tk\n",
    "p = ryplot.Plotter(1,1,1,figsize=(10,7));\n",
    "p.mesh3D(1, varx, vary, zdata, ptitle='Title', xlabel='x', ylabel='y', zlabel='z',\n",
    "  plotCol=['r'], edgeCol=['k'], rstride=3, cstride=3, linewidth= 0.1, maxNX=5, maxNY=5, maxNZ=0,\n",
    "  drawGrid=True, cbarshow=True, alpha=0.5);\n",
    "# p.getPlot().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alternative three-dimensional plot libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### mpld3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mpld3 project brings together Matplotlib, the popular Python-based graphing library, and D3js, the popular Javascript library for creating interactive data visualizations for the web. The result is a simple API for exporting your matplotlib graphics to HTML code which can be used within the browser, within standard web pages, blogs, or tools such as the IPython notebook.  \n",
    "<https://mpld3.github.io/>  \n",
    "<https://github.com/jakevdp/mpld3>  \n",
    "<https://chart-studio.plotly.com/~mpld3/#/>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Marker Symbols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib has a rich marker set.  These markers have several variations in shape, fill and colour. The \n",
    "[ryplot.Markers](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html#pyradi.ryplot.Markers) class provides a mechanism to add markers to a graph, without actually plotting a line.  This is useful to mark some region in a contour graph or an image.  The [ryplot.Markers](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html#pyradi.ryplot.Markers) class uses filled markers (13 shapes), each with six fill styles, with at least seven colours and al least seven alternative colours; giving  3822 variations of marker symbols.\n",
    "\n",
    "Each symbol is cut into half, with the symbol colour in one half and the alternative colour in the other half.  The fill style van be one of ['full', 'left', 'right', 'bottom', 'top', 'none'].\n",
    "\n",
    "The filled markers include [`o`, `v`, `^`, `<`, `>`, `8`, `s`, `p`, `*`, `h`, `H`, `D`, `d`], as illustrated in the graph below.\n",
    "\n",
    "<https://matplotlib.org/stable/gallery/lines_bars_and_markers/marker_reference.html>  \n",
    "<https://matplotlib.org/stable/api/markers_api.html>  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [ryplot.Markers](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html#pyradi.ryplot.Markers) works as follows:\n",
    "\n",
    "1. Create a [ryplot.Plotter](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html#pyradi.ryplot.Plotter) instance and create the plot you want to superimpose the markers onto.\n",
    "\n",
    "2. Create a [ryplot.Markers](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html#pyradi.ryplot.Markers)  instance to contain the markers.\n",
    "\n",
    "3.  Then [Markers.add](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html#pyradi.ryplot.Markers.add) as many markers as required. The  function signature is `add(x, y, markerfacecolor=None, markerfacecoloralt=None, markeredgecolor=None, marker=None, markersize=None, fillstyle=None)`. The parameters are self-describing.  Provide a  marker and fillstyle definition as described below.\n",
    "\n",
    "4.  When all the markers are added, [Markers.plot](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html#pyradi.ryplot.Markers.plot) the markers on the subplot.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following graph each symbol is labelled with the following format: colour-markerID-fillStyle. Each symbol can be given a primary colour and an alternative colour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "from matplotlib import rc\n",
    "import matplotlib.lines as mlines\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "\n",
    "plt.rc('text', usetex=False) # otherwise, '^' will cause trouble\n",
    "\n",
    "pm = ryplot.Plotter(1, 1, 1,'Marker symbols',figsize=(18,12))\n",
    "pm.plot(1,np.asarray([-8,60]),np.asarray([-10,125]),plotCol=['w'], drawGrid=False)\n",
    "markers = ryplot.Markers(markerfacecolor='y', marker='*')\n",
    "\n",
    "colors = itertools.cycle(['b', 'g', 'r', 'c', 'm', 'y', 'k'])\n",
    "\n",
    "for j,marker in enumerate(mlines.Line2D.filled_markers):\n",
    "    for i,fs in enumerate(mlines.Line2D.fillStyles):\n",
    "        \n",
    "        \n",
    "        if (sys.version_info > (3, 0)):\n",
    "             # Python 3 code in this block\n",
    "             color = next(colors)\n",
    "        else:\n",
    "             # Python 2 code in this block\n",
    "            color = colors.next()\n",
    "        \n",
    "\n",
    "        markers.add(i*10,j*10,markerfacecolor=color, marker=marker,fillstyle=fs,markerfacecoloralt='PaleGreen')\n",
    "        lab = '{}-{}-{}'.format(color, marker,fs)\n",
    "        pm.getSubPlot(1).text(i*10+1.5,j*10, '{}-{}-{}'.format(color, marker,fs), \n",
    "              horizontalalignment='left', fontsize=16)\n",
    "        \n",
    "markers.plot(pm.getSubPlot(1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following graphic shows markers superimposed on some of the graphs already seen in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pm = ryplot.Plotter(1, 1, 2,'Marker symbols',figsize=(18,6))\n",
    "#create the radial and angular vectors\n",
    "r = np.linspace(0,1.25,100)\n",
    "p = np.linspace(0,2*np.pi,100)\n",
    "P, R = np.meshgrid(p, r)\n",
    "value =  ((R**2 - 1)**2) * np.sin(2 * P)\n",
    "pm.polarMesh(1, p, r, value, rgrid=[0,5], drawGrid=True, cbarshow=False, contourLine=False)\n",
    "\n",
    "delta = 0.025\n",
    "x = np.arange(-3.0, 3.0, delta)\n",
    "y = np.arange(-2.0, 2.0, delta)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)\n",
    "Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)\n",
    "# difference of Gaussians\n",
    "Z = 10.0 * (Z2 - Z1)\n",
    "pm.meshContour(2, X, Y, Z, levels=30, ptitle='meshContour', xlabel='X-value', ylabel='Y-value',\n",
    "                shading='gouraud', plotCol=['k'], titlefsize=12, meshCmap=cm.rainbow,zeroContourLine=True,\n",
    "                cbarshow=True, cbarorientation='vertical', cbarfontsize=12, drawGrid=False, \n",
    "                negativeSolid=False, contourFill=True, contourLine=False, logScale=False)\n",
    "\n",
    "# add filled markers\n",
    "markers = ryplot.Markers(markerfacecolor='r', marker='*')\n",
    "markers.add(0*np.pi/6,1)\n",
    "markers.add(1*np.pi/6,0.9,markerfacecolor='y', marker='^',fillstyle='top')\n",
    "markers.add(2*np.pi/6,0.8,fillstyle='top',markeredgecolor='g')\n",
    "markers.add(3*np.pi/6,0.7,marker='v')\n",
    "markers.add(4*np.pi/6,0.6,marker='p',fillstyle='top')\n",
    "markers.add(5*np.pi/6,0.5,markerfacecolor='r',marker='H',fillstyle='bottom',markerfacecoloralt='PaleGreen')\n",
    "markers.add(6*np.pi/6,0.4,marker='D',fillstyle='left',markerfacecoloralt='Sienna',markersize=10)\n",
    "markers.plot(pm.getSubPlot(1))\n",
    "markers.plot(pm.getSubPlot(2));\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import rc\n",
    "plt.rc('text', usetex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick and dirty image display\n",
    "\n",
    "https://twitter.com/jakevdp/status/939887746134323201  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "def display_image(x):\n",
    "    x_scaled = np.uint8(255 * (x-x.min())/(x.max() - x.min()))\n",
    "    return  Image.fromarray(x_scaled)\n",
    "\n",
    "display_image(np.random.rand(200,200))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pyradi tools for plotting images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [ryplot.Plotter.showImage](http://nelisw.github.io/pyradi-docs/_build/html/ryplot.html?highlight=showimage#pyradi.ryplot.Plotter.showImage) function provides the functionality to display images in a graph.  The user can supply a colour map (a grey level map is also a colour map).\n",
    "\n",
    "The function signature is\n",
    "\n",
    "    showImage(plotnum, img,  ptitle=None, cmap=plt.cm.gray, titlefsize=12, cbarshow=False, \n",
    "                  cbarorientation = 'vertical', cbarcustomticks=[], cbarfontsize = 12)\n",
    "\n",
    "- `plotnum (int)` subplot number, 1-based index\n",
    "- `img (np.ndarray)` numpy 2d array\n",
    "- `ptitle (string)` plot title (optional).\n",
    "- `xlabel (string)` x axis label (optional).\n",
    "- `ylabel (string)` y axis label (optional).\n",
    "- `cmap` matplotlib colormap, default gray (optional).\n",
    "- `fsize (int)` title font size, default 12pt (optional).\n",
    "- `cbarshow (bool)` if true, the show a colour bar  (optional).\n",
    "- `cbarorientation (string)` 'vertical' (right) or 'horizontal' (below)  (optional).\n",
    "- `cbarcustomticks zip([z values/float],[tick labels/string])`  define custom colourbar ticks locations for given z values(optional).\n",
    "- `cbarfontsize (int)` font size for colour bar  (optional).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next example display several image plots and variations of the image presentations.  The first three graphs are from the same data set.\n",
    "\n",
    "1.  The image is plotted with colour bar underneath the image.  The `plt.cm.winter` colour map is used.\n",
    "\n",
    "2.  The image is plotted with colour bar besides the image.  The `ryplot.cubehelixcmap()` colour map is used.\n",
    "\n",
    "3.  Sections through the dataset is shown for correlation with the first two images.\n",
    "\n",
    "4.  A blurred Siemens star is read from a png file and displayed with a grey scale colour map.\n",
    "\n",
    "5.  The same data set as in the blurred Siemens star, but this star is unfolded to a cartesian coordinate system.\n",
    "\n",
    "6.  The picture of a simulated infrared image of an aircraft is read from a unsigned long binary raw file.\n",
    "\n",
    "Download the files to be plotted from the internet (internet connection required)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyradi.ryfiles as ryfiles\n",
    "import pyradi.ryutils as ryutils\n",
    "\n",
    "tgzFilename = 'sample.tgz'\n",
    "destinationDir = '.'\n",
    "tarFilename = 'sample.tar'\n",
    "url = 'https://raw.githubusercontent.com/NelisW/pyradi/master/pyradi/data/'\n",
    "dlNames = ryfiles.downloadUntar(tgzFilename, url, destinationDir, tarFilename)\n",
    "print('filesAvailable are {}'.format(dlNames))\n",
    "\n",
    "tgzFilename = 'siemensstar.tgz'\n",
    "tarFilename = 'siemensstar.tar'\n",
    "dlNames = ryfiles.downloadUntar(tgzFilename, url, destinationDir, tarFilename)\n",
    "print('filesAvailable are {}'.format(dlNames))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use imread from the imageio library to read the png images from file.\n",
    "\n",
    "from imageio import imread\n",
    "#read from the binary file using a pyradi function\n",
    "frames, img = ryfiles.readRawFrames('sample.ulong', 100, 100, np.uint32, [5])\n",
    "\n",
    "xv,yv = np.mgrid[-5:5:21j, -5:5:21j]\n",
    "z = np.sin(np.sqrt(xv**2 + yv**2))\n",
    "\n",
    "I = ryplot.Plotter(4, 2, 3,'Images and Array Linear', figsize=(18, 12))\n",
    "\n",
    "I.showImage(1, z, ptitle='winter colormap, font 10pt', cmap=plt.cm.winter, titlefsize=10,  cbarshow=True, cbarorientation = 'horizontal', cbarfontsize = 7)\n",
    "barticks = zip([-1, 0, 1], ['low', 'med', 'high'])\n",
    "I.showImage(2, z, ptitle='cubehelix colormap, default font ',cmap=ryplot.cubehelixcmap(), cbarshow=True, cbarcustomticks=barticks)\n",
    "I.plot(3, xv[:, 1],  z, \"Sections through shape\",\"x\", \"z\")\n",
    "I.showImage(4, imread('600px-Siemens_star-blurred.png'), ptitle='Defocussed Star')\n",
    "I.showImage(5, imread('Siemens_Star-unfolded.png'), ptitle='Unfolded Defocussed Star')\n",
    "I.showImage(6, img[0], ptitle='Aircraft');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Turbo colour map, a better Jet\n",
    "\n",
    "\n",
    "Turbo, An Improved Rainbow Colormap for Visualization   \n",
    "https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html  \n",
    "Anton Mikhailov\n",
    "\n",
    "One of the most commonly used color mapping algorithms in computer vision applications \n",
    "is Jet, which is high contrast, making it useful for accentuating even weakly \n",
    "distinguished image features. However, if you look at the color map gradient, \n",
    "one can see distinct “bands” of color, most notably in the cyan and yellow regions. \n",
    "This causes sharp transitions when the map is applied to images, which are misleading \n",
    "when the underlying data is actually smoothly varying. Because the rate at which the \n",
    "color changes ‘perceptually’ is not constant, Jet is not perceptually uniform. \n",
    "\n",
    "Today we are happy to introduce Turbo, a new colormap that has the desirable \n",
    "properties of Jet while also addressing some of its shortcomings, such as false detail, \n",
    "banding and color blindness ambiguity.\n",
    "\n",
    "https://gist.github.com/mikhailov-work/ee72ba4191942acecc03fe6da94fc73f  \n",
    "https://gist.githubusercontent.com/FedeMiorelli/640bbc66b2038a14802729e609abfe89/raw/c84943cb48ca7d7d90e2b882ea46e07613dcfe13/turbo_colormap_mpl.py\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "XX, YY = np.meshgrid(np.linspace(0,1,100), np.linspace(0,1,100))\n",
    "ZZ = np.sqrt(XX**2 + YY**2)\n",
    "q = ryplot.Plotter(1,1,3,figsize=(8,4),doWarning=False)\n",
    "cimage = q.showImage(1,ZZ,'Jet', cmap='jet')\n",
    "cimage = q.showImage(2,ZZ,'Turbo', cmap='turbo')\n",
    "cimage = q.showImage(3,ZZ,'Turbo', cmap='iturbo')\n",
    "q.saveFig('jet-turbo.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mayavi 3D visualisation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Mayavi](http://docs.enthought.com/mayavi/mayavi/) is an application and library for interactive scientific data visualization and 3D plotting in Python.\n",
    "\n",
    "\n",
    "Install Mayavi from [PyPI](https://pypi.python.org/pypi/mayavi) or if you are using Anaconda install using conda:\n",
    "\n",
    "\n",
    "    conda update conda\n",
    "    conda install mayavi\n",
    "\n",
    "Note that the Mayavi installation may downgrade your numpy, scipy and other installs. On my PC when installing Mayavi:\n",
    "                                                                    \n",
    "    The following packages will be DOWNGRADED:                          \n",
    "\n",
    "        numexpr:      2.5-np110py27_0    --> 2.4.4-np19py27_0           \n",
    "        numpy:        1.10.4-py27_0      --> 1.9.3-py27_1               \n",
    "        scikit-learn: 0.17.1-np110py27_0 --> 0.16.1-np19py27_0          \n",
    "        scipy:        0.17.0-np110py27_0 --> 0.16.0-np19py27_0          \n",
    "\n",
    "The older versions of numpy or scipy may break existing code, or the Jupyter notebook.  I had to upgrade the software again afterwards.\n",
    "\n",
    "    conda install numpy=1.10.4\n",
    "    conda install scipy=0.17.0\n",
    "    \n",
    "This may result in Mayavi not working as intended, if it breaks with the more recent package.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For some Mayavi examples, see [here](http://docs.enthought.com/mayavi/mayavi/auto/examples.html#example-gallery) and [here](http://docs.enthought.com/mayavi/mayavi/example_exploring_a_vector_field.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "HTML('<iframe src=http://docs.enthought.com/mayavi/mayavi/auto/examples.html#example-gallery  width=1000 height=200></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python and module versions, and dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Software versions\n",
      "Python:   3.9.18 64bit [MSC v.1929 64 bit (AMD64)]\n",
      "IPython:   8.18.1\n",
      "OS:   Windows 10 10.0.22621 SP0\n",
      "matplotlib:   3.5.3\n",
      "numpy:   1.21.6\n",
      "pyradi:   1.1.4\n",
      "scipy:   1.10.1\n",
      "pandas:   1.5.3\n",
      "Tue Jan 09 11:34:58 2024 South Africa Standard Time\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import pyradi.ryutils as ryutils\n",
    "    print(ryutils.VersionInformation('matplotlib,numpy,pyradi,scipy,pandas'))\n",
    "except:\n",
    "    print(\"pyradi.ryutils not found\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  }
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