{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demonstration of using a custom color list with `donut_plot_with_subgroups_from_dataframe.py`\n", "\n", "Demonstrating use of `donut_plot_with_subgroups_from_dataframe.py` with a custom color list when using the imported script in a Jupyter notebook.\n", "\n", "The same approach can be used with the two related scripts `donut_plot_with_total_summary_and_subgroups_from_dataframe.py` and `donut_plot_with_total_binary_summary_and_binary_state_subgroups.py` and this is covered at the bottom of this notebook.\n", "\n", "This builds on the demonstration [here](index.ipynb) and so you may want to look at that first if you are not familiar with the script `donut_plot_with_subgroups_from_dataframe.py` first. And also see [here](https://github.com/fomightez/donut_plots_with_subgroups) for more information.\n", "\n", "In addition to this demonstration page, there are several other demonstration pages:\n", "\n", "- [Demonstrate the full-featured script to make a single donut plot with subgroups](index.ipynb)\n", "- [Demonstrate the basics](demo_basics_from_df.ipynb)\n", "- [Demonstrate a full-featured script that plots a summary for the subgroups in addition to the donut plot with subgroups](demo_summary_subgroups.ipynb)\n", "- [Demonstrate a full-featured script that plots a summary for binary data in addition to the donut plot with the binary group broken down by a group](demo_summary_binary.ipynb)\n", "\n", "\n", "-----\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparation\n", "\n", "Let's get the script to insure it is here. (It won't do antyhing if the script is already retrieved.)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "file_needed = \"donut_plot_with_subgroups_from_dataframe.py\"\n", "if not os.path.isfile(file_needed):\n", " !curl -OL https://raw.githubusercontent.com/fomightez/donut_plots_with_subgroups/master/donut_plot_with_subgroups_from_dataframe.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a demo dataframe will be used for input data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
groupsubgroupsub-subgroup
0A1frizzled
1A1lethargic
2A1polythene
3A1epic
4A2frizzled
\n", "
" ], "text/plain": [ " group subgroup sub-subgroup\n", "0 A 1 frizzled\n", "1 A 1 lethargic\n", "2 A 1 polythene\n", "3 A 1 epic\n", "4 A 2 frizzled" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "obs = [('A', 1, \"frizzled\"), \n", " ('A', 1, \"lethargic\"), \n", " ('A', 1, \"polythene\"), \n", " ('A', 1, \"epic\"),\n", " ('A', 2, \"frizzled\"), \n", " ('A', 2, \"lethargic\"), \n", " ('A', 2, \"epic\"),\n", " ('A', 3, \"frizzled\"), \n", " ('A', 3, \"lethargic\"),\n", " ('A', 3, \"polythene\"),\n", " ('A', 3, \"epic\"),\n", " ('A', 3, \"bedraggled\"),\n", " ('B', 1, \"frizzled\"), \n", " ('B', 1, \"lethargic\"),\n", " ('B', 1, \"polythene\"),\n", " ('B', 1, \"epic\"),\n", " ('B', 1, \"bedraggled\"),\n", " ('B', 1, \"moombahcored\"),\n", " ('B', 2, \"frizzled\"), \n", " ('B', 2, \"lethargic\"),\n", " ('B', 2, \"polythene\"),\n", " ('B', 2, \"epic\"),\n", " ('B', 2, \"bedraggled\"),\n", " ('C', 1, \"frizzled\"), \n", " ('C', 1, \"lethargic\"),\n", " ('C', 1, \"polythene\"),\n", " ('C', 1, \"epic\"),\n", " ('C', 1, \"bedraggled\"),\n", " ('C', 1, \"moombahcored\"),\n", " ('C', 1, \"zoned\"),\n", " ('C', 1, \"erstaz\"),\n", " ('C', 1, \"mined\"),\n", " ('C', 1, \"liberated\"),\n", " ('C', 2, \"frizzled\"), \n", " ('C', 2, \"lethargic\"),\n", " ('C', 2, \"polythene\"),\n", " ('C', 2, \"epic\"),\n", " ('C', 2, \"bedraggled\"),\n", " ('C', 3, \"frizzled\"), \n", " ('C', 3, \"lethargic\"),\n", " ('C', 3, \"polythene\"),\n", " ('C', 3, \"epic\"),\n", " ('C', 3, \"bedraggled\"),\n", " ('C', 4, \"bedraggled\"),\n", " ('C', 4, \"frizzled\"), \n", " ('C', 4, \"lethargic\"),\n", " ('C', 4, \"polythene\"),\n", " ('C', 4, \"epic\"),\n", " ('C', 5, \"frizzled\"), \n", " ('C', 5, \"lethargic\"),\n", " ('C', 5, \"polythene\"),\n", " ('C', 5, \"epic\"),\n", " ('C', 5, \"bedraggled\"),\n", " ('C', 5, \"moombahcored\")]\n", "labels = ['group', 'subgroup', 'sub-subgroup']\n", "df = pd.DataFrame.from_records(obs, columns=labels)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have script and the dataframe, we are ready to demonstrate using our own colors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using a custom color list\n", "\n", "In the [previous notebook](index.ipynb), I used the following to import the main function of the `donut_plot_with_subgroups_from_dataframe.py` script.\n", "\n", "```python\n", "from donut_plot_with_subgroups_from_dataframe import donut_plot_with_subgroups_from_dataframe\n", "```\n", "\n", "While that was useful for making the call of the script shorter, I couldn't come up with a way to replace the colormap generator/function when I did it that way. (See [here](https://stackoverflow.com/a/710603/8508004) for more about the pros and cons.) \n", "And so you'll note that here I **import the script slightly differently and then call it differently**. This is **important** to making this process to provide your own custom color list work as described here. (*Note: there is most likely a way to do it the other way; however, my attempts to work out what that was all failed and the one I am suggesting here works.)\n", "\n", "The next cell demonstrates how you'll need to import for this to work:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import donut_plot_with_subgroups_from_dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's use the main function with the standard colormapping for now so we can be sure we are overriding it later. \n", "The next cell calls the script." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Note: No list to specify high to low intensity coloring provided, and so using '1,2,3,4,5',\n", "where leftmost identifer corresponds to most intense and rightmost is least.\n", "Provide a Python list as `hilolist` when calling the function to specify the order of intensity.\n", "\n", "Plot figure object returned." ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "donut_plot_with_subgroups_from_dataframe.donut_plot_with_subgroups_from_dataframe(df=df,groups_col=\"group\",subgroups_col=\"subgroup\", sort_on_subgroup_name=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, the script runs; however, those are not the colors we want. Let's work on getting customized colors as part of the plot.\n", "\n", "The generator function `sequential_color_maps_generator` is the one in the current script that generates the sequential colormaps used. We'd like to effectively replace that with the following where an example color list `color_list_to_use` is used:\n", "\n", "```python\n", "#ALTERNATE WHEN WANT SET COLORS YOURSELF (SUCH AS TO MATCH A PALETTE FOR POSTER):\n", "# This will cycle over a list of colors\n", "def sequential_color_maps_generator():\n", " '''\n", " This will cycle over a built-in list of colors to return color maps.\n", "\n", " Edit the list of colors as hexidecimals to change them to yours.\n", "\n", " Returns sequential colormaps based on the next color in list.\n", " '''\n", " color_list_to_use = [\"#17254E\",\"#F46249\",\"#BBCACD\",\"#30688D\",\"#B1A091\"]\n", " from itertools import cycle\n", " import seaborn as sns\n", " color_cycle = cycle(color_list_to_use)\n", " for cl in color_cycle:\n", " yield sns.light_palette(cl, as_cmap=True)\n", "```\n", "\n", "Indeed, you could edit the script to replace the defining of ``sequential_color_maps_generator`` with that code above. (Feel free to do that if this a one-off and you know what you are doing. Change the list of colors to your list of colors in hexidecimal format.)\n", "\n", "However, because we are in the Jupyter environment we can alternatively replace the generator function in memory and so when the function is called it uses the updated version and uses the custom color list. This has the advantage that we can reproducibly step through to do it every time. The steps taken are all included in the notebook. If you went and edited the code by hand, it wouldn't necessarily be as clear that you did that. \n", "The following will step through doing that replacing now. First I'll demonstrate it and then encourage you to edit the colors to your own custom list. Of course, if you understand what is happening, dive in and change the colors to your own now.\n", "\n", "Run this next cell to define a generator function we are going to swap in to update `sequential_color_maps_generator`. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def new_version():\n", " '''\n", " This will cycle over a built-in list of colors to return color maps.\n", "\n", " Edit the list of colors as hexidecimals to change them to yours.\n", "\n", " Returns sequential colormaps based on the next color in list.\n", " '''\n", " color_list_to_use = [\"#17254E\",\"#F46249\",\"#BBCACD\",\"#30688D\",\"#B1A091\"]\n", " from itertools import cycle\n", " import seaborn as sns\n", " color_cycle = cycle(color_list_to_use)\n", " for cl in color_cycle:\n", " yield sns.light_palette(cl, as_cmap=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So now have defined a new function that we want to be the action `sequential_color_maps_generator` actually does when it is called as part of the main function of the script. Now we swap it in, based on [this approach](https://stackoverflow.com/a/2789542/8508004)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "donut_plot_with_subgroups_from_dataframe.sequential_color_maps_generator = new_version" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you run that cell above nothing will seem to happen; however, behind-the-scenes the in-memory generator function has been swapped with the one that cycles over our custom-defined list.\n", "\n", "To see that it has changed, run the next cell." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Note: No list to specify high to low intensity coloring provided, and so using '1,2,3,4,5',\n", "where leftmost identifer corresponds to most intense and rightmost is least.\n", "Provide a Python list as `hilolist` when calling the function to specify the order of intensity.\n", "\n", "Plot figure object returned." ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "donut_plot_with_subgroups_from_dataframe.donut_plot_with_subgroups_from_dataframe(df=df,groups_col=\"group\",subgroups_col=\"subgroup\", sort_on_subgroup_name=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Yay! The color list is being used when `sequential_color_maps_generator` gets called by the script.\n", "\n", "Okay, that process works. All that should remain now is to edit the colors now to what you want to be your custom colors list and run it with your own data as outlined here and in the other [demo](index.ipynb).\n", "\n", "Questions that may remain:\n", "\n", "**So I am using this on the command line or equivalent, what can I do?**\n", "\n", "You have to edit the script because there is no in-memory namespace you can edit. However, this editing can be done in a way that can be documented and reproducibly done in a way that can also be easily repeated. 'Easily repeated' is important because unless you save the new version yourself and specify using it, it has to be done in every fresh session. *To Be Done: Illustrate how to use sed/awk/Python to replace the code from a notebook.*\n", "\n", "**I am using the related scripts, either `donut_plot_with_total_summary_and_subgroups_from_dataframe.py` or `donut_plot_with_total_binary_summary_and_binary_state_subgroups.py`, what can I do?**\n", "\n", "The same process would work. \n", "However, I didn't include illustrating with them for two reasons. \n", "The primary reason is that it would be very redundant as you basically just need to to change the script name in the code above and it will work. The other reason is that the colors for the summary plot on the left gets drawn from the same resource, and I set up the code to cycle on the one list of colos. And so if you provide less colors than you have subgroups and groups, some of the same colors are going to end up in both the right and left plots. Here is a variation on the code above you can use to more effectively control each side of the plots where there is a summary sub-plot on the left:\n", "\n", "```python\n", "#ALTERNATE WHEN WANT SET COLORS YOURSELF (SUCH AS TO MATCH A PALETTE FOR POSTER):\n", "# This will go through three colors for a left, summar plot and then cycle for the \n", "# right plot.\n", "color_list_to_use = [\"#17254E\",\"#F46249\",\"#BBCACD\",\"#30688D\",\"#B1A091\"]\n", "def new_version():\n", " '''\n", " This will cycle over a built-in list of colors to return color maps.\n", " The first list being for the summary on the left. The other being for\n", " the sub-plot on the right.\n", "\n", " Edit the list of colors as hexidecimals to change them to yours.\n", "\n", " Returns sequential colormaps based on the next color in respective list.\n", " '''\n", " import seaborn as sns\n", " color_list_to_use_in_summary = [\"#17254E\",\"#F46249\",\"#BBCACD\"]\n", " color_list_to_use_on_right = [\"#30688D\",\"#B1A091\"]\n", " for cl in ccolor_list_to_use_in_summary:\n", " yield sns.light_palette(cl, as_cmap=True)\n", " from itertools import cycle\n", " color_cycle = cycle(color_list_to_use)\n", " for cl in color_cycle:\n", " yield sns.light_palette(cl, as_cmap=True)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----\n", "\n", "Enjoy." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }