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@spestana
Last active August 1, 2022 15:00
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Stacked bar chart for two data sets, normalized by the number of points within each bin, with numpy and matplotlib
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x20f2ce80860>]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# random data\n",
"a = np.random.randn(100)\n",
"b = np.random.randn(100) + np.random.randn(1)\n",
"\n",
"plt.figure()\n",
"plt.plot(a)\n",
"plt.plot(b)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# note that these give you bin edges, not bin centers\n",
"a_count, a_bin_edges = np.histogram(a, bins=range(-6,6))\n",
"b_count, b_bin_edges = np.histogram(b, bins=range(-6,6))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x20f2d5ea748>]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# to get bin centers, add 1/2 of the bin width (in this case 1/2 of 1 is 0.5) to the bin edges \n",
"# (but drop the last bin edge value)\n",
"a_bin_centers = a_bin_edges[:-1] + 0.5\n",
"b_bin_centers = b_bin_edges[:-1] + 0.5\n",
"\n",
"# plot a PDF curve for each\n",
"plt.figure()\n",
"plt.plot(a_bin_centers,a_count)\n",
"plt.plot(b_bin_centers,b_count)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\steve\\Anaconda3\\envs\\rasterenv\\lib\\site-packages\\ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in true_divide\n",
" \n",
"C:\\Users\\steve\\Anaconda3\\envs\\rasterenv\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n",
" This is separate from the ipykernel package so we can avoid doing imports until\n"
]
}
],
"source": [
"# calculate percent based on total (a and b) in each bin\n",
"a_percent = a_count / (a_count + b_count)\n",
"b_percent = b_count / (a_count + b_count)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# remove nans where there was missing a or b data\n",
"a_percent[np.isnan(a_percent)] = 0\n",
"b_percent[np.isnan(b_percent)] = 0"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x20f2d674dd8>]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plot these normalized values now\n",
"plt.figure()\n",
"plt.plot(a_bin_centers,a_percent)\n",
"plt.plot(b_bin_centers,b_percent)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x20f2d6f55c0>]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# make sure each bin adds up to 100% (where we have data, otherwise 0)\n",
"plt.figure()\n",
"plt.plot(a_bin_centers,a_percent+b_percent)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x20f2d7554a8>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# make the stacked bar plot\n",
"fig, ax = plt.subplots()\n",
"ax.bar(a_bin_edges[1:], a_percent, width=1, label='a')\n",
"ax.bar(a_bin_edges[1:], b_percent, width=1, bottom=a_percent, label='b')\n",
"plt.xlim(-6,6)\n",
"plt.legend()"
]
}
],
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
def stacked(adata, bdata, x_min, x_max, bin_width, title, xlabel):
# by Cassie Lumbrazo, 7/29/2022
np.seterr(invalid='ignore') #ignore the runtime errors for 0/0
# define datasets
a = adata.copy(deep=True)
b = bdata.copy(deep=True)
# note that these give you bin edges, not bin centers
a_count, a_bin_edges = np.histogram(a, bins=range(int(x_min), int(x_max), int(bin_width)))
b_count, b_bin_edges = np.histogram(b, bins=range(int(x_min), int(x_max), int(bin_width)))
# to get bin centers, add 1/2 of the bin width (in this case 1/2 of 1 is 0.5) to the bin edges
# (but drop the last bin edge value)
half_range = bin_width / 2
a_bin_centers = a_bin_edges[:-1] + half_range # SINCE SW IN 100 w/m2 BINS
b_bin_centers = b_bin_edges[:-1] + half_range
# calculate percent based on total (a and b) in each bin
a_percent = a_count / (a_count + b_count)
b_percent = b_count / (a_count + b_count)
# remove nans where there was missing a or b data
a_percent[np.isnan(a_percent)] = 0
b_percent[np.isnan(b_percent)] = 0
# make the stacked bar plot
fig, ax = plt.subplots()
ax.bar(a_bin_edges[1:], a_percent, width=bin_width, label=namesnow, color=colorsnow, alpha=.7)
ax.bar(a_bin_edges[1:], b_percent, width=bin_width, bottom=a_percent, label=namesnowunload, color=colorsnowunload, alpha=.7)
plt.xlim(x_min, x_max)
plt.title(title)
plt.xlabel(xlabel)
plt.grid()
plt.legend()
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