{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting with plot.ly backend"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Basic imports\n",
"from physt.examples import normal_h2, normal_h1\n",
"from physt.plotting import plotly\n",
"import physt.plotting\n",
"\n",
"import numpy as np\n",
"np.random.seed(42)\n",
"\n",
"# Set that we want plotly\n",
"physt.plotting.set_default_backend(\"plotly\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following lines are necessary if you want to display plots in the Jupyter notebook."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/vnd.plotly.v1+html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.offline as po\n",
"po.init_notebook_mode()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Define the 1-D example\n",
"H = normal_h1()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The default plot is `bar`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"name": "normal",
"opacity": 1,
"type": "bar",
"uid": "206477f2-3e92-11e9-ba8b-3b7509a6e7b7",
"width": [
0.784863795805467,
0.7848637958054665,
0.784863795805467,
0.784863795805467,
0.784863795805467,
0.7848637958054665,
0.7848637958054674,
0.7848637958054665,
0.7848637958054665,
0.7848637958054678
],
"x": [
-3.529968353715609,
-2.745104557910142,
-1.9602407621046753,
-1.1753769662992084,
-0.39051317049374146,
0.39435062531172527,
1.1792144211171922,
1.9640782169226592,
2.7489420127281257,
3.533805808533593
],
"y": [
10,
88,
485,
1605,
2831,
2844,
1543,
498,
88,
8
]
}
],
"layout": {
"barmode": "overlay"
}
},
"text/html": [
""
],
"text/vnd.plotly.v1+html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"H.plot() # Same as H.plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"mode": "lines",
"name": "normal",
"type": "scatter",
"uid": "207e7ddc-3e92-11e9-ba8b-3b7509a6e7b7",
"x": [
-3.529968353715609,
-2.745104557910142,
-1.9602407621046753,
-1.1753769662992084,
-0.39051317049374146,
0.39435062531172527,
1.1792144211171922,
1.9640782169226592,
2.7489420127281257,
3.533805808533593
],
"y": [
10,
88,
485,
1605,
2831,
2844,
1543,
498,
88,
8
]
}
],
"layout": {}
},
"text/html": [
""
],
"text/vnd.plotly.v1+html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"H.plot.line()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"mode": "markers",
"name": "normal",
"type": "scatter",
"uid": "208e3254-3e92-11e9-ba8b-3b7509a6e7b7",
"x": [
-3.529968353715609,
-2.745104557910142,
-1.9602407621046753,
-1.1753769662992084,
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1.1792144211171922,
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3.533805808533593
],
"y": [
10,
88,
485,
1605,
2831,
2844,
1543,
498,
88,
8
]
}
],
"layout": {}
},
"text/html": [
""
],
"text/vnd.plotly.v1+html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"H.plot.scatter()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Matplotlib compatibility\n",
"\n",
"Plot.ly supports to convert matplotlib figures into plot.ly ones.\n",
"You can use this compatibility with several fancy properties.\n",
"\n",
"Just add the `mpl` keyword argument to your plotting call:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"line": {
"color": "rgba (255, 0, 0, 1)",
"dash": "solid",
"width": 5
},
"mode": "lines",
"name": "normal",
"type": "scatter",
"uid": "212d2774-3e92-11e9-ba8b-3b7509a6e7b7",
"x": [
-3.529968353715609,
-2.745104557910142,
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-1.1753769662992084,
-0.39051317049374146,
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1.1792144211171922,
1.9640782169226592,
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"xaxis": "x",
"y": [
10,
88,
485,
1605,
2831,
2844,
1543,
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],
"yaxis": "y"
}
],
"layout": {
"autosize": false,
"height": 345,
"hovermode": "closest",
"margin": {
"b": 41,
"l": 36,
"pad": 0,
"r": 14,
"t": 26
},
"showlegend": false,
"title": "1D normal distribution",
"titlefont": {
"color": "#000000",
"size": 12
},
"width": 460,
"xaxis": {
"anchor": "y",
"domain": [
0,
1
],
"mirror": "ticks",
"nticks": 9,
"range": [
-3.9224002516183423,
3.9262377064363267
],
"showgrid": false,
"showline": true,
"side": "bottom",
"tickfont": {
"size": 10
},
"ticks": "inside",
"title": "x",
"titlefont": {
"color": "#000000",
"size": 10
},
"type": "linear",
"zeroline": false
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"mirror": "ticks",
"nticks": 7,
"range": [
0.8573324964312685,
3.536714962374179
],
"showgrid": false,
"showline": true,
"side": "left",
"tickfont": {
"size": 10
},
"ticks": "inside",
"type": "log",
"zeroline": false
}
}
},
"text/html": [
""
],
"text/vnd.plotly.v1+html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"H.plot.line(color=\"red\", lw=5, yscale=\"log\", mpl=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot.ly object\n",
"\n",
"If you want to further manipulate the figures, you can return them from\n",
"the function as-is using the `raw` keyword."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Figure({\n",
" 'data': [{'mode': 'markers',\n",
" 'name': 'normal',\n",
" 'type': 'scatter',\n",
" 'uid': '2138449c-3e92-11e9-ba8b-3b7509a6e7b7',\n",
" 'x': array([-3.52996835, -2.74510456, -1.96024076, -1.17537697, -0.39051317,\n",
" 0.39435063, 1.17921442, 1.96407822, 2.74894201, 3.53380581]),\n",
" 'y': array([ 10., 88., 485., 1605., 2831., 2844., 1543., 498., 88., 8.])}],\n",
" 'layout': {}\n",
"})"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"H.plot.scatter(raw=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2D histograms"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"H2 = normal_h2()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"type": "heatmap",
"uid": "214b3bd8-3e92-11e9-ba8b-3b7509a6e7b7",
"z": [
[
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2,
6,
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[
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1
],
[
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1
],
[
6,
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29,
8
],
[
2,
30,
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426,
672,
640,
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2
],
[
1,
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],
[
1,
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],
[
0,
2,
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2,
7,
6,
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],
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0,
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}
],
"layout": {}
},
"text/html": [
""
],
"text/vnd.plotly.v1+html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Default is heatmap\n",
"H2.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Collections"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from physt.histogram_collection import HistogramCollection\n",
"\n",
"collection = HistogramCollection.h1({\n",
" \"small\": np.random.normal(160, 20, 600),\n",
" \"tall\": np.random.normal(180, 20, 1000),\n",
" \"huge\": np.random.normal(200, 20, 400),\n",
" \"gigantic\": np.random.normal(220, 20, 200)\n",
"}, \"human\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
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{
"mode": "lines",
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{
"mode": "lines",
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"mode": "lines",
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"mode": "lines",
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],
"layout": {}
},
"text/html": [
""
],
"text/vnd.plotly.v1+html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"collection.plot.line()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
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"layout": {
"barmode": "overlay"
}
},
"text/html": [
""
],
"text/vnd.plotly.v1+html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Let's see normalized histograms in the collection\n",
"collection.normalize_bins().plot(barmode=\"overlay\", alpha=0.3)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
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