deploy: e0bb3d2

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 0fee1f01507d8d01e27abb7c79ccc3a6
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_downloads/017531d7482ba7804ed7eada3f5b6b1c/layered_histogram.py

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+"""
+Layered histograms
+------------------
+
+This example shows how to plot multiple
+histograms as layers on single plot.
+
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from emcpy.plots.plots import Histogram
+from emcpy.plots.create_plots import CreatePlot, CreateFigure
+
+
+def main():
+    # Generate test data for histogram plots
+    mu = 100  # mean of distribution
+    sigma = 15  # standard deviation of distribution
+    data1 = mu + sigma * np.random.randn(450)
+    data2 = mu + sigma * np.random.randn(225)
+
+    # Create histogram objects
+    hst1 = Histogram(data1)
+    hst1.color = 'tab:green'
+    hst1.alpha = 0.7
+    hst1.label = 'data 1'
+
+    hst2 = Histogram(data2)
+    hst2.color = 'tab:purple'
+    hst2.alpha = 0.7
+    hst2.label = 'data 2'
+
+    # Create histogram plot object and add features
+    plot1 = CreatePlot()
+    plot1.plot_layers = [hst1, hst2]
+    plot1.add_title(label='Test Histogram Plot')
+    plot1.add_xlabel(xlabel='X Axis Label')
+    plot1.add_ylabel(ylabel='Y Axis Label')
+    plot1.add_legend()
+
+    # Create figure and save as png
+    fig = CreateFigure()
+    fig.plot_list = [plot1]
+    fig.create_figure()
+
+    plt.show()
+
+
+if __name__ == '__main__':
+    main()

_downloads/0bb05dfab40171a5a8e1daed7344798f/layered_histogram.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Layered histograms\n\nThis example shows how to plot multiple\nhistograms as layers on single plot.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom emcpy.plots.plots import Histogram\nfrom emcpy.plots.create_plots import CreatePlot, CreateFigure\n\n\ndef main():\n    # Generate test data for histogram plots\n    mu = 100  # mean of distribution\n    sigma = 15  # standard deviation of distribution\n    data1 = mu + sigma * np.random.randn(450)\n    data2 = mu + sigma * np.random.randn(225)\n\n    # Create histogram objects\n    hst1 = Histogram(data1)\n    hst1.color = 'tab:green'\n    hst1.alpha = 0.7\n    hst1.label = 'data 1'\n\n    hst2 = Histogram(data2)\n    hst2.color = 'tab:purple'\n    hst2.alpha = 0.7\n    hst2.label = 'data 2'\n\n    # Create histogram plot object and add features\n    plot1 = CreatePlot()\n    plot1.plot_layers = [hst1, hst2]\n    plot1.add_title(label='Test Histogram Plot')\n    plot1.add_xlabel(xlabel='X Axis Label')\n    plot1.add_ylabel(ylabel='Y Axis Label')\n    plot1.add_legend()\n\n    # Create figure and save as png\n    fig = CreateFigure()\n    fig.plot_list = [plot1]\n    fig.create_figure()\n\n    plt.show()\n\n\nif __name__ == '__main__':\n    main()"
+      ]
+    }
+  ],
+  "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": "3.12.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/23b2839cf2eaa53ad26d37e65d13333c/scatter_with_regression_line.py

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+"""
+Creating a Scatter Plot with a Regression Line
+----------------------------------------------
+
+The following is an example of how to plot data
+as a scatter plot and include a linear regression
+line. Calling the linear regression function will
+give the user the y=mx+b equation as well as the
+R-squared value if the user specifies a legend.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from emcpy.plots.plots import Scatter
+from emcpy.plots.create_plots import CreatePlot, CreateFigure
+from emcpy.stats import get_linear_regression
+
+
+def main():
+    # Create test data
+    rng = np.random.RandomState(0)
+    x = rng.randn(100)
+    y = rng.randn(100)
+
+    # Create Scatter object
+    sctr1 = Scatter(x, y)
+    # Add linear regression feature in scatter object
+    sctr1.do_linear_regression = True
+    sctr1.add_linear_regression()
+
+    # Create plot object and add features
+    plot1 = CreatePlot()
+    plot1.plot_layers = [sctr1]
+    plot1.add_title(label='Test Scatter Plot')
+    plot1.add_xlabel(xlabel='X Axis Label')
+    plot1.add_ylabel(ylabel='Y Axis Label')
+    plot1.add_legend()
+
+    # Create figure
+    fig = CreateFigure()
+    fig.plot_list = [plot1]
+    fig.create_figure()
+
+    plt.show()
+
+
+if __name__ == '__main__':
+    main()

_downloads/2809c46bfe2b922fb375ada1f466b428/map_gridded.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Create a map plot with gridded data\n\nThe following example plots gridded data over\na CONUS domain.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom emcpy.plots import CreatePlot, CreateFigure\nfrom emcpy.plots.map_tools import Domain, MapProjection\nfrom emcpy.plots.map_plots import MapGridded\n\n\ndef main():\n    # Create 2d gridded plot on global domian\n    lats = np.linspace(25, 50, 25)\n    lons = np.linspace(245, 290, 45)\n    X, Y = np.meshgrid(lats, lons)\n    Z = np.random.normal(size=X.shape)\n\n    # Create gridded map object\n    gridded = MapGridded(X, Y, Z)\n    gridded.cmap = 'plasma'\n\n    # Create plot object and add features\n    plot1 = CreatePlot()\n    plot1.plot_layers = [gridded]\n    plot1.projection = 'plcarr'\n    plot1.domain = 'conus'\n    plot1.add_map_features(['coastline'])\n    plot1.add_xlabel(xlabel='longitude')\n    plot1.add_ylabel(ylabel='latitude')\n    plot1.add_title(label='2D Gridded Data', loc='center')\n    plot1.add_grid()\n    plot1.add_colorbar(label='colorbar label',\n                       fontsize=12, extend='neither')\n\n    # Create figure\n    fig = CreateFigure()\n    fig.plot_list = [plot1]\n    fig.create_figure()\n\n    plt.show()\n\n\nif __name__ == '__main__':\n    main()"
+      ]
+    }
+  ],
+  "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": "3.12.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/290e6ea373fb2d702d94fe3b97705bcc/map_scatter_2D.py

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+"""
+Plotting 2D scatter data on map plot
+------------------------------------
+
+Sometimes, users only want to look at the locations
+of their data on a map and do not care about the
+actual values. Below is an example of how to just plot
+lat and lon values on map.
+
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from emcpy.plots import CreatePlot, CreateFigure
+from emcpy.plots.map_tools import Domain, MapProjection
+from emcpy.plots.map_plots import MapScatter
+
+
+def main():
+    # Create test data
+    lats = np.linspace(35, 50, 30)
+    lons = np.linspace(-70, -120, 30)
+
+    # Create scatter plot on CONUS domian
+    scatter = MapScatter(lats, lons)
+    # change colormap and markersize
+    scatter.color = 'tab:red'
+    scatter.markersize = 25
+
+    # Create plot object and add features
+    plot1 = CreatePlot()
+    plot1.plot_layers = [scatter]
+    plot1.projection = 'plcarr'
+    plot1.domain = 'conus'
+    plot1.add_map_features(['coastline', 'states'])
+    plot1.add_xlabel(xlabel='longitude')
+    plot1.add_ylabel(ylabel='latitude')
+    plot1.add_title(label='EMCPy Map', loc='center',
+                    fontsize=20)
+
+    fig = CreateFigure()
+    fig.plot_list = [plot1]
+    fig.create_figure()
+
+    plt.show()
+
+
+if __name__ == '__main__':
+    main()

_downloads/2d36913f893ce1e5968a5140b00be434/multi_line_plot.py

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+"""
+Plotting multiple lines on a single plot
+----------------------------------------
+
+The following example shows how to use EMCPy's
+method to plot several lines on a single subplot.
+The user creates three separate objects using different
+data and plots them on the same layer.
+
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from emcpy.plots.plots import LinePlot
+from emcpy.plots.create_plots import CreatePlot, CreateFigure
+
+
+def _getLineData():
+    # generate test data for line plots
+
+    x1 = [0, 401, 1039, 2774, 2408]
+    x2 = [500, 250, 710, 1515, 1212]
+    x3 = [400, 150, 910, 1215, 850]
+    y1 = [0, 2.5, 5, 7.5, 12.5]
+    y2 = [1, 5, 6, 8, 10]
+    y3 = [1, 4, 5.5, 9, 10.5]
+
+    return x1, y1, x2, y2, x3, y3
+
+
+def main():
+    # create line plot with multiple lines
+    x1, y1, x2, y2, x3, y3 = _getLineData()
+    lp1 = LinePlot(x1, y1)
+    lp1.label = 'line 1'
+
+    lp2 = LinePlot(x2, y2)
+    lp2.color = 'tab:green'
+    lp2.label = 'line 2'
+
+    lp3 = LinePlot(x3, y3)
+    lp3.color = 'tab:red'
+    lp3.label = 'line 3'
+
+    plot1 = CreatePlot()
+    plot1.plot_layers = [lp1, lp2, lp3]
+    plot1.add_title('Test Line Plot')
+    plot1.add_xlabel('X Axis Label')
+    plot1.add_ylabel('Y Axis Label')
+    plot1.add_legend(loc='upper right')
+
+    fig = CreateFigure()
+    fig.plot_list = [plot1]
+    fig.create_figure()
+
+    plt.show()
+
+
+if __name__ == '__main__':
+    main()