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56 lines
1.3 KiB
Python
56 lines
1.3 KiB
Python
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# TRAINS - Example of Matplotlib and Seaborn integration and reporting
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#
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import numpy as np
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import matplotlib.pyplot as plt
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from trains import Task
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# Create a new task, disable automatic matplotlib connect
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task = Task.init(
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project_name='examples',
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task_name='Manual Matplotlib example',
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auto_connect_frameworks={'matplotlib': False}
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)
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# Create plot and explicitly report as figure
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N = 50
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x = np.random.rand(N)
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y = np.random.rand(N)
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colors = np.random.rand(N)
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area = (30 * np.random.rand(N))**2 # 0 to 15 point radii
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plt.scatter(x, y, s=area, c=colors, alpha=0.5)
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task.logger.report_matplotlib_figure(
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title="Manual Reporting",
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series="Just a plot",
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iteration=0,
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figure=plt,
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)
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# Show the plot
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plt.show()
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# Create plot and explicitly report as an image
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plt.scatter(x, y, s=area, c=colors, alpha=0.5)
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task.logger.report_matplotlib_figure(
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title="Manual Reporting",
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series="Plot as an image",
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iteration=0,
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figure=plt,
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report_image=True,
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)
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# Create an image plot and explicitly report (as an image)
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m = np.eye(256, 256, dtype=np.uint8)
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plt.imshow(m)
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task.logger.report_matplotlib_figure(
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title="Manual Reporting",
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series="Image plot",
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iteration=0,
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figure=plt,
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report_image=True, # Note this is required for image plots
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)
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# Show the plot
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plt.show()
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