Add manual seaborn logging example (#628)

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pollfly 2022-04-02 13:37:07 +03:00 committed by GitHub
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@ -2,15 +2,16 @@
#
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from clearml import Task
# Connecting ClearML with the current process,
# from here on everything is logged automatically
# Create a new task, disable automatic matplotlib connect
task = Task.init(
project_name='examples',
task_name='Manual Matplotlib example',
auto_connect_frameworks={'matplotlib': False}
project_name="examples",
task_name="Manual Matplotlib example",
auto_connect_frameworks={"matplotlib": False},
)
# Create plot and explicitly report as figure
@ -21,10 +22,7 @@ colors = np.random.rand(N)
area = (30 * np.random.rand(N))**2 # 0 to 15 point radii
plt.scatter(x, y, s=area, c=colors, alpha=0.5)
task.logger.report_matplotlib_figure(
title="Manual Reporting",
series="Just a plot",
iteration=0,
figure=plt,
title="Manual Reporting", series="Just a plot", iteration=0, figure=plt
)
# Show the plot
@ -39,19 +37,38 @@ task.logger.report_matplotlib_figure(
figure=plt,
report_image=True,
)
# Show the plot
plt.show()
# Create an image plot and explicitly report (as an image)
# Create image plot
m = np.eye(256, 256, dtype=np.uint8)
plt.imshow(m)
# Report plot
task.logger.report_matplotlib_figure(
title="Manual Reporting",
series="Image plot",
iteration=0,
figure=plt,
report_image=True, # Note this is required for image plots
report_interactive=False,
)
# Show the plot
# Show plot
plt.show()
# Create Seaborn plot
sns.set(style="darkgrid")
# Load an example dataset with long-form data
fmri = sns.load_dataset("fmri")
# Plot the responses for different events and regions
sns.lineplot(x="timepoint", y="signal", hue="region", style="event", data=fmri)
# Report plot
task.logger.report_matplotlib_figure(
title="Seaborn example",
series="My Plot Series 4",
iteration=10,
figure=plt,
report_interactive=False,
)
# Show plot
plt.show()
print("This is a Matplotlib & Seaborn example")