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2be3ea1cc7
* matplotlib-agg * fix * context manager * Update CHANGELOG.md * update demos * linting * removed warning * fix test * fixes * fix tests
69 lines
1.6 KiB
Python
69 lines
1.6 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import random
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import os
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def random_plot():
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start_year = 2020
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x = np.arange(start_year, start_year + random.randint(0, 10))
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year_count = x.shape[0]
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plt_format = "-"
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fig = plt.figure()
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ax = fig.add_subplot(111)
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series = np.arange(0, year_count, dtype=float)
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series = series**2
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series += np.random.rand(year_count)
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ax.plot(x, series, plt_format)
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return fig
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img_dir = os.path.join(os.path.dirname(__file__), "files")
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file_dir = os.path.join(os.path.dirname(__file__), "..", "kitchen_sink", "files")
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model3d_dir = os.path.join(os.path.dirname(__file__), "..", "model3D", "files")
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highlighted_text_output_1 = [
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{
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"entity": "I-LOC",
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"score": 0.9988978,
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"index": 2,
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"word": "Chicago",
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"start": 5,
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"end": 12,
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},
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{
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"entity": "I-MISC",
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"score": 0.9958592,
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"index": 5,
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"word": "Pakistani",
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"start": 22,
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"end": 31,
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},
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]
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highlighted_text_output_2 = [
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{
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"entity": "I-LOC",
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"score": 0.9988978,
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"index": 2,
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"word": "Chicago",
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"start": 5,
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"end": 12,
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},
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{
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"entity": "I-LOC",
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"score": 0.9958592,
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"index": 5,
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"word": "Pakistan",
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"start": 22,
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"end": 30,
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},
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]
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highlighted_text = "Does Chicago have any Pakistani restaurants"
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def random_model3d():
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model_3d = random.choice(
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[os.path.join(model3d_dir, model) for model in os.listdir(model3d_dir) if model != "source.txt"]
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)
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return model_3d
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