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examples/16_network/pet_group_anim.py

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Network group process
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=====================
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"""
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# sphinx_gallery_thumbnail_number = 2
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import re
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from datetime import datetime
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@@ -147,6 +146,8 @@ def update(frame):
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# %%
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# Final Result
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# -----------
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# sphinx_gallery_thumbnail_number = 2
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fig = plt.figure(figsize=(16, 9))
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ax = fig.add_axes([0, 0, 1, 1])
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ax.set_aspect("equal"), ax.grid(), ax.set_xlim(26, 34), ax.set_ylim(31, 35.5)

examples/16_network/pet_segmentation_anim.py

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@@ -120,6 +120,8 @@ def update(i_frame):
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# %%
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# Final Result
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# -----------
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# sphinx_gallery_thumbnail_number = 2
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fig = plt.figure(figsize=(16, 9))
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ax = fig.add_axes([0.04, 0.06, 0.94, 0.88], projection="full_axes")
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ax.set_xlim(19, 29), ax.set_ylim(31, 35.5), ax.grid()

notebooks/python_module/16_network/pet_group_anim.ipynb

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},
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"outputs": [],
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"source": [
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"# sphinx_gallery_thumbnail_number = 2\nimport re\nfrom datetime import datetime\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib.animation import FuncAnimation\nfrom matplotlib.colors import ListedColormap\nfrom numba import njit\nfrom numpy import arange, array, empty, ones\n\nfrom py_eddy_tracker import data\nfrom py_eddy_tracker.generic import flatten_line_matrix\nfrom py_eddy_tracker.observations.network import Network\nfrom py_eddy_tracker.observations.observation import EddiesObservations"
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"import re\nfrom datetime import datetime\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib.animation import FuncAnimation\nfrom matplotlib.colors import ListedColormap\nfrom numba import njit\nfrom numpy import arange, array, empty, ones\n\nfrom py_eddy_tracker import data\nfrom py_eddy_tracker.generic import flatten_line_matrix\nfrom py_eddy_tracker.observations.network import Network\nfrom py_eddy_tracker.observations.observation import EddiesObservations"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"fig = plt.figure(figsize=(16, 9))\nax = fig.add_axes([0, 0, 1, 1])\nax.set_aspect(\"equal\"), ax.grid(), ax.set_xlim(26, 34), ax.set_ylim(31, 35.5)\n_ = ax.scatter(e.lon, e.lat, c=NETWORK_GROUPS[-1][2], **kw_s)"
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"# sphinx_gallery_thumbnail_number = 2\nfig = plt.figure(figsize=(16, 9))\nax = fig.add_axes([0, 0, 1, 1])\nax.set_aspect(\"equal\"), ax.grid(), ax.set_xlim(26, 34), ax.set_ylim(31, 35.5)\n_ = ax.scatter(e.lon, e.lat, c=NETWORK_GROUPS[-1][2], **kw_s)"
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]
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}
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],

notebooks/python_module/16_network/pet_segmentation_anim.ipynb

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},
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"outputs": [],
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"source": [
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"fig = plt.figure(figsize=(16, 9))\nax = fig.add_axes([0.04, 0.06, 0.94, 0.88], projection=\"full_axes\")\nax.set_xlim(19, 29), ax.set_ylim(31, 35.5), ax.grid()\n_ = ax.scatter(e.lon, e.lat, c=TRACKS[-1], cmap=cmap, vmin=0, vmax=vmax, s=20)"
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"# sphinx_gallery_thumbnail_number = 2\nfig = plt.figure(figsize=(16, 9))\nax = fig.add_axes([0.04, 0.06, 0.94, 0.88], projection=\"full_axes\")\nax.set_xlim(19, 29), ax.set_ylim(31, 35.5), ax.grid()\n_ = ax.scatter(e.lon, e.lat, c=TRACKS[-1], cmap=cmap, vmin=0, vmax=vmax, s=20)"
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]
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}
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],

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