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Merge pull request #180 from mcflugen/mcflugen/plot-directives-to-doc…
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…tests-sample-data

Convert docstring plot directives to doctests for sample_data.py
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@mcflugen
mcflugen authored Dec 11, 2024
2 parents 9a2b2d8 + 28083e9 commit de772b2
Showing 1 changed file with 106 additions and 87 deletions.
193 changes: 106 additions & 87 deletions deltametrics/sample_data/sample_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,19 +57,22 @@ def golf():
.. plot::
golf = dm.sample_data.golf()
nt = 5
ts = np.linspace(0, golf['eta'].shape[0]-1, num=nt, dtype=int)
fig, ax = plt.subplots(1, nt, figsize=(12, 2))
for i, t in enumerate(ts):
ax[i].imshow(golf['eta'][t, :, :], vmin=-2, vmax=0.5)
ax[i].set_title('t = ' + str(t))
ax[i].axes.get_xaxis().set_ticks([])
ax[i].axes.get_yaxis().set_ticks([])
ax[0].set_ylabel('dim1 direction')
ax[0].set_xlabel('dim2 direction')
plt.show()
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from deltametrics.sample_data.sample_data import golf
>>> golf = golf()
>>> nt = 5
>>> ts = np.linspace(0, golf["eta"].shape[0] - 1, num=nt, dtype=int)
>>> fig, ax = plt.subplots(1, nt, figsize=(12, 2))
>>> for i, t in enumerate(ts):
... _ = ax[i].imshow(golf["eta"][t, :, :], vmin=-2, vmax=0.5)
... _ = ax[i].set_title(f"t = {t}")
... _ = ax[i].axes.get_xaxis().set_ticks([])
... _ = ax[i].axes.get_yaxis().set_ticks([])
>>> _ = ax[0].set_ylabel("dim1 direction")
>>> _ = ax[0].set_xlabel("dim2 direction")
"""
golf_path = _get_golf_path()
return DataCube(golf_path)
Expand Down Expand Up @@ -114,24 +117,26 @@ def xslope():
.. plot::
xslope0, xslope1 = dm.sample_data.xslope()
nt = 5
ts = np.linspace(0, xslope0['eta'].shape[0]-1, num=nt, dtype=int)
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from deltametrics.sample_data.sample_data import xslope
fig, ax = plt.subplots(2, nt, figsize=(12, 2))
for i, t in enumerate(ts):
ax[0, i].imshow(xslope0['eta'][t, :, :], vmin=-10, vmax=0.5)
ax[0, i].set_title('t = ' + str(t))
ax[1, i].imshow(xslope1['eta'][t, :, :], vmin=-10, vmax=0.5)
>>> xslope0, xslope1 = xslope()
>>> nt = 5
>>> ts = np.linspace(0, xslope0["eta"].shape[0] - 1, num=nt, dtype=int)
ax[1, 0].set_ylabel('dim1 direction')
ax[1, 0].set_xlabel('dim2 direction')
>>> fig, ax = plt.subplots(2, nt, figsize=(12, 2))
>>> for i, t in enumerate(ts):
... _ = ax[0, i].imshow(xslope0["eta"][t, :, :], vmin=-10, vmax=0.5)
... _ = ax[0, i].set_title(f"t = {t}")
... _ = ax[1, i].imshow(xslope1["eta"][t, :, :], vmin=-10, vmax=0.5)
for axi in ax.ravel():
axi.axes.get_xaxis().set_ticks([])
axi.axes.get_yaxis().set_ticks([])
>>> _ = ax[1, 0].set_ylabel("dim1 direction")
>>> _ = ax[1, 0].set_xlabel("dim2 direction")
plt.show()
>>> for axi in ax.ravel():
... _ = axi.axes.get_xaxis().set_ticks([])
... _ = axi.axes.get_yaxis().set_ticks([])
Parameters
----------
Expand Down Expand Up @@ -190,19 +195,22 @@ def aeolian():
.. plot::
aeolian = dm.sample_data.aeolian()
nt = 5
ts = np.linspace(0, aeolian['eta'].shape[0]-1, num=nt, dtype=int)
fig, ax = plt.subplots(1, nt, figsize=(8, 4))
for i, t in enumerate(ts):
ax[i].imshow(aeolian['eta'][t, :, :], vmin=-5, vmax=7)
ax[i].set_title('t = ' + str(t))
ax[i].axes.get_xaxis().set_ticks([])
ax[i].axes.get_yaxis().set_ticks([])
ax[0].set_ylabel('northing')
ax[0].set_xlabel('easting')
plt.show()
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> import deltametrics as dm
>>> aeolian = dm.sample_data.aeolian()
>>> nt = 5
>>> ts = np.linspace(0, aeolian["eta"].shape[0] - 1, num=nt, dtype=int)
>>> fig, ax = plt.subplots(1, nt, figsize=(8, 4))
>>> for i, t in enumerate(ts):
... _ = ax[i].imshow(aeolian["eta"][t, :, :], vmin=-5, vmax=7)
... _ = ax[i].set_title(f"t = {t}")
... _ = ax[i].axes.get_xaxis().set_ticks([])
... _ = ax[i].axes.get_yaxis().set_ticks([])
>>> _ = ax[0].set_ylabel("northing")
>>> _ = ax[0].set_xlabel("easting")
"""
aeolian_path = _get_aeolian_path()
return DataCube(aeolian_path)
Expand Down Expand Up @@ -232,22 +240,25 @@ def rcm8():
.. plot::
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
rcm8 = dm.sample_data.rcm8()
nt = 5
ts = np.linspace(0, rcm8['eta'].shape[0]-1, num=nt, dtype=int)
fig, ax = plt.subplots(1, nt, figsize=(12, 2))
for i, t in enumerate(ts):
ax[i].imshow(rcm8['eta'][t, :, :], vmin=-2, vmax=0.5)
ax[i].set_title('t = ' + str(t))
ax[i].axes.get_xaxis().set_ticks([])
ax[i].axes.get_yaxis().set_ticks([])
ax[0].set_ylabel('y-direction')
ax[0].set_xlabel('x-direction')
plt.show()
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> import warnings
>>> import deltametrics as dm
>>> with warnings.catch_warnings():
... warnings.simplefilter("ignore")
... rcm8 = dm.sample_data.rcm8()
>>> nt = 5
>>> ts = np.linspace(0, rcm8["eta"].shape[0] - 1, num=nt, dtype=int)
>>> fig, ax = plt.subplots(1, nt, figsize=(12, 2))
>>> for i, t in enumerate(ts):
... _ = ax[i].imshow(rcm8["eta"][t, :, :], vmin=-2, vmax=0.5)
... _ = ax[i].set_title(f"t = {t}")
... _ = ax[i].axes.get_xaxis().set_ticks([])
... _ = ax[i].axes.get_yaxis().set_ticks([])
>>> _ = ax[0].set_ylabel("y-direction")
>>> _ = ax[0].set_xlabel("x-direction")
"""
rcm8_path = _get_rcm8_path()
return DataCube(rcm8_path)
Expand All @@ -268,24 +279,30 @@ def landsat():
.. plot::
landsat = dm.sample_data.landsat()
nt = landsat.shape[0]
maxr = np.max(landsat['Red'][:])
maxg = np.max(landsat['Green'][:])
maxb = np.max(landsat['Blue'][:])
fig, ax = plt.subplots(1, nt, figsize=(12, 2))
for i in np.arange(nt):
_arr = np.dstack((landsat['Red'][i, :, :]/maxr,
landsat['Green'][i, :, :]/maxg,
landsat['Blue'][i, :, :]/maxb))
ax[i].imshow(_arr)
ax[i].set_title('year = ' + str(landsat.t[i]))
ax[i].axes.get_xaxis().set_ticks([])
ax[i].axes.get_yaxis().set_ticks([])
plt.show()
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> import deltametrics as dm
>>> landsat = dm.sample_data.landsat()
>>> nt = landsat.shape[0]
>>> maxr = np.max(landsat["Red"][:])
>>> maxg = np.max(landsat["Green"][:])
>>> maxb = np.max(landsat["Blue"][:])
>>> fig, ax = plt.subplots(1, nt, figsize=(12, 2))
>>> for i in np.arange(nt):
... _arr = np.dstack(
... (
... landsat["Red"][i, :, :] / maxr,
... landsat["Green"][i, :, :] / maxg,
... landsat["Blue"][i, :, :] / maxb,
... )
... )
... _ = ax[i].imshow(_arr)
... _ = ax[i].set_title(f"year = {landsat.t[i]}")
... _ = ax[i].axes.get_xaxis().set_ticks([])
... _ = ax[i].axes.get_yaxis().set_ticks([])
"""
landsat_path = _get_landsat_path()
return DataCube(landsat_path)
Expand Down Expand Up @@ -335,22 +352,24 @@ def savi2020():
.. plot::
img, scans = dm.sample_data.savi2020()
nt = 5
ts_i = np.linspace(0, img['red'].shape[0]-1, num=nt, dtype=int)
ts_s = np.linspace(0, scans['eta'].shape[0]-1, num=nt, dtype=int)
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> import deltametrics as dm
fig, ax = plt.subplots(2, nt, figsize=(9, 6))
for i in range(nt):
ax[0, i].imshow(img['red'][ts_i[i], :, :], vmin=0, vmax=1)
ax[0, i].set_title('t = ' + str(ts_i[i]))
ax[1, i].imshow(scans['eta'][ts_s[i], :, :])
ax[1, i].set_title('t = ' + str(ts_s[i]))
>>> img, scans = dm.sample_data.savi2020()
>>> nt = 5
>>> ts_i = np.linspace(0, img["red"].shape[0] - 1, num=nt, dtype=int)
>>> ts_s = np.linspace(0, scans["eta"].shape[0] - 1, num=nt, dtype=int)
ax[1, 0].set_ylabel('dim1 direction')
ax[1, 0].set_xlabel('dim2 direction')
>>> fig, ax = plt.subplots(2, nt, figsize=(9, 6))
>>> for i in range(nt):
... _ = ax[0, i].imshow(img["red"][ts_i[i], :, :], vmin=0, vmax=1)
... _ = ax[0, i].set_title(f"t = {ts_i[i]}")
... _ = ax[1, i].imshow(scans["eta"][ts_s[i], :, :])
... _ = ax[1, i].set_title(f"t = {ts_s[i]}")
plt.show()
>>> _ = ax[1, 0].set_ylabel("dim1 direction")
>>> _ = ax[1, 0].set_xlabel("dim2 direction")
Parameters
----------
Expand Down

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