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Convert docstring plot directives to doctests for plot.py #175

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Dec 9, 2024
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Convert docstring plot directives to doctests for plot.py #175

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b1ce9eb
remove code, doctest directives from plot module
mcflugen Dec 6, 2024
face15b
run doctests in plot.py
mcflugen Dec 9, 2024
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295 changes: 146 additions & 149 deletions deltametrics/plot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,35 +37,30 @@ class VariableInfo(object):
for a variable ``vegetation_density`` as a range of green shades we
would create the VariableInfo as:

.. doctest::
>>> import matplotlib
>>> from deltametrics.plot import VariableInfo

>>> from deltametrics.plot import VariableInfo
>>> veg = VariableInfo('vegetation_density',
... cmap='Greens')
>>> veg = VariableInfo('vegetation_density',
... cmap='Greens')

>>> veg.cmap
<matplotlib.colors.LinearSegmentedColormap object at 0x...>
>>> veg.cmap
<matplotlib.colors.LinearSegmentedColormap object at 0x...>

>>> veg.cmap.N
64
>>> veg.cmap.N
64

which creates a 64 interval colormap from the matplotlib `Greens`
colormap. If instead we wanted just three colors, we could specify the
colormap manually:

.. doctest::

>>> veg3 = VariableInfo('vegetation_density',
... cmap=matplotlib.colormaps['Greens'].resampled(3))
>>> veg3.cmap.N
3
>>> veg3 = VariableInfo('vegetation_density',
... cmap=matplotlib.colormaps['Greens'].resampled(3))
>>> veg3.cmap.N
3

We can then set a more human-readable label for the variable:

.. doctest::

>>> veg3.label = 'vegetation density'

>>> veg3.label = 'vegetation density'
"""

def __init__(self, name, **kwargs):
Expand Down Expand Up @@ -190,10 +185,10 @@ class VariableSet(object):

Create a default variable set object.

.. doctest::
>>> from deltametrics.plot import VariableSet

>>> dm.plot.VariableSet()
<deltametrics.plot.VariableSet object at 0x...>
>>> VariableSet()
<deltametrics.plot.VariableSet object at 0x...>

"""

Expand Down Expand Up @@ -559,23 +554,24 @@ def cartographic_colormap(H_SL=0.0, h=4.5, n=1.0):
To display with default depth and relief parameters (left) and with adjust
parameters to highlight depth variability (right):

.. plot::
:include-source:
>>> import matplotlib.pyplot as plt
>>> from deltametrics.plot import cartographic_colormap
>>> from deltametrics.plot import append_colorbar
>>> from deltametrics.sample_data.sample_data import golf

golfcube = dm.sample_data.golf()
>>> golfcube = golf()

cmap0, norm0 = dm.plot.cartographic_colormap(H_SL=0)
cmap1, norm1 = dm.plot.cartographic_colormap(H_SL=0, h=5, n=0.5)
>>> cmap0, norm0 = cartographic_colormap(H_SL=0)
>>> cmap1, norm1 = cartographic_colormap(H_SL=0, h=5, n=0.5)

fig, ax = plt.subplots(1, 2, figsize=(10, 4))
im0 = ax[0].imshow(golfcube['eta'][-1, ...], origin='lower',
cmap=cmap0, norm=norm0)
cb0 = dm.plot.append_colorbar(im0, ax[0])
im1 = ax[1].imshow(golfcube['eta'][-1, ...], origin='lower',
cmap=cmap1, norm=norm1)
cb1 = dm.plot.append_colorbar(im1, ax[1])
plt.tight_layout()
plt.show()
>>> fig, ax = plt.subplots(1, 2, figsize=(10, 4))
>>> im0 = ax[0].imshow(golfcube['eta'][-1, ...], origin='lower',
... cmap=cmap0, norm=norm0)
>>> cb0 = append_colorbar(im0, ax[0])
>>> im1 = ax[1].imshow(golfcube['eta'][-1, ...], origin='lower',
... cmap=cmap1, norm=norm1)
>>> cb1 = append_colorbar(im1, ax[1])
>>> plt.tight_layout()
"""
blues = mpl.colormaps["Blues_r"].resampled(64)
greens = mpl.colormaps["YlGn_r"].resampled(64)
Expand Down Expand Up @@ -625,49 +621,47 @@ def vintage_colormap(H_SL=0.0, h=4.5, n=1.0):
To display with default depth and relief parameters (left) and with adjust
parameters to highlight depth variability (right):

.. plot::
:include-source:
>>> import matplotlib.pyplot as plt
>>> from deltametrics.plot import append_colorbar
>>> from deltametrics.plot import vintage_colormap
>>> from deltametrics.sample_data.sample_data import golf

golfcube = dm.sample_data.golf()
>>> golfcube = golf()

cmap0, norm0 = dm.plot.vintage_colormap(H_SL=0)
cmap1, norm1 = dm.plot.vintage_colormap(H_SL=0, h=3, n=0.25)
>>> cmap0, norm0 = vintage_colormap(H_SL=0)
>>> cmap1, norm1 = vintage_colormap(H_SL=0, h=3, n=0.25)

fig, ax = plt.subplots(1, 2, figsize=(10, 4))
im0 = ax[0].imshow(golfcube['eta'][-1, ...], origin='lower',
cmap=cmap0, norm=norm0)
cb0 = dm.plot.append_colorbar(im0, ax[0])
im1 = ax[1].imshow(golfcube['eta'][-1, ...], origin='lower',
cmap=cmap1, norm=norm1)
cb1 = dm.plot.append_colorbar(im1, ax[1])
plt.tight_layout()
plt.show()
>>> fig, ax = plt.subplots(1, 2, figsize=(10, 4))
>>> im0 = ax[0].imshow(golfcube['eta'][-1, ...], origin='lower',
... cmap=cmap0, norm=norm0)
>>> cb0 = append_colorbar(im0, ax[0])
>>> im1 = ax[1].imshow(golfcube['eta'][-1, ...], origin='lower',
... cmap=cmap1, norm=norm1)
>>> cb1 = append_colorbar(im1, ax[1])
>>> plt.tight_layout()

To use the colormap exactly as described in Pearson's original publication, use parameters

.. code::

cmap, norm = dm.plot.vintage_colormap(H_SL=0, h=20, n=10)
cmap, norm = vintage_colormap(H_SL=0, h=20, n=10)

.. plot::
>>> import numpy as np

X, Y = np.meshgrid(np.linspace(-30, 10), np.arange(10))

cmapd, normd = dm.plot.vintage_colormap(H_SL=0, h=20, n=10)

fig, ax = plt.subplots(
figsize=(4, 2),
gridspec_kw=dict(
left=0.07, right=0.85, bottom=0.25))
imd = ax.imshow(
X, extent=(-30, 10, 0, 20),
cmap=cmapd, norm=normd)
cbd = dm.plot.append_colorbar(imd, ax)
ax.set_yticks([])
ax.set_xlabel('elevation [m]')

plt.show()
>>> X, Y = np.meshgrid(np.linspace(-30, 10), np.arange(10))

>>> cmapd, normd = vintage_colormap(H_SL=0, h=20, n=10)

>>> fig, ax = plt.subplots(
... figsize=(4, 2),
... gridspec_kw=dict(
... left=0.07, right=0.85, bottom=0.25))
>>> imd = ax.imshow(
... X, extent=(-30, 10, 0, 20),
... cmap=cmapd, norm=normd)
>>> cbd = append_colorbar(imd, ax)
>>> _ = ax.set_yticks([])
>>> _ = ax.set_xlabel('elevation [m]')
"""
# In implementation, we differ from Pearson's version. Pearson's version
# takes in `vertSpacing`, a 10-column array with elevations of each
Expand Down Expand Up @@ -772,25 +766,24 @@ def style_axes_km(*args):
Examples
--------

.. plot::
:include-source:
:context: reset
>>> import matplotlib.pyplot as plt
>>> from deltametrics.plot import style_axes_km
>>> from deltametrics.sample_data.sample_data import golf

golf = dm.sample_data.golf()
>>> golf = golf()

fig, ax = plt.subplots(
3, 1,
gridspec_kw=dict(hspace=0.5))
golf.quick_show('eta', ax=ax[0], ticks=True)
>>> fig, ax = plt.subplots(
... 3, 1,
... gridspec_kw=dict(hspace=0.5))
>>> golf.quick_show('eta', ax=ax[0], ticks=True)

golf.quick_show('eta', ax=ax[1], ticks=True)
dm.plot.style_axes_km(ax[1])
>>> golf.quick_show('eta', ax=ax[1], ticks=True)
>>> style_axes_km(ax[1])

golf.quick_show('eta', axis=1, idx=10, ax=ax[2])
ax[2].xaxis.set_major_formatter(dm.plot.style_axes_km)
# OR use: dm.plot.style_axes_km(ax[2], 'x')
>>> golf.quick_show('eta', axis=1, idx=10, ax=ax[2])
>>> ax[2].xaxis.set_major_formatter(style_axes_km)

plt.show()
OR use: style_axes_km(ax[2], 'x')
"""
if isinstance(args[0], mpl.axes.Axes):
ax = args[0]
Expand Down Expand Up @@ -1290,21 +1283,26 @@ def _scale_lightness(rgb, scale_l):

.. plot::

fig, ax = plt.subplots(figsize=(5, 2))
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from deltametrics.plot import _scale_lightness

>>> fig, ax = plt.subplots(figsize=(5, 2))

# initial color red
red = (1.0, 0.0, 0.0)
ax.plot(-1, 1, 'o', color=red)
Initial color red

# scale from 1 to 0.05
scales = np.arange(1, 0, -0.05)
>>> red = (1.0, 0.0, 0.0)
>>> _ = ax.plot(-1, 1, 'o', color=red)

# loop through scales and plot
for s, scale in enumerate(scales):
darker_red = dm.plot._scale_lightness(red, scale)
ax.plot(s, scale, 'o', color=darker_red)
Scale from 1 to 0.05

plt.show()
>>> scales = np.arange(1, 0, -0.05)

Loop through scales and plot

>>> for s, scale in enumerate(scales):
... darker_red = _scale_lightness(red, scale)
... _ = ax.plot(s, scale, 'o', color=darker_red)
"""
# https://stackoverflow.com/a/60562502/4038393
# convert rgb to hls
Expand Down Expand Up @@ -1345,19 +1343,21 @@ def show_histograms(*args, sets=None, ax=None, **kwargs):
--------

.. plot::
:include-source:

locs = [0.25, 1, 0.5, 4, 2]
scales = [0.1, 0.25, 0.4, 0.5, 0.1]
bins = np.linspace(0, 6, num=40)
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from deltametrics.plot import show_histograms

hist_bin_sets = [np.histogram(np.random.normal(l, s, size=500), bins=bins, density=True) for l, s in zip(locs, scales)]
>>> locs = [0.25, 1, 0.5, 4, 2]
>>> scales = [0.1, 0.25, 0.4, 0.5, 0.1]
>>> bins = np.linspace(0, 6, num=40)

fig, ax = plt.subplots()
dm.plot.show_histograms(*hist_bin_sets, sets=[0, 1, 0, 1, 2], ax=ax)
ax.set_xlim((0, 6))
ax.set_ylabel('density')
plt.show()
>>> hist_bin_sets = [np.histogram(np.random.normal(l, s, size=500), bins=bins, density=True) for l, s in zip(locs, scales)]

>>> fig, ax = plt.subplots()
>>> show_histograms(*hist_bin_sets, sets=[0, 1, 0, 1, 2], ax=ax)
>>> _ = ax.set_xlim((0, 6))
>>> _ = ax.set_ylabel('density')
"""
if not ax:
fig, ax = plt.subplots()
Expand Down Expand Up @@ -1443,15 +1443,16 @@ def aerial_view(
Examples
--------
.. plot::
:include-source:

golfcube = dm.sample_data.golf()
elevation_data = golfcube['eta'][-1, :, :]
>>> import matplotlib.pyplot as plt
>>> from deltametrics.plot import aerial_view
>>> from deltametrics.sample_data.sample_data import golf

fig, ax = plt.subplots()
dm.plot.aerial_view(elevation_data, ax=ax)
plt.show()
>>> golfcube = golf()
>>> elevation_data = golfcube['eta'][-1, :, :]

>>> fig, ax = plt.subplots()
>>> _ = aerial_view(elevation_data, ax=ax)
"""
if not ax:
fig, ax = plt.subplots()
Expand Down Expand Up @@ -1545,48 +1546,44 @@ def overlay_sparse_array(
of sparse data.

.. plot::
:include-source:
:context: reset

golfcube = dm.sample_data.golf()
elevation_data = golfcube['eta'][-1, :, :]
sparse_data = golfcube['discharge'][-1, ...]

fig, ax = plt.subplots(1, 3, figsize=(8, 3))
for axi in ax.ravel():
dm.plot.aerial_view(elevation_data, ax=axi)

dm.plot.overlay_sparse_array(
sparse_data, ax=ax[0]) # default clip is (None, 90)
dm.plot.overlay_sparse_array(
sparse_data, alpha_clip=(None, None), ax=ax[1])
dm.plot.overlay_sparse_array(
sparse_data, alpha_clip=(70, 90), ax=ax[2])

plt.tight_layout()
plt.show()

.. plot::
:include-source:
:context: close-figs

fig, ax = plt.subplots(1, 3, figsize=(8, 3))
for axi in ax.ravel():
dm.plot.aerial_view(elevation_data, ax=axi)

dm.plot.overlay_sparse_array(
sparse_data, ax=ax[0],
clip_type='value') # default clip is (None, 90)
dm.plot.overlay_sparse_array(
sparse_data, ax=ax[1],
alpha_clip=(None, 0.2), clip_type='value')
dm.plot.overlay_sparse_array(
sparse_data, ax=ax[2],
alpha_clip=(0.4, 0.6), clip_type='value')

plt.tight_layout()
plt.show()

>>> import matplotlib.pyplot as plt
>>> from deltametrics.plot import aerial_view
>>> from deltametrics.plot import overlay_sparse_array
>>> from deltametrics.sample_data.sample_data import golf

>>> golfcube = golf()
>>> elevation_data = golfcube['eta'][-1, :, :]
>>> sparse_data = golfcube['discharge'][-1, ...]

>>> fig, ax = plt.subplots(1, 3, figsize=(8, 3))
>>> for axi in ax.ravel():
... _ = aerial_view(elevation_data, ax=axi)

>>> _ = overlay_sparse_array(
... sparse_data, ax=ax[0]) # default clip is (None, 90)
>>> _ = overlay_sparse_array(
... sparse_data, alpha_clip=(None, None), ax=ax[1])
>>> _ = overlay_sparse_array(
... sparse_data, alpha_clip=(70, 90), ax=ax[2])

>>> plt.tight_layout()

>>> fig, ax = plt.subplots(1, 3, figsize=(8, 3))
>>> for axi in ax.ravel():
... _ = aerial_view(elevation_data, ax=axi)

>>> _ = overlay_sparse_array(
... sparse_data, ax=ax[0],
... clip_type='value') # default clip is (None, 90)
>>> _ = overlay_sparse_array(
... sparse_data, ax=ax[1],
... alpha_clip=(None, 0.2), clip_type='value')
>>> _ = overlay_sparse_array(
... sparse_data, ax=ax[2],
... alpha_clip=(0.4, 0.6), clip_type='value')

>>> plt.tight_layout()
"""
if not ax:
fig, ax = plt.subplots()
Expand Down
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