def pcolormesh(*args, **kwargs):
"""
Use for large datasets
Non-traditional `pcolormesh` kwargs are:
- xticklabels, which will put x tick labels exactly in the center of the
heatmap block
- yticklables, which will put y tick labels exactly aligned in the center
of the heatmap block
- xticklabels_rotation, which can be either 'horizontal' or 'vertical'
depending on how you want the xticklabels rotated. The default is
'horizontal', but if you have xticklabels that are longer, you may want
to do 'vertical' so they don't overlap.
- yticklabels_rotation, which can also be either 'horizontal' or
'vertical'. The default is 'horizontal' and in most cases,
that's what you'll want to stick with. But the option is there if you
want.
- center_value, which will be the centered value for a divergent
colormap, for example if you have data above and below zero, but you want
the white part of the colormap to be equal to 10 rather than 0,
then specify 'center_value=10'.
"""
# Deal with arguments in kwargs that should be there, or need to be taken
# out
fig, ax, args, kwargs = maybe_get_fig_ax(*args, **kwargs)
x = args[0]
if 'vmax' not in kwargs:
kwargs['vmax'] = x.max()
if 'vmin' not in kwargs:
kwargs['vmin'] = x.min()
center_value = kwargs.pop('center_value', 0)
# If
divergent_data = False
if kwargs['vmax'] > 0 and kwargs['vmin'] < 0:
divergent_data = True
kwargs['vmax'] += center_value
kwargs['vmin'] += center_value
# If we have both negative and positive values, use a divergent colormap
if 'cmap' not in kwargs:
# Check if this is divergent
if divergent_data:
kwargs['cmap'] = blue_red
elif kwargs['vmax'] <= 0:
kwargs['cmap'] = blues_r
elif kwargs['vmax'] > 0:
kwargs['cmap'] = reds
if 'xticklabels' in kwargs:
xticklabels = kwargs['xticklabels']
kwargs.pop('xticklabels')
else:
xticklabels = None
if 'yticklabels' in kwargs:
yticklabels = kwargs['yticklabels']
kwargs.pop('yticklabels')
else:
yticklabels = None
if 'xticklabels_rotation' in kwargs:
xticklabels_rotation = kwargs['xticklabels_rotation']
kwargs.pop('xticklabels_rotation')
else:
xticklabels_rotation = 'horizontal'
if 'yticklabels_rotation' in kwargs:
yticklabels_rotation = kwargs['yticklabels_rotation']
kwargs.pop('yticklabels_rotation')
else:
yticklabels_rotation = 'horizontal'
ax_colorbar = kwargs.pop('ax_colorbar', None)
orientation_colorbar = kwargs.pop('orientation_colorbar', 'vertical')
p = ax.pcolormesh(*args, **kwargs)
ax.set_ylim(0, x.shape[0])
# Get rid of ALL axes
remove_chartjunk(ax, ['top', 'right', 'left', 'bottom'])
if xticklabels:
xticks = np.arange(0.5, x.shape[1] + 0.5)
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels, rotation=xticklabels_rotation)
if yticklabels:
yticks = np.arange(0.5, x.shape[0] + 0.5)
ax.set_yticks(yticks)
ax.set_yticklabels(yticklabels, rotation=yticklabels_rotation)
# Show the scale of the colorbar
fig.colorbar(p, cax=ax_colorbar, use_gridspec=True,
orientation=orientation_colorbar)
return fig
def pcolormesh(*args, **kwargs):
"""
Use for large datasets
Non-traditional `pcolormesh` kwargs are:
- xticklabels, which will put x tick labels exactly in the center of the
heatmap block
- yticklables, which will put y tick labels exactly aligned in the center
of the heatmap block
- xticklabels_rotation, which can be either 'horizontal' or 'vertical'
depending on how you want the xticklabels rotated. The default is
'horizontal', but if you have xticklabels that are longer, you may want
to do 'vertical' so they don't overlap.
- yticklabels_rotation, which can also be either 'horizontal' or
'vertical'. The default is 'horizontal' and in most cases,
that's what you'll want to stick with. But the option is there if you
want.
- center_value, which will be the centered value for a divergent
colormap, for example if you have data above and below zero, but you want
the white part of the colormap to be equal to 10 rather than 0,
then specify 'center_value=10'.
"""
# Deal with arguments in kwargs that should be there, or need to be taken
# out
fig, ax, args, kwargs = maybe_get_fig_ax(*args, **kwargs)
# If x and y axis are passed in arguments, gets correct data
# Ticks will work with x and y data, although it would be pointless to use
# both x/y and custom ticks
if len(args) == 3:
x = args[0]
y = args[1]
data = args[2]
elif len(args) == 1:
data = args[0]
kwargs.setdefault('vmax', data.max())
kwargs.setdefault('vmin', data.min())
center_value = kwargs.pop('center_value', 0)
# If
divergent_data = False
if kwargs['vmax'] > 0 and kwargs['vmin'] < 0:
divergent_data = True
kwargs['vmax'] += center_value
kwargs['vmin'] += center_value
# If we have both negative and positive values, use a divergent colormap
if 'cmap' not in kwargs:
# Check if this is divergent
if divergent_data:
kwargs['cmap'] = blue_red
elif kwargs['vmax'] <= 0:
kwargs['cmap'] = blues_r
elif kwargs['vmax'] > 0:
kwargs['cmap'] = reds
if 'xticklabels' in kwargs:
xticklabels = kwargs['xticklabels']
kwargs.pop('xticklabels')
else:
xticklabels = None
if 'yticklabels' in kwargs:
yticklabels = kwargs['yticklabels']
kwargs.pop('yticklabels')
else:
yticklabels = None
if 'xticklabels_rotation' in kwargs:
xticklabels_rotation = kwargs['xticklabels_rotation']
kwargs.pop('xticklabels_rotation')
else:
xticklabels_rotation = 'horizontal'
if 'yticklabels_rotation' in kwargs:
yticklabels_rotation = kwargs['yticklabels_rotation']
kwargs.pop('yticklabels_rotation')
else:
yticklabels_rotation = 'horizontal'
ax_colorbar = kwargs.pop('ax_colorbar', None)
orientation_colorbar = kwargs.pop('orientation_colorbar', 'vertical')
p = ax.pcolormesh(*args, **kwargs)
# ax.set_ylim(0, x.shape[0])
# ax.set_xlim(0, x.shape[1])
# Get rid of ALL axes
remove_chartjunk(ax, ['top', 'right', 'left', 'bottom'])
if xticklabels is not None and any(xticklabels):
if len(args) == 1:
xticks = np.arange(0.5, data.shape[1] + 0.5)
else:
xticks = []
for i in np.arange(len(x) - 1):
half = float(x[i + 1] - x[i]) / 2. + x[i]
xticks.append(half)
xticks = np.array(xticks)
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels, rotation=xticklabels_rotation)
if yticklabels is not None and any(yticklabels):
if len(args) == 1:
yticks = np.arange(0.5, data.shape[1] + 0.5)
else:
yticks = []
for i in np.arange(len(y) - 1):
half = float(y[i + 1] - y[i]) / 2. + y[i]
yticks.append(half)
yticks = np.array(yticks)
ax.set_yticks(yticks)
ax.set_yticklabels(yticklabels, rotation=yticklabels_rotation)
# Show the scale of the colorbar
cbar = fig.colorbar(p, cax=ax_colorbar, use_gridspec=True,
orientation=orientation_colorbar)
return p, cbar
def pcolormesh(*args, **kwargs):
"""
Use for large datasets
Non-traditional `pcolormesh` kwargs are:
- xticklabels, which will put x tick labels exactly in the center of the
heatmap block
- yticklables, which will put y tick labels exactly aligned in the center
of the heatmap block
- xticklabels_rotation, which can be either 'horizontal' or 'vertical'
depending on how you want the xticklabels rotated. The default is
'horizontal', but if you have xticklabels that are longer, you may want
to do 'vertical' so they don't overlap.
- yticklabels_rotation, which can also be either 'horizontal' or
'vertical'. The default is 'horizontal' and in most cases,
that's what you'll want to stick with. But the option is there if you
want.
- center_value, which will be the centered value for a divergent
colormap, for example if you have data above and below zero, but you want
the white part of the colormap to be equal to 10 rather than 0,
then specify 'center_value=10'.
"""
# Deal with arguments in kwargs that should be there, or need to be taken
# out
fig, ax, args, kwargs = maybe_get_fig_ax(*args, **kwargs)
# If x and y axis are passed in arguments, gets correct data
# Ticks will work with x and y data, although it would be pointless to use
# both x/y and custom ticks
if len(args) == 3:
x = args[0]
y = args[1]
data = args[2]
elif len(args) == 1:
data = args[0]
kwargs.setdefault('vmax', data.max())
kwargs.setdefault('vmin', data.min())
center_value = kwargs.pop('center_value', 0)
# If
divergent_data = False
if kwargs['vmax'] > 0 and kwargs['vmin'] < 0:
divergent_data = True
kwargs['vmax'] += center_value
kwargs['vmin'] += center_value
# If we have both negative and positive values, use a divergent colormap
if 'cmap' not in kwargs:
# Check if this is divergent
if divergent_data:
kwargs['cmap'] = blue_red
elif kwargs['vmax'] <= 0:
kwargs['cmap'] = blues_r
elif kwargs['vmax'] > 0:
kwargs['cmap'] = reds
if 'xticklabels' in kwargs:
xticklabels = kwargs['xticklabels']
kwargs.pop('xticklabels')
else:
xticklabels = None
if 'yticklabels' in kwargs:
yticklabels = kwargs['yticklabels']
kwargs.pop('yticklabels')
else:
yticklabels = None
if 'xticklabels_rotation' in kwargs:
xticklabels_rotation = kwargs['xticklabels_rotation']
kwargs.pop('xticklabels_rotation')
else:
xticklabels_rotation = 'horizontal'
if 'yticklabels_rotation' in kwargs:
yticklabels_rotation = kwargs['yticklabels_rotation']
kwargs.pop('yticklabels_rotation')
else:
yticklabels_rotation = 'horizontal'
ax_colorbar = kwargs.pop('ax_colorbar', None)
orientation_colorbar = kwargs.pop('orientation_colorbar', 'vertical')
p = ax.pcolormesh(*args, **kwargs)
# ax.set_ylim(0, x.shape[0])
# ax.set_xlim(0, x.shape[1])
# Get rid of ALL axes
remove_chartjunk(ax, ['top', 'right', 'left', 'bottom'])
if xticklabels is not None and any(xticklabels):
if len(args) == 1:
xticks = np.arange(1, data.shape[1] + 1)
else:
xticks = []
for i in np.arange(len(x) - 1):
half = float(x[i + 1] - x[i]) / 2. + x[i]
xticks.append(half)
xticks = np.array(xticks)
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels, rotation=xticklabels_rotation)
if yticklabels is not None and any(yticklabels):
if len(args) == 1:
yticks = np.arange(1, data.shape[2] + 1)
else:
yticks = []
for i in np.arange(len(y) - 1):
half = float(y[i + 1] - y[i]) / 2. + y[i]
yticks.append(half)
yticks = np.array(yticks)
ax.set_yticks(yticks)
ax.set_yticklabels(yticklabels, rotation=yticklabels_rotation)
# Show the scale of the colorbar
cbar = fig.colorbar(p,
cax=ax_colorbar,
use_gridspec=True,
orientation=orientation_colorbar)
return p, cbar
def pcolormesh(*args, **kwargs):
"""
Use for large datasets
Non-traditional `pcolormesh` kwargs are:
- xticklabels, which will put x tick labels exactly in the center of the
heatmap block
- yticklables, which will put y tick labels exactly aligned in the center
of the heatmap block
- xticklabels_rotation, which can be either 'horizontal' or 'vertical'
depending on how you want the xticklabels rotated. The default is
'horizontal', but if you have xticklabels that are longer, you may want
to do 'vertical' so they don't overlap.
- yticklabels_rotation, which can also be either 'horizontal' or
'vertical'. The default is 'horizontal' and in most cases,
that's what you'll want to stick with. But the option is there if you
want.
- center_value, which will be the centered value for a divergent
colormap, for example if you have data above and below zero, but you want
the white part of the colormap to be equal to 10 rather than 0,
then specify 'center_value=10'.
"""
# Deal with arguments in kwargs that should be there, or need to be taken
# out
fig, ax, args, kwargs = maybe_get_fig_ax(*args, **kwargs)
x = args[0]
kwargs.setdefault('vmax', x.max())
kwargs.setdefault('vmin', x.min())
center_value = kwargs.pop('center_value', 0)
# If
divergent_data = False
if kwargs['vmax'] > 0 and kwargs['vmin'] < 0:
divergent_data = True
kwargs['vmax'] += center_value
kwargs['vmin'] += center_value
# If we have both negative and positive values, use a divergent colormap
if 'cmap' not in kwargs:
# Check if this is divergent
if divergent_data:
kwargs['cmap'] = blue_red
elif kwargs['vmax'] <= 0:
kwargs['cmap'] = blues_r
elif kwargs['vmax'] > 0:
kwargs['cmap'] = reds
if 'xticklabels' in kwargs:
xticklabels = kwargs['xticklabels']
kwargs.pop('xticklabels')
else:
xticklabels = None
if 'yticklabels' in kwargs:
yticklabels = kwargs['yticklabels']
kwargs.pop('yticklabels')
else:
yticklabels = None
if 'xticklabels_rotation' in kwargs:
xticklabels_rotation = kwargs['xticklabels_rotation']
kwargs.pop('xticklabels_rotation')
else:
xticklabels_rotation = 'horizontal'
if 'yticklabels_rotation' in kwargs:
yticklabels_rotation = kwargs['yticklabels_rotation']
kwargs.pop('yticklabels_rotation')
else:
yticklabels_rotation = 'horizontal'
ax_colorbar = kwargs.pop('ax_colorbar', None)
orientation_colorbar = kwargs.pop('orientation_colorbar', 'vertical')
p = ax.pcolormesh(*args, **kwargs)
ax.set_ylim(0, x.shape[0])
# Get rid of ALL axes
remove_chartjunk(ax, ['top', 'right', 'left', 'bottom'])
if any(xticklabels):
xticks = np.arange(0.5, x.shape[1] + 0.5)
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels, rotation=xticklabels_rotation)
if any(yticklabels):
yticks = np.arange(0.5, x.shape[0] + 0.5)
ax.set_yticks(yticks)
ax.set_yticklabels(yticklabels, rotation=yticklabels_rotation)
# Show the scale of the colorbar
fig.colorbar(p,
cax=ax_colorbar,
use_gridspec=True,
orientation=orientation_colorbar)
return fig