Module wsp_tools.pyplotwrapper
A wrapper for matplotlib.pyplot containing common plotting routines.
Example
import numpy as np
import wsp_tools as wt
X = np.linspace(-10,10,100)
Y = np.linspace(-10,10,100)
x, y = np.meshgrid(X, Y)
data = x + 1j*y
window = (-4, 4, -6, 6)
fig, ax = wt.subplots(12)
ax1, ax2 = ax[0,0], ax[0,1]
ax1.setAxes(X, Y)
ax1.imshow(np.abs(data))
ax1.inset(window=window)
ax2.setAxes(X, Y, window=window)
ax2.rgba(data)
wt.plt.show()
Expand source code
# wsp-tools is TEM data analysis and simulation tools developed by WSP as a grad student in the McMorran Lab.
# Copyright (C) 2021 William S. Parker
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
"""A wrapper for matplotlib.pyplot containing common plotting routines.
Example
```python
import numpy as np
import wsp_tools as wt
X = np.linspace(-10,10,100)
Y = np.linspace(-10,10,100)
x, y = np.meshgrid(X, Y)
data = x + 1j*y
window = (-4, 4, -6, 6)
fig, ax = wt.subplots(12)
ax1, ax2 = ax[0,0], ax[0,1]
ax1.setAxes(X, Y)
ax1.imshow(np.abs(data))
ax1.inset(window=window)
ax2.setAxes(X, Y, window=window)
ax2.rgba(data)
wt.plt.show()
```
"""
import matplotlib.pyplot as plt
import numpy as np
from .cielab import rgba, cielab_cmap
__all__ = ['singleAx','subplots']
class singleAx():
"""An extension of the `matplotlib.axes.Axes` class.
This class adds macros for 2d plotting that I commonly use. In particular,
it's easy to select only a window of your data to show, to add x-y axes,
to add an inset, and to show the rgba version of a complex 2d array.
Typical usage:
```python
X = np.linspace(-10,10,xres)
Y = np.linspace(-10,10,yres)
x, y = np.meshgrid(X, Y)
data = x+1j*y
window = [-3,7,1,4]
fig, ax = plt.subplots()
myax = wsp_tools.singleAx(ax)
ax.set_axes(x, y, window)
ax.set_xytitle('x','y','title')
ax.rgba(data)
plt.show()
```
More commonly, this class is returned by ```wsp_tools.pyplotwrapper.subplots```.
**Parameters**
* **ax** : _matplotlib.axes.Axes_ <br />
"""
def __init__(self, ax):
self.ax = ax
self.origin = 'lower'
self.x = None
self.y = None
self.xmin = None
self.ymin = None
self.xmax = None
self.ymax = None
def set_title(self, title='', **kwargs):
"""Sets the title of the plot.
**Parameters**
* **title** : _string_ <br />
The plot title.
* ****kwargs** <br />
All other kwargs are passed on to `matplotlib.axes.Axes.set_title`.
**Returns**
* **self** : _singleAx_
"""
self.ax.set_title(title, **kwargs)
return(self)
def set_xlabel(self, xlabel='', **kwargs):
"""Sets the xlabel of the plot.
**Parameters*
* **xlabel** : _string_ <br />
The xlabel.
* ****kwargs** <br />
All other kwargs are passed on to `matplotlib.axes.Axes.set_xlabel`.
**Returns**
* **self** : _singleAx_
"""
self.ax.set_xlabel(xlabel, **kwargs)
return(self)
def set_ylabel(self, ylabel='', **kwargs):
"""Sets the ylabel of the plot.
**Parameters**
* **ylabel** : _string_ <br />
The ylabel.
* ****kwargs** <br />
All other kwargs are passed on to `matplotlib.axes.Axes.set_ylabel`.
**Returns**
* **self** : _singleAx_
"""
self.ax.set_ylabel(ylabel, **kwargs)
return(self)
def set_xytitle(self, xlabel='', ylabel='', title='', **kwargs):
"""Set the xlabel, ylabel, and title at the same time.
Sets all three even if not all are given. Whatever you input will be applied to all three.
For individual control, use `singleAx.set_xlabel`, `singleAx.set_ylabel`,
or `singleAx.set_title`.
**Parameters**
* **ylabel** : _string_ <br />
The ylabel.
* **xlabel** : _string_ <br />
The xlabel.
* **title** : _string_ <br />
The plot title.
* ****kwargs** <br />
All other kwargs are passed on
to `matplotlib.axes.Axes.set_xlabel`, `matplotlib.axes.Axes.set_ylabel`,
and `matplotlib.axes.Axes.set_title`.
**Returns**
* **self** : _singleAx_
"""
self.ax.set_xlabel(xlabel, **kwargs)
self.ax.set_ylabel(ylabel, **kwargs)
self.ax.set_title(title, **kwargs)
return(self)
def set_axes(self, x, y):
"""Sets the x and y axes of the singleAx object, and can apply a window.
Note that this can be used before or after `set_window()`, as long as the two are in the same units.
**Parameters**
* **x** : _ndarray_ <br />
The x-coordinates. Should be 1-dimensional.
* **y** : _ndarray_ <br />
The y-coordinates. Should be 1-dimensional.
**Returns**
* **self** : _singleAx_
"""
self.x = x
self.y = y
return(self)
def set_window(self, window):
"""Applies a window to the singleAx object.
Note that this can be used before or after `set_axes()`, as long as the two are in the same units.
**Parameters**
* **window** : _array-like, optional_ <br />
Format: `window = [xmin, xmax, ymin, ymax]`. Note that these are the x
and y values, rather than their indices.
**Returns**
* **self** : _singleAx_
"""
self.xmin = window[0]
self.xmax = window[1]
self.ymin = window[2]
self.ymax = window[3]
return(self)
def pre_plot(self, data, step=1, origin=None):
"""Utility function that applies the axes and window before plotting.
If you want to use a plotting function from matplotlib, you can use this
function to get the windowed data:
```python
fig, axis = plt.subplots()
ax = singleAx(axis)
ax.set_window(window)
x_windowed, y_windowed, data_windowed = ax.pre_plot(data)
ax.ax.SomeOtherMatplotlibPlottingRoutine(x_windowed, y_windowed, data_windowed)
plt.show()
```
**Parameters** :
* **data** : _complex ndarray_ <br />
The data to plot. Must be 2-dimensional.
* **step** : _int_ <br />
data will be returned as `data[::step,::step]` - particularly useful for
quiver plots. <br />
Default is `step = 1`.
* **origin** : _string_ <br />
Either 'upper' or 'lower'. If not entered, default is `self.origin`, whose initial value is 'lower'. <br />
**Returns**
* **xout** : _ndarray_ <br />
A 1darray with the windowed x coordinates.
* **yout** : _ndarray_ <br />
A 1darray with the windowed y coordinates.
* **dout** : _ndarray_ <br />
A 2darray with the data to be plotted. If you have set a window using
`set_window()`, the data will be windowed.
"""
if self.x is None:
self.x = np.linspace(0, 100, data.shape[1])
if self.y is None:
self.y = np.linspace(0, 100, data.shape[0])
if self.xmin is None:
self.xmin = self.x[0]
if self.xmax is None:
self.xmax = self.x[-1]
if self.ymin is None:
self.ymin = self.y[0]
if self.ymax is None:
self.ymax = self.y[-1]
argxmin = np.argmin(np.abs(self.x - self.xmin))
argxmax = np.argmin(np.abs(self.x - self.xmax))
argymin = np.argmin(np.abs(self.x - self.ymin))
argymax = np.argmin(np.abs(self.x - self.ymax))
dout = data[argymin:argymax:step, argxmin:argxmax:step]
xout = self.x[argxmin:argxmax:step]
yout = self.y[argymin:argymax:step]
return(xout, yout, dout)
def imshow(self, data, step=1, **kwargs):
"""Imshows the (windowed) data.
**Parameters**
* **data** : _ndarray_ <br />
The data to be shown. Use the un-windowed data - the window will be
applied automatically, if you have set one.
* **step** : _int_ <br />
data will be returned as `data[::step,::step]` - particularly useful for
quiver plots. <br />
Default is `step = 1`.
* ****kwargs** <br />
All other kwargs are passed on to `matplotlib.axes.Axes.imshow`.
**Returns**
* **self** : _singleAx_
"""
imshowargs = {'origin': self.origin}
imshowargs.update(kwargs)
x, y, d = self.pre_plot(data, step, imshowargs['origin'])
if imshowargs['origin'] == 'lower':
extent = [x[0], x[-1], y[0], y[-1]]
elif imshowargs['origin'] == 'upper':
extent = [x[0], x[-1], y[-1], y[0]]
imshowargs.update({'extent': extent})
imshowargs.update(kwargs)
self.ax.imshow(d, **imshowargs)
return(self)
def quiver(self, data, step=1, origin=None, **kwargs):
"""Shows a quiver plot of complex data.
**Parameters**
* **data** : _ndarray, complex_ <br />
The data to be shown. Real part is x-component, imaginary is y-component.
Use the un-windowed data - the window will be
applied automatically, if you set one.
* **step** : _int_ <br />
data will be returned as `data[::step,::step]` - particularly useful for
quiver plots. <br />
Default is `step = 1`.
* **origin** : _string_ <br />
Either 'upper' or 'lower'. <br />
Default is `self.origin`, which is 'lower'.
* ****kwargs** <br />
All other kwargs are passed on to `matplotlib.axes.Axes.quiver`.
**Returns**
* **self** : _singleAx_
"""
if origin is None:
origin = self.origin
x, y, d = self.pre_plot(data, step, origin)
d = d.astype(complex)
if origin == 'upper':
d.imag *= -1
self.ax.quiver(x, y, d.real, d.imag, **kwargs)
return(self)
def rgba(self, data, step=1, cmap='uniform', brightness='intensity', alpha='uniform', **kwargs):
"""Shows an rgba interpretation of complex data.
**Parameters**
* **data** : _complex ndarray_ <br />
An array with the data to represent. Dtype may be complex or real - if real,
the color will be uniform, and values will be represented by brightness.
* **step** : _int_ <br />
data will be returned as `data[::step,::step]` - particularly useful for
quiver plots. <br />
Default is `step = 1`.
* **cmap** : _string, optional_ <br />
If `cmap = 'uniform'`, the CIELAB color space will be used. Otherwise, any
pyplot ScalarMappable may be used. <br />
Default is `cmap = 'uniform'`.
* **brightness** : _string, optional_ <br />
Allowed values: `'intensity'`, `'amplitude'`, `'uniform'`. <br />
Default is `brightness = 'intensity'`.
* **alpha** : _string, optional_ <br />
Allowed values: `'intensity'`, `'amplitude'`, `'uniform'`. Determines the alpha
component of the rgba value. <br />
Default is `alpha = 'uniform'`.
* ****kwargs** <br />
All other kwargs are passed on to `matplotlib.axes.Axes.imshow`.
**Returns**
* **self** : _singleAx_
"""
imshowargs = {'origin': self.origin}
imshowargs.update(kwargs)
x, y, d = self.pre_plot(data, step, imshowargs['origin'])
d = d.astype(complex)
if imshowargs['origin'] == 'lower':
extent = [x[0], x[-1], y[0], y[-1]]
elif imshowargs['origin'] == 'upper':
extent = [x[0], x[-1], y[-1], y[0]]
d.imag *= -1
imshowargs.update({'extent': extent})
imshowargs.update(kwargs)
self.ax.imshow(rgba(d, brightness=brightness, alpha=alpha, cmap=cmap), **imshowargs)
return(self)
def inset(self, window, **kwargs):
"""Plots a square box with vertices defined by window.
Default color is white.
**Parameters**
* **window** : _array-like_ <br />
Format: `window = [xmin, xmax, ymin, ymax]`. Note that these are the x
and y values, rather than their indices.
* ****kwargs** <br />
All other kwargs are passed on to `matplotlib.axes.Axes.plot`.
**Returns**
* **self** : _singleAx_
"""
plotargs = {'color': 'white', 'linewidth': .5}
plotargs.update(kwargs)
self.ax.plot(np.linspace(window[0], window[1], 100),
np.zeros(100) + window[2], **plotargs)
self.ax.plot(np.linspace(window[0], window[1],100),
np.zeros(100)+window[3], **plotargs)
self.ax.plot(np.zeros(100) + window[0],
np.linspace(window[2], window[3], 100), **plotargs)
self.ax.plot(np.zeros(100) + window[1],
np.linspace(window[2], window[3], 100),
**plotargs)
return(self)
def colorwheel(self, res=128, scale=0.25, cmap='uniform', brightness='intensity', alpha='uniform', **kwargs):
"""Adds a colorwheel to the bottom right corner of the plot.
**Parameters**
* **res** : _int_ <br />
The resolution of the colorwheel. <br />
Default is `res = 128`.
* **scale** : _float_ <br />
The size of the colorwheel, in units of the width of the axis. <br />
Default is `scale = 0.25`.
* **cmap** : _string, optional_ <br />
If `cmap = 'uniform'`, the CIELAB color space will be used. Otherwise, any
pyplot ScalarMappable may be used. <br />
Default is `cmap = 'uniform'`.
* **brightness** : _string, optional_ <br />
Allowed values: `'intensity'`, `'amplitude'`, `'uniform'`. <br />
Default is `brightness = 'intensity'`.
* **alpha** : _string, optional_ <br />
Allowed values: `'intensity'`, `'amplitude'`, `'uniform'`. Determines the alpha
component of the rgba value. <br />
Default is `alpha = 'uniform'`.
* ****kwargs** <br />
All other kwargs are passed on to `matplotlib.axes.Axes.imshow`.
**Returns**
* **self** : _singleAx_
"""
imshowargs = {'origin': self.origin, 'zorder': 3}
imshowargs.update(kwargs)
X = np.linspace(-1, 1, res)
x, y = np.meshgrid(X, X)
z = x + 1j*y
sel = np.abs(z) > 1
z[sel] = 0
colors = rgba(z, brightness=brightness, alpha=alpha, cmap=cmap)
colors[:,:,0][sel] = 0
colors[:,:,1][sel] = 0
colors[:,:,2][sel] = 0
xlims = self.ax.get_xlim()
ylims = self.ax.get_ylim()
self.ax.set_xlim(xlims)
self.ax.set_ylim(ylims)
self.ax.set_aspect('equal')
if imshowargs['origin'] == 'lower':
extent = [xlims[1] - scale * (xlims[1] - xlims[0]),
xlims[1],
ylims[0],
ylims[0] + scale * (xlims[1] - xlims[0])]
elif imshowargs['origin'] == 'upper':
extent = [xlims[1] - scale * (xlims[1] - xlims[0]),
xlims[1],
ylims[0] + scale * (xlims[0] - xlims[1]),
ylims[0]]
imshowargs.update(kwargs)
self.ax.imshow(colors, extent=extent, **imshowargs)
return(self)
def subplots(rc=11, **kwargs):
"""Creates a fig, ax instance but replaces ax with singleAx.
Behaves almost identically to matplotlib.pyplot.subplots(), but replaces each
`matplotlib.axes.Axes` object with a `wsp_tools.pyplotwrapper.singleAx`
object.
Each `wsp_tools.pyplotwrapper.singleAx` object in turn behaves just like a
normal `Axes` object, but with added methods.
**Parameters**
* **rc** : _int_ <br />
First digit - nrows. Second digit - ncols. <br />
Default is `rc = 11`.
* **squeeze** : _bool_ <br />
If true, extra dimensions are squeezed out from the returned array of Axes. <br />
Default is `squeeze = False`.
* ****kwargs** <br />
All other kwargs are passed on to `matplotlib.axes.Axes.subplots`.
**Returns**
* **fig** : _Figure_ <br />
* **ax** : _singleAx_ or array of _singleAx_ objects <br />
"""
subplotsargs = {'tight_layout': True, 'squeeze': False}
subplotsargs.update(kwargs)
fig, ax = plt.subplots(nrows=rc//10, ncols=rc%10, **subplotsargs)
for i in range(ax.shape[0]):
for j in range(ax.shape[1]):
ax[i][j] = singleAx(ax[i][j])
return(fig, np.array(ax))Functions
def subplots(rc=11, **kwargs)-
Creates a fig, ax instance but replaces ax with singleAx.
Behaves almost identically to matplotlib.pyplot.subplots(), but replaces each
matplotlib.axes.Axesobject with asingleAxobject.Each
singleAxobject in turn behaves just like a normalAxesobject, but with added methods.Parameters
-
rc : int
First digit - nrows. Second digit - ncols.
Default isrc = 11. -
squeeze : bool
If true, extra dimensions are squeezed out from the returned array of Axes.
Default issqueeze = False. -
**kwargs
All other kwargs are passed on tomatplotlib.axes.Axes.subplots.
Returns
-
fig : Figure
-
ax : singleAx or array of singleAx objects
Expand source code
def subplots(rc=11, **kwargs): """Creates a fig, ax instance but replaces ax with singleAx. Behaves almost identically to matplotlib.pyplot.subplots(), but replaces each `matplotlib.axes.Axes` object with a `wsp_tools.pyplotwrapper.singleAx` object. Each `wsp_tools.pyplotwrapper.singleAx` object in turn behaves just like a normal `Axes` object, but with added methods. **Parameters** * **rc** : _int_ <br /> First digit - nrows. Second digit - ncols. <br /> Default is `rc = 11`. * **squeeze** : _bool_ <br /> If true, extra dimensions are squeezed out from the returned array of Axes. <br /> Default is `squeeze = False`. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.subplots`. **Returns** * **fig** : _Figure_ <br /> * **ax** : _singleAx_ or array of _singleAx_ objects <br /> """ subplotsargs = {'tight_layout': True, 'squeeze': False} subplotsargs.update(kwargs) fig, ax = plt.subplots(nrows=rc//10, ncols=rc%10, **subplotsargs) for i in range(ax.shape[0]): for j in range(ax.shape[1]): ax[i][j] = singleAx(ax[i][j]) return(fig, np.array(ax)) -
Classes
class singleAx (ax)-
An extension of the
matplotlib.axes.Axesclass.This class adds macros for 2d plotting that I commonly use. In particular, it's easy to select only a window of your data to show, to add x-y axes, to add an inset, and to show the rgba version of a complex 2d array.
Typical usage:
X = np.linspace(-10,10,xres) Y = np.linspace(-10,10,yres) x, y = np.meshgrid(X, Y) data = x+1j*y window = [-3,7,1,4] fig, ax = plt.subplots() myax = wsp_tools.singleAx(ax) ax.set_axes(x, y, window) ax.set_xytitle('x','y','title') ax.rgba(data) plt.show()More commonly, this class is returned by
wsp_tools.pyplotwrapper.subplots.Parameters
- ax : matplotlib.axes.Axes
Expand source code
class singleAx(): """An extension of the `matplotlib.axes.Axes` class. This class adds macros for 2d plotting that I commonly use. In particular, it's easy to select only a window of your data to show, to add x-y axes, to add an inset, and to show the rgba version of a complex 2d array. Typical usage: ```python X = np.linspace(-10,10,xres) Y = np.linspace(-10,10,yres) x, y = np.meshgrid(X, Y) data = x+1j*y window = [-3,7,1,4] fig, ax = plt.subplots() myax = wsp_tools.singleAx(ax) ax.set_axes(x, y, window) ax.set_xytitle('x','y','title') ax.rgba(data) plt.show() ``` More commonly, this class is returned by ```wsp_tools.pyplotwrapper.subplots```. **Parameters** * **ax** : _matplotlib.axes.Axes_ <br /> """ def __init__(self, ax): self.ax = ax self.origin = 'lower' self.x = None self.y = None self.xmin = None self.ymin = None self.xmax = None self.ymax = None def set_title(self, title='', **kwargs): """Sets the title of the plot. **Parameters** * **title** : _string_ <br /> The plot title. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.set_title`. **Returns** * **self** : _singleAx_ """ self.ax.set_title(title, **kwargs) return(self) def set_xlabel(self, xlabel='', **kwargs): """Sets the xlabel of the plot. **Parameters* * **xlabel** : _string_ <br /> The xlabel. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.set_xlabel`. **Returns** * **self** : _singleAx_ """ self.ax.set_xlabel(xlabel, **kwargs) return(self) def set_ylabel(self, ylabel='', **kwargs): """Sets the ylabel of the plot. **Parameters** * **ylabel** : _string_ <br /> The ylabel. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.set_ylabel`. **Returns** * **self** : _singleAx_ """ self.ax.set_ylabel(ylabel, **kwargs) return(self) def set_xytitle(self, xlabel='', ylabel='', title='', **kwargs): """Set the xlabel, ylabel, and title at the same time. Sets all three even if not all are given. Whatever you input will be applied to all three. For individual control, use `singleAx.set_xlabel`, `singleAx.set_ylabel`, or `singleAx.set_title`. **Parameters** * **ylabel** : _string_ <br /> The ylabel. * **xlabel** : _string_ <br /> The xlabel. * **title** : _string_ <br /> The plot title. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.set_xlabel`, `matplotlib.axes.Axes.set_ylabel`, and `matplotlib.axes.Axes.set_title`. **Returns** * **self** : _singleAx_ """ self.ax.set_xlabel(xlabel, **kwargs) self.ax.set_ylabel(ylabel, **kwargs) self.ax.set_title(title, **kwargs) return(self) def set_axes(self, x, y): """Sets the x and y axes of the singleAx object, and can apply a window. Note that this can be used before or after `set_window()`, as long as the two are in the same units. **Parameters** * **x** : _ndarray_ <br /> The x-coordinates. Should be 1-dimensional. * **y** : _ndarray_ <br /> The y-coordinates. Should be 1-dimensional. **Returns** * **self** : _singleAx_ """ self.x = x self.y = y return(self) def set_window(self, window): """Applies a window to the singleAx object. Note that this can be used before or after `set_axes()`, as long as the two are in the same units. **Parameters** * **window** : _array-like, optional_ <br /> Format: `window = [xmin, xmax, ymin, ymax]`. Note that these are the x and y values, rather than their indices. **Returns** * **self** : _singleAx_ """ self.xmin = window[0] self.xmax = window[1] self.ymin = window[2] self.ymax = window[3] return(self) def pre_plot(self, data, step=1, origin=None): """Utility function that applies the axes and window before plotting. If you want to use a plotting function from matplotlib, you can use this function to get the windowed data: ```python fig, axis = plt.subplots() ax = singleAx(axis) ax.set_window(window) x_windowed, y_windowed, data_windowed = ax.pre_plot(data) ax.ax.SomeOtherMatplotlibPlottingRoutine(x_windowed, y_windowed, data_windowed) plt.show() ``` **Parameters** : * **data** : _complex ndarray_ <br /> The data to plot. Must be 2-dimensional. * **step** : _int_ <br /> data will be returned as `data[::step,::step]` - particularly useful for quiver plots. <br /> Default is `step = 1`. * **origin** : _string_ <br /> Either 'upper' or 'lower'. If not entered, default is `self.origin`, whose initial value is 'lower'. <br /> **Returns** * **xout** : _ndarray_ <br /> A 1darray with the windowed x coordinates. * **yout** : _ndarray_ <br /> A 1darray with the windowed y coordinates. * **dout** : _ndarray_ <br /> A 2darray with the data to be plotted. If you have set a window using `set_window()`, the data will be windowed. """ if self.x is None: self.x = np.linspace(0, 100, data.shape[1]) if self.y is None: self.y = np.linspace(0, 100, data.shape[0]) if self.xmin is None: self.xmin = self.x[0] if self.xmax is None: self.xmax = self.x[-1] if self.ymin is None: self.ymin = self.y[0] if self.ymax is None: self.ymax = self.y[-1] argxmin = np.argmin(np.abs(self.x - self.xmin)) argxmax = np.argmin(np.abs(self.x - self.xmax)) argymin = np.argmin(np.abs(self.x - self.ymin)) argymax = np.argmin(np.abs(self.x - self.ymax)) dout = data[argymin:argymax:step, argxmin:argxmax:step] xout = self.x[argxmin:argxmax:step] yout = self.y[argymin:argymax:step] return(xout, yout, dout) def imshow(self, data, step=1, **kwargs): """Imshows the (windowed) data. **Parameters** * **data** : _ndarray_ <br /> The data to be shown. Use the un-windowed data - the window will be applied automatically, if you have set one. * **step** : _int_ <br /> data will be returned as `data[::step,::step]` - particularly useful for quiver plots. <br /> Default is `step = 1`. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.imshow`. **Returns** * **self** : _singleAx_ """ imshowargs = {'origin': self.origin} imshowargs.update(kwargs) x, y, d = self.pre_plot(data, step, imshowargs['origin']) if imshowargs['origin'] == 'lower': extent = [x[0], x[-1], y[0], y[-1]] elif imshowargs['origin'] == 'upper': extent = [x[0], x[-1], y[-1], y[0]] imshowargs.update({'extent': extent}) imshowargs.update(kwargs) self.ax.imshow(d, **imshowargs) return(self) def quiver(self, data, step=1, origin=None, **kwargs): """Shows a quiver plot of complex data. **Parameters** * **data** : _ndarray, complex_ <br /> The data to be shown. Real part is x-component, imaginary is y-component. Use the un-windowed data - the window will be applied automatically, if you set one. * **step** : _int_ <br /> data will be returned as `data[::step,::step]` - particularly useful for quiver plots. <br /> Default is `step = 1`. * **origin** : _string_ <br /> Either 'upper' or 'lower'. <br /> Default is `self.origin`, which is 'lower'. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.quiver`. **Returns** * **self** : _singleAx_ """ if origin is None: origin = self.origin x, y, d = self.pre_plot(data, step, origin) d = d.astype(complex) if origin == 'upper': d.imag *= -1 self.ax.quiver(x, y, d.real, d.imag, **kwargs) return(self) def rgba(self, data, step=1, cmap='uniform', brightness='intensity', alpha='uniform', **kwargs): """Shows an rgba interpretation of complex data. **Parameters** * **data** : _complex ndarray_ <br /> An array with the data to represent. Dtype may be complex or real - if real, the color will be uniform, and values will be represented by brightness. * **step** : _int_ <br /> data will be returned as `data[::step,::step]` - particularly useful for quiver plots. <br /> Default is `step = 1`. * **cmap** : _string, optional_ <br /> If `cmap = 'uniform'`, the CIELAB color space will be used. Otherwise, any pyplot ScalarMappable may be used. <br /> Default is `cmap = 'uniform'`. * **brightness** : _string, optional_ <br /> Allowed values: `'intensity'`, `'amplitude'`, `'uniform'`. <br /> Default is `brightness = 'intensity'`. * **alpha** : _string, optional_ <br /> Allowed values: `'intensity'`, `'amplitude'`, `'uniform'`. Determines the alpha component of the rgba value. <br /> Default is `alpha = 'uniform'`. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.imshow`. **Returns** * **self** : _singleAx_ """ imshowargs = {'origin': self.origin} imshowargs.update(kwargs) x, y, d = self.pre_plot(data, step, imshowargs['origin']) d = d.astype(complex) if imshowargs['origin'] == 'lower': extent = [x[0], x[-1], y[0], y[-1]] elif imshowargs['origin'] == 'upper': extent = [x[0], x[-1], y[-1], y[0]] d.imag *= -1 imshowargs.update({'extent': extent}) imshowargs.update(kwargs) self.ax.imshow(rgba(d, brightness=brightness, alpha=alpha, cmap=cmap), **imshowargs) return(self) def inset(self, window, **kwargs): """Plots a square box with vertices defined by window. Default color is white. **Parameters** * **window** : _array-like_ <br /> Format: `window = [xmin, xmax, ymin, ymax]`. Note that these are the x and y values, rather than their indices. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.plot`. **Returns** * **self** : _singleAx_ """ plotargs = {'color': 'white', 'linewidth': .5} plotargs.update(kwargs) self.ax.plot(np.linspace(window[0], window[1], 100), np.zeros(100) + window[2], **plotargs) self.ax.plot(np.linspace(window[0], window[1],100), np.zeros(100)+window[3], **plotargs) self.ax.plot(np.zeros(100) + window[0], np.linspace(window[2], window[3], 100), **plotargs) self.ax.plot(np.zeros(100) + window[1], np.linspace(window[2], window[3], 100), **plotargs) return(self) def colorwheel(self, res=128, scale=0.25, cmap='uniform', brightness='intensity', alpha='uniform', **kwargs): """Adds a colorwheel to the bottom right corner of the plot. **Parameters** * **res** : _int_ <br /> The resolution of the colorwheel. <br /> Default is `res = 128`. * **scale** : _float_ <br /> The size of the colorwheel, in units of the width of the axis. <br /> Default is `scale = 0.25`. * **cmap** : _string, optional_ <br /> If `cmap = 'uniform'`, the CIELAB color space will be used. Otherwise, any pyplot ScalarMappable may be used. <br /> Default is `cmap = 'uniform'`. * **brightness** : _string, optional_ <br /> Allowed values: `'intensity'`, `'amplitude'`, `'uniform'`. <br /> Default is `brightness = 'intensity'`. * **alpha** : _string, optional_ <br /> Allowed values: `'intensity'`, `'amplitude'`, `'uniform'`. Determines the alpha component of the rgba value. <br /> Default is `alpha = 'uniform'`. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.imshow`. **Returns** * **self** : _singleAx_ """ imshowargs = {'origin': self.origin, 'zorder': 3} imshowargs.update(kwargs) X = np.linspace(-1, 1, res) x, y = np.meshgrid(X, X) z = x + 1j*y sel = np.abs(z) > 1 z[sel] = 0 colors = rgba(z, brightness=brightness, alpha=alpha, cmap=cmap) colors[:,:,0][sel] = 0 colors[:,:,1][sel] = 0 colors[:,:,2][sel] = 0 xlims = self.ax.get_xlim() ylims = self.ax.get_ylim() self.ax.set_xlim(xlims) self.ax.set_ylim(ylims) self.ax.set_aspect('equal') if imshowargs['origin'] == 'lower': extent = [xlims[1] - scale * (xlims[1] - xlims[0]), xlims[1], ylims[0], ylims[0] + scale * (xlims[1] - xlims[0])] elif imshowargs['origin'] == 'upper': extent = [xlims[1] - scale * (xlims[1] - xlims[0]), xlims[1], ylims[0] + scale * (xlims[0] - xlims[1]), ylims[0]] imshowargs.update(kwargs) self.ax.imshow(colors, extent=extent, **imshowargs) return(self)Methods
def colorwheel(self, res=128, scale=0.25, cmap='uniform', brightness='intensity', alpha='uniform', **kwargs)-
Adds a colorwheel to the bottom right corner of the plot.
Parameters
-
res : int
The resolution of the colorwheel.
Default isres = 128. -
scale : float
The size of the colorwheel, in units of the width of the axis.
Default isscale = 0.25. -
cmap : string, optional
Ifcmap = 'uniform', the CIELAB color space will be used. Otherwise, any pyplot ScalarMappable may be used.
Default iscmap = 'uniform'. -
brightness : string, optional
Allowed values:'intensity','amplitude','uniform'.
Default isbrightness = 'intensity'. -
alpha : string, optional
Allowed values:'intensity','amplitude','uniform'. Determines the alpha component of the rgba value.
Default isalpha = 'uniform'. -
**kwargs
All other kwargs are passed on tomatplotlib.axes.Axes.imshow.
Returns
- self : singleAx
Expand source code
def colorwheel(self, res=128, scale=0.25, cmap='uniform', brightness='intensity', alpha='uniform', **kwargs): """Adds a colorwheel to the bottom right corner of the plot. **Parameters** * **res** : _int_ <br /> The resolution of the colorwheel. <br /> Default is `res = 128`. * **scale** : _float_ <br /> The size of the colorwheel, in units of the width of the axis. <br /> Default is `scale = 0.25`. * **cmap** : _string, optional_ <br /> If `cmap = 'uniform'`, the CIELAB color space will be used. Otherwise, any pyplot ScalarMappable may be used. <br /> Default is `cmap = 'uniform'`. * **brightness** : _string, optional_ <br /> Allowed values: `'intensity'`, `'amplitude'`, `'uniform'`. <br /> Default is `brightness = 'intensity'`. * **alpha** : _string, optional_ <br /> Allowed values: `'intensity'`, `'amplitude'`, `'uniform'`. Determines the alpha component of the rgba value. <br /> Default is `alpha = 'uniform'`. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.imshow`. **Returns** * **self** : _singleAx_ """ imshowargs = {'origin': self.origin, 'zorder': 3} imshowargs.update(kwargs) X = np.linspace(-1, 1, res) x, y = np.meshgrid(X, X) z = x + 1j*y sel = np.abs(z) > 1 z[sel] = 0 colors = rgba(z, brightness=brightness, alpha=alpha, cmap=cmap) colors[:,:,0][sel] = 0 colors[:,:,1][sel] = 0 colors[:,:,2][sel] = 0 xlims = self.ax.get_xlim() ylims = self.ax.get_ylim() self.ax.set_xlim(xlims) self.ax.set_ylim(ylims) self.ax.set_aspect('equal') if imshowargs['origin'] == 'lower': extent = [xlims[1] - scale * (xlims[1] - xlims[0]), xlims[1], ylims[0], ylims[0] + scale * (xlims[1] - xlims[0])] elif imshowargs['origin'] == 'upper': extent = [xlims[1] - scale * (xlims[1] - xlims[0]), xlims[1], ylims[0] + scale * (xlims[0] - xlims[1]), ylims[0]] imshowargs.update(kwargs) self.ax.imshow(colors, extent=extent, **imshowargs) return(self) -
def imshow(self, data, step=1, **kwargs)-
Imshows the (windowed) data.
Parameters
-
data : ndarray
The data to be shown. Use the un-windowed data - the window will be applied automatically, if you have set one. -
step : int
data will be returned asdata[::step,::step]- particularly useful for quiver plots.
Default isstep = 1. -
**kwargs
All other kwargs are passed on tomatplotlib.axes.Axes.imshow.
Returns
- self : singleAx
Expand source code
def imshow(self, data, step=1, **kwargs): """Imshows the (windowed) data. **Parameters** * **data** : _ndarray_ <br /> The data to be shown. Use the un-windowed data - the window will be applied automatically, if you have set one. * **step** : _int_ <br /> data will be returned as `data[::step,::step]` - particularly useful for quiver plots. <br /> Default is `step = 1`. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.imshow`. **Returns** * **self** : _singleAx_ """ imshowargs = {'origin': self.origin} imshowargs.update(kwargs) x, y, d = self.pre_plot(data, step, imshowargs['origin']) if imshowargs['origin'] == 'lower': extent = [x[0], x[-1], y[0], y[-1]] elif imshowargs['origin'] == 'upper': extent = [x[0], x[-1], y[-1], y[0]] imshowargs.update({'extent': extent}) imshowargs.update(kwargs) self.ax.imshow(d, **imshowargs) return(self) -
def inset(self, window, **kwargs)-
Plots a square box with vertices defined by window.
Default color is white.
Parameters
-
window : array-like
Format:window = [xmin, xmax, ymin, ymax]. Note that these are the x and y values, rather than their indices. -
**kwargs
All other kwargs are passed on tomatplotlib.axes.Axes.plot.
Returns
- self : singleAx
Expand source code
def inset(self, window, **kwargs): """Plots a square box with vertices defined by window. Default color is white. **Parameters** * **window** : _array-like_ <br /> Format: `window = [xmin, xmax, ymin, ymax]`. Note that these are the x and y values, rather than their indices. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.plot`. **Returns** * **self** : _singleAx_ """ plotargs = {'color': 'white', 'linewidth': .5} plotargs.update(kwargs) self.ax.plot(np.linspace(window[0], window[1], 100), np.zeros(100) + window[2], **plotargs) self.ax.plot(np.linspace(window[0], window[1],100), np.zeros(100)+window[3], **plotargs) self.ax.plot(np.zeros(100) + window[0], np.linspace(window[2], window[3], 100), **plotargs) self.ax.plot(np.zeros(100) + window[1], np.linspace(window[2], window[3], 100), **plotargs) return(self) -
def pre_plot(self, data, step=1, origin=None)-
Utility function that applies the axes and window before plotting.
If you want to use a plotting function from matplotlib, you can use this function to get the windowed data:
fig, axis = plt.subplots() ax = singleAx(axis) ax.set_window(window) x_windowed, y_windowed, data_windowed = ax.pre_plot(data) ax.ax.SomeOtherMatplotlibPlottingRoutine(x_windowed, y_windowed, data_windowed) plt.show()Parameters :
-
data : complex ndarray
The data to plot. Must be 2-dimensional. -
step : int
data will be returned asdata[::step,::step]- particularly useful for quiver plots.
Default isstep = 1. -
origin : string
Either 'upper' or 'lower'. If not entered, default isself.origin, whose initial value is 'lower'.
Returns
-
xout : ndarray
A 1darray with the windowed x coordinates. -
yout : ndarray
A 1darray with the windowed y coordinates. -
dout : ndarray
A 2darray with the data to be plotted. If you have set a window usingset_window(), the data will be windowed.
Expand source code
def pre_plot(self, data, step=1, origin=None): """Utility function that applies the axes and window before plotting. If you want to use a plotting function from matplotlib, you can use this function to get the windowed data: ```python fig, axis = plt.subplots() ax = singleAx(axis) ax.set_window(window) x_windowed, y_windowed, data_windowed = ax.pre_plot(data) ax.ax.SomeOtherMatplotlibPlottingRoutine(x_windowed, y_windowed, data_windowed) plt.show() ``` **Parameters** : * **data** : _complex ndarray_ <br /> The data to plot. Must be 2-dimensional. * **step** : _int_ <br /> data will be returned as `data[::step,::step]` - particularly useful for quiver plots. <br /> Default is `step = 1`. * **origin** : _string_ <br /> Either 'upper' or 'lower'. If not entered, default is `self.origin`, whose initial value is 'lower'. <br /> **Returns** * **xout** : _ndarray_ <br /> A 1darray with the windowed x coordinates. * **yout** : _ndarray_ <br /> A 1darray with the windowed y coordinates. * **dout** : _ndarray_ <br /> A 2darray with the data to be plotted. If you have set a window using `set_window()`, the data will be windowed. """ if self.x is None: self.x = np.linspace(0, 100, data.shape[1]) if self.y is None: self.y = np.linspace(0, 100, data.shape[0]) if self.xmin is None: self.xmin = self.x[0] if self.xmax is None: self.xmax = self.x[-1] if self.ymin is None: self.ymin = self.y[0] if self.ymax is None: self.ymax = self.y[-1] argxmin = np.argmin(np.abs(self.x - self.xmin)) argxmax = np.argmin(np.abs(self.x - self.xmax)) argymin = np.argmin(np.abs(self.x - self.ymin)) argymax = np.argmin(np.abs(self.x - self.ymax)) dout = data[argymin:argymax:step, argxmin:argxmax:step] xout = self.x[argxmin:argxmax:step] yout = self.y[argymin:argymax:step] return(xout, yout, dout) -
def quiver(self, data, step=1, origin=None, **kwargs)-
Shows a quiver plot of complex data.
Parameters
-
data : ndarray, complex
The data to be shown. Real part is x-component, imaginary is y-component. Use the un-windowed data - the window will be applied automatically, if you set one. -
step : int
data will be returned asdata[::step,::step]- particularly useful for quiver plots.
Default isstep = 1. -
origin : string
Either 'upper' or 'lower'.
Default isself.origin, which is 'lower'. -
**kwargs
All other kwargs are passed on tomatplotlib.axes.Axes.quiver.
Returns
- self : singleAx
Expand source code
def quiver(self, data, step=1, origin=None, **kwargs): """Shows a quiver plot of complex data. **Parameters** * **data** : _ndarray, complex_ <br /> The data to be shown. Real part is x-component, imaginary is y-component. Use the un-windowed data - the window will be applied automatically, if you set one. * **step** : _int_ <br /> data will be returned as `data[::step,::step]` - particularly useful for quiver plots. <br /> Default is `step = 1`. * **origin** : _string_ <br /> Either 'upper' or 'lower'. <br /> Default is `self.origin`, which is 'lower'. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.quiver`. **Returns** * **self** : _singleAx_ """ if origin is None: origin = self.origin x, y, d = self.pre_plot(data, step, origin) d = d.astype(complex) if origin == 'upper': d.imag *= -1 self.ax.quiver(x, y, d.real, d.imag, **kwargs) return(self) -
def rgba(self, data, step=1, cmap='uniform', brightness='intensity', alpha='uniform', **kwargs)-
Shows an rgba interpretation of complex data.
Parameters
-
data : complex ndarray
An array with the data to represent. Dtype may be complex or real - if real, the color will be uniform, and values will be represented by brightness. -
step : int
data will be returned asdata[::step,::step]- particularly useful for quiver plots.
Default isstep = 1. -
cmap : string, optional
Ifcmap = 'uniform', the CIELAB color space will be used. Otherwise, any pyplot ScalarMappable may be used.
Default iscmap = 'uniform'. -
brightness : string, optional
Allowed values:'intensity','amplitude','uniform'.
Default isbrightness = 'intensity'. -
alpha : string, optional
Allowed values:'intensity','amplitude','uniform'. Determines the alpha component of the rgba value.
Default isalpha = 'uniform'. -
**kwargs
All other kwargs are passed on tomatplotlib.axes.Axes.imshow.
Returns
- self : singleAx
Expand source code
def rgba(self, data, step=1, cmap='uniform', brightness='intensity', alpha='uniform', **kwargs): """Shows an rgba interpretation of complex data. **Parameters** * **data** : _complex ndarray_ <br /> An array with the data to represent. Dtype may be complex or real - if real, the color will be uniform, and values will be represented by brightness. * **step** : _int_ <br /> data will be returned as `data[::step,::step]` - particularly useful for quiver plots. <br /> Default is `step = 1`. * **cmap** : _string, optional_ <br /> If `cmap = 'uniform'`, the CIELAB color space will be used. Otherwise, any pyplot ScalarMappable may be used. <br /> Default is `cmap = 'uniform'`. * **brightness** : _string, optional_ <br /> Allowed values: `'intensity'`, `'amplitude'`, `'uniform'`. <br /> Default is `brightness = 'intensity'`. * **alpha** : _string, optional_ <br /> Allowed values: `'intensity'`, `'amplitude'`, `'uniform'`. Determines the alpha component of the rgba value. <br /> Default is `alpha = 'uniform'`. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.imshow`. **Returns** * **self** : _singleAx_ """ imshowargs = {'origin': self.origin} imshowargs.update(kwargs) x, y, d = self.pre_plot(data, step, imshowargs['origin']) d = d.astype(complex) if imshowargs['origin'] == 'lower': extent = [x[0], x[-1], y[0], y[-1]] elif imshowargs['origin'] == 'upper': extent = [x[0], x[-1], y[-1], y[0]] d.imag *= -1 imshowargs.update({'extent': extent}) imshowargs.update(kwargs) self.ax.imshow(rgba(d, brightness=brightness, alpha=alpha, cmap=cmap), **imshowargs) return(self) -
def set_axes(self, x, y)-
Sets the x and y axes of the singleAx object, and can apply a window.
Note that this can be used before or after
set_window(), as long as the two are in the same units.Parameters
-
x : ndarray
The x-coordinates. Should be 1-dimensional. -
y : ndarray
The y-coordinates. Should be 1-dimensional.
Returns
- self : singleAx
Expand source code
def set_axes(self, x, y): """Sets the x and y axes of the singleAx object, and can apply a window. Note that this can be used before or after `set_window()`, as long as the two are in the same units. **Parameters** * **x** : _ndarray_ <br /> The x-coordinates. Should be 1-dimensional. * **y** : _ndarray_ <br /> The y-coordinates. Should be 1-dimensional. **Returns** * **self** : _singleAx_ """ self.x = x self.y = y return(self) -
def set_title(self, title='', **kwargs)-
Sets the title of the plot.
Parameters
-
title : string
The plot title. -
**kwargs
All other kwargs are passed on tomatplotlib.axes.Axes.set_title.
Returns
- self : singleAx
Expand source code
def set_title(self, title='', **kwargs): """Sets the title of the plot. **Parameters** * **title** : _string_ <br /> The plot title. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.set_title`. **Returns** * **self** : _singleAx_ """ self.ax.set_title(title, **kwargs) return(self) -
def set_window(self, window)-
Applies a window to the singleAx object.
Note that this can be used before or after
set_axes(), as long as the two are in the same units.Parameters
- window : array-like, optional
Format:window = [xmin, xmax, ymin, ymax]. Note that these are the x and y values, rather than their indices.
Returns
- self : singleAx
Expand source code
def set_window(self, window): """Applies a window to the singleAx object. Note that this can be used before or after `set_axes()`, as long as the two are in the same units. **Parameters** * **window** : _array-like, optional_ <br /> Format: `window = [xmin, xmax, ymin, ymax]`. Note that these are the x and y values, rather than their indices. **Returns** * **self** : _singleAx_ """ self.xmin = window[0] self.xmax = window[1] self.ymin = window[2] self.ymax = window[3] return(self) - window : array-like, optional
def set_xlabel(self, xlabel='', **kwargs)-
Sets the xlabel of the plot.
*Parameters
-
xlabel : string
The xlabel. -
**kwargs
All other kwargs are passed on tomatplotlib.axes.Axes.set_xlabel.
Returns
- self : singleAx
Expand source code
def set_xlabel(self, xlabel='', **kwargs): """Sets the xlabel of the plot. **Parameters* * **xlabel** : _string_ <br /> The xlabel. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.set_xlabel`. **Returns** * **self** : _singleAx_ """ self.ax.set_xlabel(xlabel, **kwargs) return(self) -
def set_xytitle(self, xlabel='', ylabel='', title='', **kwargs)-
Set the xlabel, ylabel, and title at the same time.
Sets all three even if not all are given. Whatever you input will be applied to all three.
For individual control, use
singleAx.set_xlabel(),singleAx.set_ylabel(), orsingleAx.set_title().Parameters
-
ylabel : string
The ylabel. -
xlabel : string
The xlabel. -
title : string
The plot title. -
**kwargs
All other kwargs are passed on tomatplotlib.axes.Axes.set_xlabel,matplotlib.axes.Axes.set_ylabel, andmatplotlib.axes.Axes.set_title.
Returns
- self : singleAx
Expand source code
def set_xytitle(self, xlabel='', ylabel='', title='', **kwargs): """Set the xlabel, ylabel, and title at the same time. Sets all three even if not all are given. Whatever you input will be applied to all three. For individual control, use `singleAx.set_xlabel`, `singleAx.set_ylabel`, or `singleAx.set_title`. **Parameters** * **ylabel** : _string_ <br /> The ylabel. * **xlabel** : _string_ <br /> The xlabel. * **title** : _string_ <br /> The plot title. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.set_xlabel`, `matplotlib.axes.Axes.set_ylabel`, and `matplotlib.axes.Axes.set_title`. **Returns** * **self** : _singleAx_ """ self.ax.set_xlabel(xlabel, **kwargs) self.ax.set_ylabel(ylabel, **kwargs) self.ax.set_title(title, **kwargs) return(self) -
def set_ylabel(self, ylabel='', **kwargs)-
Sets the ylabel of the plot.
Parameters
-
ylabel : string
The ylabel. -
**kwargs
All other kwargs are passed on tomatplotlib.axes.Axes.set_ylabel.
Returns
- self : singleAx
Expand source code
def set_ylabel(self, ylabel='', **kwargs): """Sets the ylabel of the plot. **Parameters** * **ylabel** : _string_ <br /> The ylabel. * ****kwargs** <br /> All other kwargs are passed on to `matplotlib.axes.Axes.set_ylabel`. **Returns** * **self** : _singleAx_ """ self.ax.set_ylabel(ylabel, **kwargs) return(self) -
- ax : matplotlib.axes.Axes