def __init__(self, tab, x, y, z, title=None, x_title=None, y_title=None,
z_title=None, x_range=None, y_range=None, z_range=None,
color=None):
self._tab = tab
self.x, self.y, self.z = (x, y, z)
self._title = title
self._x_title = x_title
self._y_title = y_title
self._z_title = z_title
self._color = color
for r, n in ((x_range, 'x'), (y_range, 'y'), (z_range, 'z')):
if r and (typeutil.is_scalar(x_range) or len(x_range)!=2):
raise ValueError('%s_range must contain exactly two items' % n)
self._x_range, self._y_range, self._z_range = (x_range, y_range, z_range)
def plot_hexbin(self, x, y, title=None, x_title=None, y_title=None, x_range=None, y_range=None, binning='log',
colormap='jet', show_scalebar=False, scalebar_label=None, clear=True, save=False, show=False):
"""
Create a heatplot of the data in col x vs the data in col y using matplotlib
:param x: column name with x data
:type x: :class:`str`
:param y: column name with y data
:type y: :class:`str`
:param title: title of the plot, will be generated automatically if set to None
:type title: :class:`str`
:param x_title: label of x-axis, will be generated automatically if set to None
:type title: :class:`str`
:param y_title: label of y-axis, will be generated automatically if set to None
:type title: :class:`str`
:param x_range: start and end value for first dimension (e.g. [start_x, end_x])
:type x_range: :class:`list` of length two
:param y_range: start and end value for second dimension (e.g. [start_y, end_y])
:type y_range: :class:`list` of length two
:param binning: type of binning. If set to None, the value of a hexbin will
correspond to the number of datapoints falling into it. If
set to 'log', the value will be the log with base 10 of the above
value (log(i+1)). If an integer is provided, the number of a
hexbin is equal the number of datapoints falling into it divided
by the integer. If a list of values is provided, these values
will be the lower bounds of the bins.
:param colormap: colormap, that will be used. Value can be every colormap defined
in matplotlib or an own defined colormap. You can either pass a
string with the name of the matplotlib colormap or a colormap
object.
:param show_scalebar: If set to True, a scalebar according to the chosen colormap is shown
:type show_scalebar: :class:`bool`
:param scalebar_label: Label of the scalebar
:type scalebar_label: :class:`str`
:param clear: clear old data from plot
:type clear: :class:`bool`
:param save: filename for saving plot
:type save: :class:`str`
:param show: directly show plot
:type show: :class:`bool`
"""
try:
import matplotlib.pyplot as plt
import matplotlib.cm as cm
except:
raise ImportError('plot_hexbin relies on matplotlib, but I could not import it')
idx=self.col_index(x)
idy=self.col_index(y)
xdata=[]
ydata=[]
for r in self.rows:
if r[idx]!=None and r[idy]!=None:
xdata.append(r[idx])
ydata.append(r[idy])
if clear:
plt.clf()
if x_title!=None:
nice_x=x_title
else:
nice_x=make_title(x)
if y_title!=None:
nice_y=y_title
else:
nice_y=make_title(y)
if title==None:
title = '%s vs. %s' % (nice_x, nice_y)
if typeutil.is_string_like(colormap):
colormap=getattr(cm, colormap)
if x_range and (typeutil.is_scalar(x_range) or len(x_range)!=2):
raise ValueError('parameter x_range must contain exactly two elements')
if y_range and (typeutil.is_scalar(y_range) or len(y_range)!=2):
raise ValueError('parameter y_range must contain exactly two elements')
if x_range:
plt.xlim((x_range[0], x_range[1]))
if y_range:
plt.ylim(y_range[0], y_range[1])
extent = None
if x_range and y_range:
extent = [x_range[0], x_range[1], y_range[0], y_range[1]]
plt.hexbin(xdata, ydata, bins=binning, cmap=colormap, extent=extent)
plt.title(title, size='x-large', fontweight='bold',
verticalalignment='bottom')
plt.xlabel(nice_x)
plt.ylabel(nice_y)
if show_scalebar:
cb=plt.colorbar()
if scalebar_label:
cb.set_label(scalebar_label)
if save:
plt.savefig(save)
if show:
plt.show()
return plt
