def plot_counts(values,
ax=None,
xscale='lin',
yscale='lin',
xParam=None,
label=None,
**kwargs):
"""
Plots the probability density function of given values.
Parameters
----------
values : numpy ndarray
the values for which the experimental pdf is computed
ax : matplotlib axes object, optional
axes to plot the figure in
xscale : str
'lin' or 'log', or ... (see binner.Bins for more details)
yscale : str
'lin' or 'log'
xParam : different things, optional
see binner.Bins for more details
**kwargs : kwargs
keyword arguments that will be passed to matplotlib hist
function
Returns
-------
fig : matplotlib Figure
the parent figure of the axes
ax : matplotlib Axes object
the axes in which the pdf is plotted
"""
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
else:
fig = ax.get_figure()
indices = bins.get_reasonable_data_indices_for_binning(values,
xscale=xscale)
prop_vals = values[indices]
if xParam is None:
if xscale == 'log':
xParam = np.sqrt(2) # just getting a nice factor of two...
if xscale == 'lin':
xParam = 50
xbins = bins.Bins(float, np.min(prop_vals), np.max(prop_vals), xscale,
xParam)
ax.hist(values, bins=xbins.bin_limits, normed=False, label=label, **kwargs)
if 'log' in xscale:
ax.set_xscale('log')
if 'log' in yscale:
ax.set_yscale('log')
return fig, ax
def plot_counts(values, ax=None,
xscale='lin', yscale='lin',
xParam=None, label=None, **kwargs):
"""
Plots the probability density function of given values.
Parameters
----------
values : numpy ndarray
the values for which the experimental pdf is computed
ax : matplotlib axes object, optional
axes to plot the figure in
xscale : str
'lin' or 'log', or ... (see binner.Bins for more details)
yscale : str
'lin' or 'log'
xParam : different things, optional
see binner.Bins for more details
**kwargs : kwargs
keyword arguments that will be passed to matplotlib hist
function
Returns
-------
fig : matplotlib Figure
the parent figure of the axes
ax : matplotlib Axes object
the axes in which the pdf is plotted
"""
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
else:
fig = ax.get_figure()
indices = bins.get_reasonable_data_indices_for_binning(
values, xscale=xscale)
prop_vals = values[indices]
if xParam is None:
if xscale == 'log':
xParam = np.sqrt(2) # just getting a nice factor of two...
if xscale == 'lin':
xParam = 50
xbins = bins.Bins(float, np.min(prop_vals),
np.max(prop_vals), xscale, xParam)
ax.hist(values, bins=xbins.bin_limits, normed=False, label=label, **kwargs)
if 'log' in xscale:
ax.set_xscale('log')
if 'log' in yscale:
ax.set_yscale('log')
return fig, ax
def _get_count_data_to_plot(x, y, xscale, yscale, xParam, yParam):
"""
See e.g. function plot_counts for interpreting the inner workings
of this function.
"""
if type(x) is not np.ndarray:
x = np.array(x)
if type(y) is not np.ndarray:
y = np.array(y)
xidx, yidx = bins.get_reasonable_data_indices_for_binning(
x, y, xscale, yscale)
min_x, max_x = min(x[xidx]), max(x[xidx])
min_y, max_y = min(y[yidx]), max(y[yidx])
bins2D = bins.Bins2D(
float, min_x, max_x, xscale, xParam,
float, min_y, max_y, yscale, yParam
)
counts_matrix, _, _ = np.histogram2d(
x, y, bins=bins2D.bin_limits)
# counts_matrix = counts_matrix.astype(np.float64)
counts_matrix = counts_matrix.T
counts_matrix = np.ma.masked_array(counts_matrix, counts_matrix == 0)
X, Y = bins2D.edge_grids
# compute bin average as a function of x:
x_bin_means, _, _ = scipy.stats.binned_statistic(
x, x, statistic='mean', bins=bins2D.xbin_lims
)
y_bin_means, _, _ = scipy.stats.binned_statistic(
x, y, statistic='mean', bins=bins2D.xbin_lims
)
return X, Y, counts_matrix, x_bin_means, y_bin_means, bins2D
def _get_count_data_to_plot(x, y, xscale, yscale, xParam, yParam):
"""
See e.g. function plot_counts for interpreting the inner workings
of this function.
"""
if type(x) is not np.ndarray:
x = np.array(x)
if type(y) is not np.ndarray:
y = np.array(y)
xidx, yidx = bins.get_reasonable_data_indices_for_binning(
x, y, xscale, yscale)
min_x, max_x = min(x[xidx]), max(x[xidx])
min_y, max_y = min(y[yidx]), max(y[yidx])
bins2D = bins.Bins2D(float, min_x, max_x, xscale, xParam, float, min_y,
max_y, yscale, yParam)
counts_matrix, _, _ = np.histogram2d(x, y, bins=bins2D.bin_limits)
# counts_matrix = counts_matrix.astype(np.float64)
counts_matrix = counts_matrix.T
counts_matrix = np.ma.masked_array(counts_matrix, counts_matrix == 0)
X, Y = bins2D.edge_grids
# compute bin average as a function of x:
x_bin_means, _, _ = scipy.stats.binned_statistic(x,
x,
statistic='mean',
bins=bins2D.xbin_lims)
y_bin_means, _, _ = scipy.stats.binned_statistic(x,
y,
statistic='mean',
bins=bins2D.xbin_lims)
return X, Y, counts_matrix, x_bin_means, y_bin_means, bins2D
