def plot_histogram(axes: mpl.axes.Axes, hist, popt, bin_edges: np.ndarray,
axis: str):
""" plots the histogram of one of the axis projections
Plot the projection of histogram of hits. The plot is plotted vertically
if the axis label is given as 'y'
Parameters
----------
axes : mpl.axes.Axes
the axes to plot the histogram into. The plot will be vertical if the
axis is specified to be 'y'
hist : np.ndarray
the histogramd hits
popt : np.ndarray
the parameters for the Gaussian that to be plotted over the hist
bin_edges : np.ndarray
the edges of the bins used for the histogram
axis : str
the axis on for which the results should be plotted (either 'x'
or 'y'). Plots vertically if 'y' is specified.
"""
bin_centers = (bin_edges[1:] + bin_edges[:-1])/2
if axis == 'x':
axes.set_xlim([bin_edges[0], bin_edges[-1]])
axes.hist(bin_edges[:-1], bin_edges, weights=hist, density=True)
axes.plot(bin_centers, norm.pdf(bin_centers, *popt))
elif axis == 'y':
axes.set_ylim([bin_edges[0], bin_edges[-1]])
axes.hist(bin_edges[:-1], bin_edges, weights=hist, density=True,
orientation='horizontal')
axes.plot(norm.pdf(bin_centers, *popt), bin_centers)
else:
raise ValueError("axis has to be either 'x' or 'y'")
def plot_histogram(data_numpy: np.ndarray,
xlabel: str,
bins: str = 40,
label: str = None,
color: str = 'grey',
ax: matplotlib.axes.Axes = None):
"""
Visualizes data as a histogram
Parameters:
data_numpy (numpy.ndarray): numpy ndarray of shape Nx1
xlabel (str): text that is displayed under the x axis
bins (int): number of bins in the histogram (default 40)
label (str): title of the histogram (default None)
color (str): color of the barplot
ax (matplotlib.axes.Axes): Axes to be used for plotting (default: create new)
Returns:
None
"""
if ax == None:
ax = plt.gca()
ax.hist(data_numpy, bins=bins, color=color, label=label)
ax.set_yticks([])
ax.set_ylabel("Frequency")
ax.set_xlabel(xlabel)
ax.tick_params(left=False, bottom=False)
def plot_ece_samples(ax: mpl.axes.Axes,
ground_truth_ece: float,
frequentist_ece,
samples_posterior: np.ndarray,
plot_kwargs: Dict[str, Any] = {}) -> mpl.axes.Axes:
"""
:param ax:
:param ground_truth_ece: float
:param frequentist_ece: float or np.ndarray
:param samples_posterior:
:param plot_kwargs:
:return:
"""
_plot_kwargs = DEFAULT_PLOT_KWARGS.copy()
_plot_kwargs.update(plot_kwargs)
if isinstance(frequentist_ece, float):
ax.axvline(x=frequentist_ece,
label='Frequentist',
color='blue',
**_plot_kwargs)
else:
ax.hist(frequentist_ece,
color='blue',
alpha=0.7,
label='Frequentist',
**_plot_kwargs)
ax.hist(samples_posterior,
color='red',
label='Bayesian',
alpha=0.7,
**_plot_kwargs)
ax.axvline(x=ground_truth_ece,
label='Ground truth',
color='black',
**_plot_kwargs)
ax.set_xlim(0, 0.3)
ax.set_xticks([0.0, 0.1, 0.2, 0.3])
return ax
def hist1d(foreground,
background,
axis: matplotlib.axes.Axes,
dataset_name: str,
normalize=True,
nbins: np.ndarray = np.linspace(-20, 20, 40),
norm_ymax=10,
log=False,
bg_label='total IMF'):
"""Plots 1D histogram of e.g. BxGSM.
Parameters
----------
foreground (array_like): data for the TPAs.
background (array_like): all the data for the IMF during the period of the dataset.
Returns
-------
fg_hist_values, bg_hist_values, bins
"""
foreground = foreground[~np.isnan(foreground)]
background = background[~np.isnan(background)]
axis.axvline(0, color="grey", lw=1, zorder=-1)
bg_hist_values, _, _ = axis.hist(background,
bins=nbins,
weights=np.ones_like(background) /
len(background),
label=bg_label,
histtype='step',
zorder=0)
fg_hist_values, bins, _ = axis.hist(foreground,
bins=nbins,
weights=np.ones_like(foreground) /
len(foreground),
label=dataset_name,
histtype='step',
zorder=1)
if normalize:
normalized_axis = axis.twinx(
) # instantiate a second axes that shares the same x-axis
normalized_axis.axhline(1, ls="--", color='lightgrey', lw=1)
masked_bg_hist_values = np.ma.masked_where(bg_hist_values == 0,
bg_hist_values)
normalized_axis.plot((bins[1:] + bins[:-1]) / 2,
fg_hist_values / masked_bg_hist_values,
c='g',
label='IMF normalized',
zorder=2)
normalized_axis.set_ylim(0, norm_ymax)
label = normalized_axis.set_ylabel('IMF normalized TPA distribution',
color='g')
label.set_color('g')
axis.legend(loc='upper left')
if log:
axis.set_xscale('log')
axis.set_ylabel('Probability Distribution')
axis.minorticks_on()
return fg_hist_values, bg_hist_values, bins
