def test_stats_devices(self):
from pyadlml.dataset.stats.devices import device_tcorr, devices_trigger_count, \
device_triggers_one_day, trigger_time_diff, devices_on_off_stats, duration_correlation
df = self.data.df_devices
lst = self.data.lst_devices
# tcorr = device_tcorr(df)
tcorr = device_tcorr(df, lst)
assert tcorr.shape[0] == len(lst) and tcorr.shape[1] == len(lst)
trigg_c = devices_trigger_count(df)
assert len(trigg_c) == self.num_rec_devs
trigg_c = devices_trigger_count(df, lst)
assert len(trigg_c) == len(lst)
trigg_od = device_triggers_one_day(df)
assert len(trigg_od.columns) == self.num_rec_devs
trigg_od = device_triggers_one_day(df, lst)
assert len(trigg_od.columns) == len(lst)
trigg_td = trigger_time_diff(df)
assert len(trigg_td) == len(df) - 1
onoff = devices_on_off_stats(df)
assert len(onoff) == self.num_rec_binary_devs
onoff = devices_on_off_stats(df, lst)
assert len(onoff) == len(lst)
dc = duration_correlation(df)
assert len(dc) == self.num_rec_binary_devs
dc = duration_correlation(df, lst)
assert len(dc) == len(lst)
def hist_trigger_time_diff(df_devs=None,
x=None,
n_bins=50,
figsize=(10, 6),
color=None,
file_path=None):
"""
plots
Parameters
----------
df_devs : pd.DataFrame, optional
recorded devices from a dataset. Fore more information refer to the
:ref:`user guide<device_dataframe>`.
x : ndarray, optional
array of time deltas used to plot the histogram
n_bins : int, default=50
the number of bins for the histogram.
color : str, optional
sets the color of the plot. When not set, the primary theming color is used.
Learn more about theming in the :ref:`user guide <theming>`
figsize : (float, float), default: None
width, height in inches. If not provided, the figsize is inferred by automatically.
file_path : str, optional
If set, saves the plot under the given file path and return *None* instead
of returning the figure.
Examples
--------
>>> from pyadlml.plot import plot_trigger_time_dev
>>> plot_trigger_time_dev_todo(data.df_devices)
.. image:: ../_static/images/plots/dev_hist_trigger_td.png
:height: 300px
:width: 500 px
:scale: 100 %
:alt: alternate text
:align: center
Returns
-------
res : fig or None
Either a figure if file_path is not specified or nothing.
"""
assert not (df_devs is None and x is None)
title = 'Time difference between succeeding device'
log_sec_col = 'total_log_secs'
sec_col = 'total_secs'
ylabel = 'count'
ax2label = 'cummulative percentage'
ax1label = 'timedeltas count '
xlabel = 'log seconds'
color = (get_primary_color() if color is None else color)
color2 = get_secondary_color()
if x is None:
X = trigger_time_diff(df_devs.copy())
else:
X = x
# make equal bin size from max to min
bins = np.logspace(min(np.log10(X)), max(np.log10(X)), n_bins)
# make data ready for hist
hist, _ = np.histogram(X, bins=bins)
cum_percentage = hist.cumsum() / hist.sum()
cum_percentage = np.concatenate(
([0], cum_percentage)) # let the array start with 0
# plots
fig, ax = plt.subplots(figsize=figsize)
plt.xscale('log')
ax.hist(X, bins=bins, label=ax1label, color=color)
ax.set_ylabel(ylabel)
ax.set_xlabel(xlabel)
# create axis for line
ax2 = ax.twinx()
ax2.plot(bins, cum_percentage, 'r', label=ax2label, color=color2)
ax2.set_ylabel('%')
ax2.set_xscale('log')
ax_top = ax.secondary_xaxis('top', functions=(lambda x: x, lambda x: x))
ax_top.xaxis.set_major_formatter(
ticker.FuncFormatter(func_formatter_seconds2time))
# plot single legend for multiple axis
h1, l1 = ax.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
ax.legend(h1 + h2, l1 + l2, loc='center right')
plt.title(title, y=1.08)
if file_path is not None:
savefig(fig, file_path)
return
else:
return fig