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_counts(df_dev, figsize=(10, 6), y_scale=None):
"""
plots the trigger count of each device
"""
df = devices_trigger_count(df_dev.copy())
df.reset_index(level=0, inplace=True)
title = 'Count of on/off activations per Device'
col_label = 'trigger count'
col_device = 'device'
df.columns = ['device', col_label]
df = df.sort_values(by=col_label, axis=0, ascending=True)
# plot
fig, ax = plt.subplots(figsize=figsize)
plt.title(title)
plt.xlabel(col_label)
ax.barh(df['device'], df['trigger count'])
if y_scale == 'log':
ax.set_xscale('log')
return fig
def hist_counts(df_devs=None,
lst_devs=None,
df_tc=None,
figsize=None,
y_scale=None,
color=None,
order='count',
file_path=None):
"""
bar chart displaying how often activities are occurring
Parameters
----------
df_devs : pd.DataFrame, optional
recorded devices from a dataset. Fore more information refer to the
:ref:`user guide<device_dataframe>`.
lst_devs : lst of str, optional
A list of devices that are included in the statistic. The list can be a
subset of the recorded activities or contain activities that are not recorded.
df_tc : pd.DataFrame
A precomputed correlation table. If the *df_tcorr* parameter is given, parameters
*df_devs* and *lst_devs* are ignored. The transition table can be computed
in :ref:`stats <stats_devs_tcorr>`.
y_scale : {"log", "linear"}, default: None
The axis scale type to apply.
figsize : (float, float), default: None
width, height in inches. If not provided, the figsize is inferred by automatically.
color : str, optional
sets the primary color of the plot. When not set, the primary theming color is used.
Learn more about theming in the :ref:`user guide <theming>`
order : {'count', 'alphabetically', 'room'}, default='count'
determines the order in which the devices are listed.
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.plots import plot_device_bar_count
>>> plot_device_bar_count(data.df_devices)
.. image:: ../_static/images/plots/dev_bar_trigger.png
:height: 300px
:width: 500 px
:scale: 90 %
: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 df_tc is None)
assert y_scale in ['log', 'linear']
assert order in ['alphabetic', 'count', 'room']
title = 'Device triggers'
x_label = 'count'
df_col = 'trigger_count'
df = (devices_trigger_count(df_devs.copy(), lst_devs=lst_devs)
if df_tc is None else df_tc)
num_dev = len(df)
figsize = (_num_bars_2_figsize(num_dev) if figsize is None else figsize)
color = (get_primary_color() if color is None else color)
if order == 'alphabetic':
df = df.sort_values(by=[DEVICE], ascending=True)
elif order == 'count':
df = df.sort_values(by=[df_col])
else:
raise NotImplemented('the room order is going to be implemented')
# plot
fig, ax = plt.subplots(figsize=figsize)
plt.title(title)
plt.xlabel(x_label)
ax.barh(df[DEVICE], df[df_col], color=color)
if y_scale == 'log':
ax.set_xscale('log')
if file_path is not None:
savefig(fig, file_path)
return
else:
return fig