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 heatmap_cross_correlation(df_dev, figsize=(10, 8)):
""" plots the cross correlation between the device signals
Parameters
----------
df_dev: pd.DataFrame
devices in representation 1
"""
cmap = 'BrBG'
cbarlabel = 'counts'
title = 'Cross-correlation of signals'
ct = duration_correlation(df_dev)
vals = ct.values.T
devs = list(ct.index)
fig, ax = plt.subplots(figsize=figsize)
im, cbar = heatmap_square(vals,
devs,
devs,
ax=ax,
cmap=cmap,
cbarlabel=cbarlabel,
vmin=-1,
vmax=1)
texts = annotate_heatmap(im,
textcolors=("black", "white"),
threshold=0.5,
valfmt="{x:.2f}")
ax.set_title(title)
fig.tight_layout()
plt.show()
def heatmap_cross_correlation(df_devs=None,
lst_devs=None,
df_dur_corr=None,
figsize=None,
numbers=None,
file_path=None):
"""
Plots the cross correlation between the device signals
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_tcorr : 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>`.
figsize : (float, float), default: None
width, height in inches. If not provided, the figsize is inferred by automatically.
numbers : bool, default: True
Whether to display numbers inside the heatmaps fields or not.
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_dev_hm_similarity
>>> plot_dev_hm_similarity(data.df_devices)
.. image:: ../_static/images/plots/dev_hm_dur_cor.png
:height: 400px
: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_dur_corr is None)
title = 'Devices cross-correlation'
cmap = 'RdBu'
cbarlabel = 'similarity'
if df_dur_corr is None:
ct = duration_correlation(df_devs, lst_devs=lst_devs)
else:
ct = df_dur_corr
ct = ct.replace(pd.NA, np.inf)
vals = ct.values.T
devs = list(ct.index)
num_dev = len(devs)
figsize = (_num_items_2_heatmap_square_figsize_ver2(num_dev)
if figsize is None else figsize)
fig, ax = plt.subplots(figsize=figsize)
im, cbar = heatmap_square(vals,
devs,
devs,
ax=ax,
cmap=cmap,
cbarlabel=cbarlabel,
vmin=-1,
vmax=1)
if numbers is None:
if num_dev < 15:
valfmt = "{x:.2f}"
texts = annotate_heatmap(im,
textcolors=("black", "white"),
threshold=0.5,
valfmt=valfmt)
elif num_dev < 30:
valfmt = "{x:.1f}"
texts = annotate_heatmap(im,
textcolors=("black", "white"),
threshold=0.5,
valfmt=valfmt)
if numbers:
texts = annotate_heatmap(im,
textcolors=("black", "white"),
threshold=0.5,
valfmt="{x:.2f}")
ax.set_title(title)
fig.tight_layout()
if file_path is not None:
savefig(fig, file_path)
return
else:
return fig