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1.0.9
- date
2023-11-15 12:10:00
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- keywords
data, synchronisation, re-sampling
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syncing is an useful library to synchronise and re-sample time series.
synchronisation is based on the fourier transform and the re-sampling is performed with a specific interpolation method.
To install it use (with root privileges):
$ pip install syncingOr download the last git version and use (with root privileges):
$ python setup.py installSome additional functionality is enabled installing the following extras:
- cli: enables the command line interface.
- plot: enables to plot the model process and its workflow.
- dev: installs all libraries plus the development libraries.
To install syncing and all extras (except development libraries), do:
$ pip install syncing[all]This example shows how to synchronise two data-sets obd and dyno (respectively they are the On-Board Diagnostics of a vehicle and Chassis dynamometer) with a reference signal ref. To achieve this we use the model syncing model to visualize the model:
model
>>> from syncing.model import dsp >>> model = dsp.register() >>> model.plot(view=False) SiteMap(...)
Tip
You can explore the diagram by clicking on it.
First of all, we generate synthetically the data-sets to feed the model:
>>> import numpy as np >>> data_sets = {} >>> time = np.arange(0, 150, .1) >>> velocity = (1 + np.sin(time / 10)) * 60 >>> data_sets['ref'] = dict( ... time=time, # [10 Hz] ... velocity=velocity / 3.6 # [m/s] ... ) >>> data_sets['obd'] = dict( ... time=time[::10] + 12, # 1 Hz ... velocity=velocity[::10] + np.random.normal(0, 5, 150), # [km/h] ... engine_rpm=np.maximum( ... np.random.normal(velocity[::10] * 3 + 600, 5), 800 ... ) # [RPM] ... ) >>> data_sets['dyno'] = dict( ... time=time + 6.66, # 10 Hz ... velocity=velocity + np.random.normal(0, 1, 1500) # [km/h] ... )
To synchronise the data-sets and plot the workflow:
sol
>>> from syncing.model import dsp >>> sol = dsp(dict( ... data=data_sets, x_label='time', y_label='velocity', ... reference_name='ref', interpolation_method='cubic' ... )) >>> sol.plot(view=False) SiteMap(...)
Finally, we can analyze the time shifts and the synchronised and re-sampled data-sets:
>>> import pandas as pd >>> import schedula as sh >>> pd.DataFrame(sol['shifts'], index=[0]) # doctest: +SKIP obd dyno ... >>> df = pd.DataFrame(dict(sh.stack_nested_keys(sol['resampled']))) >>> df.columns = df.columns.map('/'.join) >>> df['ref/velocity'] *= 3.6 >>> ax = df.set_index('ref/time').plot(secondary_y='obd/engine_rpm') >>> ax.set_ylabel('[km/h]'); ax.right_ax.set_ylabel('[RPM]') Text(...)
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