.. _start-intro:
What is syncing?
****************
**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.
Installation
************
To install it use (with root privileges):
.. code:: console
$ pip install syncing
Or download the last git version and use (with root privileges):
.. code:: console
$ python setup.py install
Install extras
==============
Some 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:
.. code:: console
$ pip install syncing[all]
.. _end-quick:
Synchronising Laboratory Data
*****************************
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:
>>> from syncing.model import dsp
>>> model = dsp.register()
>>> model.plot(view=False)
SiteMap(...)
[graph]
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:
>>> 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(...)
[graph]
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])
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(...)
.. image:: setup-58plrwu3/pypi-1.*
Raw data
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"description": ".. _start-intro:\n\n\nWhat is syncing?\n****************\n\n**syncing** is an useful library to synchronise and re-sample time\nseries.\n\n**synchronisation** is based on the **fourier transform** and the\n**re-sampling** is performed with a specific interpolation method.\n\n\nInstallation\n************\n\nTo install it use (with root privileges):\n\n.. code:: console\n\n $ pip install syncing\n\nOr download the last git version and use (with root privileges):\n\n.. code:: console\n\n $ python setup.py install\n\n\nInstall extras\n==============\n\nSome additional functionality is enabled installing the following\nextras:\n\n* cli: enables the command line interface.\n\n* plot: enables to plot the model process and its workflow.\n\n* dev: installs all libraries plus the development libraries.\n\nTo install syncing and all extras (except development libraries), do:\n\n.. code:: console\n\n $ pip install syncing[all]\n\n.. _end-quick:\n\n\nSynchronising Laboratory Data\n*****************************\n\nThis example shows how to synchronise two data-sets *obd* and *dyno*\n(respectively they are the On-Board Diagnostics of a vehicle and\nChassis dynamometer) with a reference signal *ref*. To achieve this we\nuse the model syncing model to visualize the model:\n\n>>> from syncing.model import dsp\n>>> model = dsp.register()\n>>> model.plot(view=False)\nSiteMap(...)\n\n[graph]\n\nTip: You can explore the diagram by clicking on it.\n\nFirst of all, we generate synthetically the data-sets to feed the\nmodel:\n\n>>> import numpy as np\n>>> data_sets = {}\n>>> time = np.arange(0, 150, .1)\n>>> velocity = (1 + np.sin(time / 10)) * 60\n>>> data_sets['ref'] = dict(\n... time=time, # [10 Hz]\n... velocity=velocity / 3.6 # [m/s]\n... )\n>>> data_sets['obd'] = dict(\n... time=time[::10] + 12, # 1 Hz\n... velocity=velocity[::10] + np.random.normal(0, 5, 150), # [km/h]\n... engine_rpm=np.maximum(\n... np.random.normal(velocity[::10] * 3 + 600, 5), 800\n... ) # [RPM]\n... )\n>>> data_sets['dyno'] = dict(\n... time=time + 6.66, # 10 Hz\n... velocity=velocity + np.random.normal(0, 1, 1500) # [km/h]\n... )\n\nTo synchronise the data-sets and plot the workflow:\n\n>>> from syncing.model import dsp\n>>> sol = dsp(dict(\n... data=data_sets, x_label='time', y_label='velocity',\n... reference_name='ref', interpolation_method='cubic'\n... ))\n>>> sol.plot(view=False)\nSiteMap(...)\n\n[graph]\n\nFinally, we can analyze the time shifts and the synchronised and\nre-sampled data-sets:\n\n>>> import pandas as pd\n>>> import schedula as sh\n>>> pd.DataFrame(sol['shifts'], index=[0]) \n obd dyno\n...\n>>> df = pd.DataFrame(dict(sh.stack_nested_keys(sol['resampled'])))\n>>> df.columns = df.columns.map('/'.join)\n>>> df['ref/velocity'] *= 3.6\n>>> ax = df.set_index('ref/time').plot(secondary_y='obd/engine_rpm')\n>>> ax.set_ylabel('[km/h]'); ax.right_ax.set_ylabel('[RPM]')\nText(...)\n\n .. image:: setup-58plrwu3/pypi-1.*\n",
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