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        "author": "Vincenzo Arcidiacono",
        "author_email": "vinci1it2000@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: European Union Public Licence 1.1 (EUPL 1.1)",
            "Natural Language :: English",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: OS Independent",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Utilities"
        ],
        "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**Synchronization** 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::\n\n   $ pip install syncing\n\nOr download the last git version and use (with root privileges):\n\n::\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::\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..\n\n   >>> from syncing.model import dsp\n   >>> model = dsp.register()\n   >>> model.plot(view=False)\n   SiteMap(...)\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 synchronize the data-sets and plot the workflow:\n\n..\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)\n   SiteMap(...)\n\n   [graph]\n\nFinally, we can analyze the time shifts and the synchronized and\nre-sampled data-sets:\n\n>>> import pandas as pd\n>>> import schedula as sh\n>>> pd.DataFrame(sol['shifts'], index=[0])  # doctest: +SKIP\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\n",
        "description_content_type": "",
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        "keywords": "python,utility,library,synchronize,data,scientific,engineering,resample,time series",
        "license": "EUPL 1.1+",
        "maintainer": "",
        "maintainer_email": "",
        "name": "syncing",
        "package_url": "https://pypi.org/project/syncing/",
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        "project_url": "https://pypi.org/project/syncing/",
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            "Donate": "https://donorbox.org/syncing",
            "Download": "https://github.com/vinci1it2000/syncing/tarball/v1.0.4",
            "Homepage": "https://github.com/vinci1it2000/syncing",
            "Issue tracker": "https://github.com/vinci1it2000/syncing/issues"
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