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        "author": "EnergieID.be",
        "author_email": "jan@energieid.be",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering"
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        "description": "# Cloogy\nPython client for Cloogy\n\n## See Demo.ipynb for a working Jupyter Notebook\n\n\n# 0. Install cloogy\n\nIn your terminal: `pip3 install cloogy`\n\nor in a python shell or notebook:\n\n```python\nimport pip\npip.main(['install', 'cloogy'])\n```\n\n\n```python\nimport yaml\n\nfrom cloogy import CloogyClient\n```\n\n# 1. Get your credentials\n\nGet your login and password.\n\nIn this example we'll load it from a YAML file.\n\n\n```python\nwith open('credentials.yaml', 'r') as f:\n    credentials = yaml.load(f)\n```\n\n\n```python\nlogin = credentials['login']\npassword = credentials['password']\n```\n\n# 2. Create a CloogyClient\n\nIf you supply a login and password, authentication will be handled for you.\n\n\n```python\nclient = CloogyClient(login=login, password=password)\n```\n\n# 3. List your Units\n\n\n```python\nunits = client.get_units()\n```\n\n\n```python\nunits\n```\n\n# 4. Get a specific Unit by ID\n\n\n```python\n# Lets grab the first ID from the list above\nunit_id = units[0]['Id']\nprint(unit_id)\n```\n\n\n```python\nunit = client.get_unit(unit_id=unit_id)\n```\n\n\n```python\nunit\n```\n\n# 5. Find out some stuff about your unit\n\nThe `Unit` class is an extension to the regular python dict. This means it behaves like a normal dict, but adds some extra features.\n\n\n```python\n# Get date and time of the last communication\nunit.get_last_communication_date()\n```\n\n# 6. List all available Tags for your login\n\n\n```python\nclient.get_tags()\n```\n\n# 7. List available Tags for a Unit\n\n\n```python\ntags = unit.get_tags()\n```\n\n\n```python\ntags\n```\n\n\n```python\n[tag['Id'] for tag in tags]\n```\n\n# 8. Get a specific Tag directly\n\n\n```python\ntag_id = tags[0]['Id']\nprint(tag_id)\n```\n\n\n```python\ntag = client.get_tag(tag_id=tag_id)\n```\n\n\n```python\ntag\n```\n\n\n```python\ntag.get_last_communication_date()\n```\n\n# 9. Get consumptions\n\n\n```python\n# pick a start and end time, as POSIX timestamp\n\nimport pandas as pd\nstart = int(pd.Timestamp('20180414').timestamp() * 1000)\nend = int(pd.Timestamp('20180416').timestamp() * 1000)\nprint(start, end)\n```\n\n\n```python\nclient.get_consumptions(\n    granularity='hourly', # can be Instant, Hourly, Daily, Monthly, Yearly\n    start=start,\n    end=end,\n    tags=[tag_id], # List of tag Id's\n    instants_type=None  # How instant measurements should be aggregated. Can be Avg, Max, Min, Stdev. Default is Avg.\n)\n```\n\n# 10. Get consumptions as a DataFrame\n\nFor some easy analysis, methods to get data as a Pandas DataFrame are included\n\nLet's say we want to analyse the active energy consumption, which has TagTypeId 20001\n\n\n```python\ntags = client.get_tags(where='TagTypeId=20001')\ntag_ids = [tag['Id'] for tag in tags]\n\nstart = pd.Timestamp('20180101')\nend = pd.Timestamp('20180417')\n```\n\n\n```python\nclient.get_consumptions_dataframe(granularity='monthly', start=start, end=end, tags=tag_ids)\n```\n\nA flat table like this is nice, but it can contain multiple TagIds, and it has way to many columns we don't need.\n\nWe can also get a table for only the readings:\n\n\n```python\ndf = client.get_readings_dataframe(granularity='monthly', start=start, end=end, tags=tag_ids, metric='Read')\n```\n\n\n```python\ndf\n```\n\n\n```python\n# make a plot!\n\n%matplotlib inline\ndf.plot.bar()\n```\n",
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        "license": "MIT",
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        "name": "cloogy",
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