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Dynamic code generation
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‎.gitignore

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build/
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docs/_build
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env/
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venv/

‎docs/conf.py

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"sphinx.ext.autodoc",
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"sphinx_autodoc_typehints",
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# 'matplotlib.sphinxext.plot_directive'
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"sphinx_exec_directive"
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]
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# Add any paths that contain templates here, relative to this directory.

‎docs/covidcast_examples.rst

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‎docs/getting_started_with_epidatpy.rst

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@@ -13,11 +13,15 @@ Setup
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You can install the stable version of this package from PyPi:
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>>> pip install epidatpy
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.. code-block:: sh
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pip install epidatpy
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Or if you want the development version, install from GitHub:
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>>> pip install -e "git+https://github.com/cmu-delphi/epidatpy.git#egg=epidatpy"
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.. code-block:: sh
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pip install -e "git+https://github.com/cmu-delphi/epidatpy.git#egg=epidatpy"
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**API Keys**
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@@ -48,29 +52,38 @@ the location and times of interest.
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The ``pub_covidcast`` function lets us access the ``covidcast`` endpoint:
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>>> from epidatpy import EpiDataContext, EpiRange
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>>> epidata = EpiDataContext(use_cache=True, cache_max_age_days=1)
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>>> # Obtain the most up-to-date version of the smoothed covid-like illness (CLI)
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>>> # signal from the COVID-19 Trends and Impact survey for the US
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>>> apicall = epidata.pub_covidcast(
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... data_source = "fb-survey",
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... signals = "smoothed_cli",
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... geo_type = "nation",
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... time_type = "day",
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... geo_values = "us",
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... time_values = EpiRange(20210405, 20210410))
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EpiDataCall(endpoint=covidcast/, params={'data_source': 'fb-survey', 'signals': 'smoothed_cli', 'geo_type': 'nation', 'time_type': 'day', 'geo_values': 'us', 'time_values': '20210405-20210410'})
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.. exec::
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:context: true
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from epidatpy import EpiDataContext, EpiRange
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import pandas as pd
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# Set common options and context
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pd.set_option('display.max_columns', None)
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pd.set_option('display.max_rows', None)
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pd.set_option('display.width', 1000)
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epidata = EpiDataContext(use_cache=False)
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# Obtain the most up-to-date version of the smoothed covid-like illness (CLI)
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# signal from the COVID-19 Trends and Impact survey for the US
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apicall = epidata.pub_covidcast(
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data_source = "fb-survey",
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signals = "smoothed_cli",
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geo_type = "nation",
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time_type = "day",
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geo_values = "us",
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time_values = EpiRange(20210405, 20210410))
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print(apicall)
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``pub_covidcast`` returns an ``EpiDataCall``, which can be further converted into different output formats - such as a Pandas DataFrame:
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>>> data = apicall.df()
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>>> data.head()
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source signal geo_type geo_value time_type time_value issue lag value stderr sample_size direction missing_value missing_stderr missing_sample_size
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0 fb-survey smoothed_cli nation us day 2021-04-05 2021-04-10 5 0.675832 0.014826 244046 <NA> 0 0 0
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1 fb-survey smoothed_cli nation us day 2021-04-06 2021-04-11 5 0.690687 0.014998 242979 <NA> 0 0 0
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2 fb-survey smoothed_cli nation us day 2021-04-07 2021-04-12 5 0.690664 0.015023 242153 <NA> 0 0 0
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3 fb-survey smoothed_cli nation us day 2021-04-08 2021-04-13 5 0.706503 0.015236 241380 <NA> 0 0 0
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4 fb-survey smoothed_cli nation us day 2021-04-09 2021-04-14 5 0.724306 0.015466 240256 <NA> 0 0 0
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.. exec::
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:context: true
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data = apicall.df()
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print(data.head())
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Each row represents one observation in the US on one
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day. The geographical abbreviation is given in the ``geo_value`` column, the date in
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access data at all locations. Check the help for a given endpoint to see if
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it supports ``*``.)
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>>> apicall = epidata.pub_covidcast(
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... data_source = "fb-survey",
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... signals = "smoothed_cli",
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... geo_type = "state",
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... time_type = "day",
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... geo_values = "*",
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... time_values = EpiRange(20210405, 20210410))
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EpiDataCall(endpoint=covidcast/, params={'data_source': 'fb-survey', 'signals': 'smoothed_cli', 'geo_type': 'state', 'time_type': 'day', 'geo_values': '*', 'time_values': '20210405-20210410'})
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>>> apicall.df.head()
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source signal geo_type geo_value time_type time_value issue lag value stderr sample_size direction missing_value missing_stderr missing_sample_size
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0 fb-survey smoothed_cli state ak day 2021-04-05 2021-04-10 5 0.736883 0.275805 720.0 <NA> 0 0 0
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1 fb-survey smoothed_cli state al day 2021-04-05 2021-04-10 5 0.796627 0.137734 3332.1117 <NA> 0 0 0
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2 fb-survey smoothed_cli state ar day 2021-04-05 2021-04-10 5 0.561916 0.131108 2354.9911 <NA> 0 0 0
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3 fb-survey smoothed_cli state az day 2021-04-05 2021-04-10 5 0.62283 0.105354 4742.2778 <NA> 0 0 0
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4 fb-survey smoothed_cli state ca day 2021-04-05 2021-04-10 5 0.444169 0.040576 21382.3806 <NA> 0 0 0
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.. exec::
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:context: true
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apicall = epidata.pub_covidcast(
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data_source = "fb-survey",
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signals = "smoothed_cli",
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geo_type = "state",
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time_type = "day",
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geo_values = "*",
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time_values = EpiRange(20210405, 20210410))
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print(apicall)
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print(apicall.df().head())
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We can fetch a subset of states by listing out the desired locations:
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>>> apicall = epidata.pub_covidcast(
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... data_source = "fb-survey",
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... signals = "smoothed_cli",
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... geo_type = "state",
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... time_type = "day",
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... geo_values = "pa,ca,fl",
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... time_values = EpiRange(20210405, 20210410))
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EpiDataCall(endpoint=covidcast/, params={'data_source': 'fb-survey', 'signals': 'smoothed_cli', 'geo_type': 'state', 'time_type': 'day', 'geo_values': 'pa,ca,fl', 'time_values': '20210405-20210410'})
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>>> apicall.df.head()
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source signal geo_type geo_value time_type time_value issue lag value stderr sample_size direction missing_value missing_stderr missing_sample_size
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0 fb-survey smoothed_cli state ca day 2021-04-05 2021-04-10 5 0.444169 0.040576 21382.3806 <NA> 0 0 0
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1 fb-survey smoothed_cli state fl day 2021-04-05 2021-04-10 5 0.690415 0.058204 16099.0005 <NA> 0 0 0
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2 fb-survey smoothed_cli state pa day 2021-04-05 2021-04-10 5 0.715758 0.072999 10894.0057 <NA> 0 0 0
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3 fb-survey smoothed_cli state ca day 2021-04-06 2021-04-11 5 0.45604 0.04127 21176.3902 <NA> 0 0 0
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4 fb-survey smoothed_cli state fl day 2021-04-06 2021-04-11 5 0.730692 0.059907 15975.0007 <NA> 0 0 0
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.. exec::
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:context: true
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apicall = epidata.pub_covidcast(
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data_source = "fb-survey",
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signals = "smoothed_cli",
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geo_type = "state",
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time_type = "day",
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geo_values = "pa,ca,fl",
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time_values = EpiRange(20210405, 20210410))
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print(apicall)
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print(apicall.df().head())
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We can also request data for a single location at a time, via the ``geo_values`` argument.
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>>> apicall = epidata.pub_covidcast(
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... data_source = "fb-survey",
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... signals = "smoothed_cli",
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... geo_type = "state",
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... time_type = "day",
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... geo_values = "pa,ca,fl",
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... time_values = EpiRange(20210405, 20210410))
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EpiDataCall(endpoint=covidcast/, params={'data_source': 'fb-survey', 'signals': 'smoothed_cli', 'geo_type': 'state', 'time_type': 'day', 'geo_values': 'pa', 'time_values': '20210405-20210410'})
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>>> apicall.df.head()
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source signal geo_type geo_value time_type time_value issue lag value stderr sample_size direction missing_value missing_stderr missing_sample_size
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0 fb-survey smoothed_cli state pa day 2021-04-05 2021-04-10 5 0.715758 0.072999 10894.0057 <NA> 0 0 0
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1 fb-survey smoothed_cli state pa day 2021-04-06 2021-04-11 5 0.69321 0.070869 10862.0055 <NA> 0 0 0
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2 fb-survey smoothed_cli state pa day 2021-04-07 2021-04-12 5 0.685934 0.070654 10790.0054 <NA> 0 0 0
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3 fb-survey smoothed_cli state pa day 2021-04-08 2021-04-13 5 0.681511 0.071394 10731.0044 <NA> 0 0 0
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4 fb-survey smoothed_cli state pa day 2021-04-09 2021-04-14 5 0.709416 0.072162 10590.0049 <NA> 0 0 0
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.. exec::
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:context: true
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apicall = epidata.pub_covidcast(
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data_source = "fb-survey",
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signals = "smoothed_cli",
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geo_type = "state",
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time_type = "day",
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geo_values = "pa",
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time_values = EpiRange(20210405, 20210410))
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print(apicall)
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print(apicall.df().head())
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Getting versioned data
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forecasting models. To fetch versioned data, we can use the ``as_of``
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argument:
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>>> apicall = epidata.pub_covidcast(
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... data_source = "fb-survey",
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... signals = "smoothed_cli",
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... geo_type = "state",
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... time_type = "day",
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... geo_values = "pa,ca,fl",
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... time_values = EpiRange(20210405, 20210410),
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... as_of = "2021-06-01")
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.. exec::
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:context: true
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apicall = epidata.pub_covidcast(
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data_source = "fb-survey",
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signals = "smoothed_cli",
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geo_type = "state",
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time_type = "day",
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geo_values = "pa",
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time_values = EpiRange(20210405, 20210410),
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as_of = "2021-06-01")
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print(apicall)
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print(apicall.df().head())
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Plotting
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--------
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Because the output data is a standard Pandas DataFrame, we can easily plot
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it using any of the available Python libraries:
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>>> data.plot(x="time_value", y="value", title="Smoothed CLI from Facebook Survey", xlabel="Date", ylabel="CLI")
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.. code-block:: python
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data.plot(x="time_value", y="value", title="Smoothed CLI from Facebook Survey", xlabel="Date", ylabel="CLI")
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.. image:: images/Figure_1.png
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:width: 800
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:alt: Smoothed CLI from Facebook Survey
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:width: 800
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:alt: Smoothed CLI from Facebook Survey
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Finding locations of interest
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-----------------------------

‎docs/index.rst

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getting_started
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covidcast_examples
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signals_covid
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getting_started_with_epidatpy
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epidatpy
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getting_started_with_epidatpy
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signal_discovery
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versioned_data

‎docs/signals_covid.rst

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