fix: remove duplicate ported_signal

Author: dshemetov

testing_utils/delphi_utils/check_covidcast_port.py

@@ -154,92 +154,6 @@ def check_metadata():
     assert_frame_equal(expected_df, df)
 
 
-def ported_signal(
-    data_source: str,
-    signal: str,  # pylint: disable=W0621
-    start_day: date = None,
-    end_day: date = None,
-    geo_type: str = "county",
-    geo_values: Union[str, Iterable[str]] = "*",
-    as_of: date = None,
-    lag: int = None,
-    time_type: str = "day",
-) -> Union[pd.DataFrame, None]:
-    """
-    Makes covidcast signal api call.
-
-    data_source: String identifying the data source to query, such as
-      ``"fb-survey"``.
-    signal: String identifying the signal from that source to query,
-      such as ``"smoothed_cli"``.
-    start_day: Query data beginning on this date. Provided as a
-      ``datetime.date`` object. If ``start_day`` is ``None``, defaults to the
-      first day data is available for this signal. If ``time_type == "week"``, then
-      this is rounded to the epiweek containing the day (i.e. the previous Sunday).
-    end_day: Query data up to this date, inclusive. Provided as a
-      ``datetime.date`` object. If ``end_day`` is ``None``, defaults to the most
-      recent day data is available for this signal. If ``time_type == "week"``, then
-      this is rounded to the epiweek containing the day (i.e. the previous Sunday).
-    geo_type: The geography type for which to request this data, such as
-      ``"county"`` or ``"state"``. Available types are described in the
-      COVIDcast signal documentation. Defaults to ``"county"``.
-    geo_values: The geographies to fetch data for. The default, ``"*"``,
-      fetches all geographies. To fetch one geography, specify its ID as a
-      string; multiple geographies can be provided as an iterable (list, tuple,
-      ...) of strings.
-    as_of: Fetch only data that was available on or before this date,
-      provided as a ``datetime.date`` object. If ``None``, the default, return
-      the most recent available data. If ``time_type == "week"``, then
-      this is rounded to the epiweek containing the day (i.e. the previous Sunday).
-    lag: Integer. If, for example, ``lag=3``, fetch only data that was
-      published or updated exactly 3 days after the date. For example, a row
-      with ``time_value`` of June 3 will only be included in the results if its
-      data was issued or updated on June 6. If ``None``, the default, return the
-      most recently issued data regardless of its lag.
-    time_type: The temporal resolution to request this data. Most signals
-      are available at the "day" resolution (the default); some are only
-      available at the "week" resolution, representing an MMWR week ("epiweek").
-    :returns: A Pandas data frame with matching data, or ``None`` if no data is
-      returned. Each row is one observation on one day in one geographic location.
-      Contains the following columns:
-    """
-    if start_day > end_day:
-        raise ValueError(
-            "end_day must be on or after start_day, but " f"start_day = '{start_day}', end_day = '{end_day}'"
-        )
-
-    if time_type == "day":
-        time_values = Epidata.range(start_day.strftime("%Y%m%d"), end_day.strftime("%Y%m%d"))
-    else:
-        time_values = Epidata.range(start_day.strftime("%Y%W"), end_day.strftime("%Y%W"))
-    response = Epidata.covidcast(
-        data_source,
-        signal,
-        time_type=time_type,
-        geo_type=geo_type,
-        time_values=time_values,
-        geo_value=geo_values,
-        as_of=as_of,
-        lag=lag,
-    )
-
-    if response["result"] != 1:
-        print(f"check {data_source} {signal}")
-        # Something failed in the API and we did not get real metadata
-        # raise RuntimeError("Error when fetching signal data from the API", response["message"])
-
-    api_df = pd.DataFrame.from_dict(response["epidata"])
-    if not api_df.empty:
-        time_type = api_df["time_type"].values[0]
-        api_df = _parse_datetimes(api_df, "time_value", time_type)
-        api_df = _parse_datetimes(api_df, "issue", time_type)
-        api_df.drop("direction", axis=1, inplace=True)
-        api_df["data_source"] = data_source
-        api_df["signal"] = signal
-
-    return api_df
-
-
 def generate_start_date_per_signal() -> Dict[Tuple[datetime, datetime, str], List[Tuple[str]]]:
     """
     Generate a dictionary of date range associated with individual signals