Added required packages for building docs

docs/requirements.txt

@@ -7,3 +7,4 @@ pocean-core>=3
 pynco
 sphinx
 sphinx-autodoc-typehints
+pooch

ioos_qc/argo.py

@@ -24,11 +24,11 @@ def pressure_increasing_test(inp):
 
     Ref: ARGO QC Manual: 8. Pressure increasing test
 
-    Args:
+    Args
     ----
         inp: Pressure values as a numeric numpy array or a list of numbers.
 
-    Returns:
+    Returns
     -------
         A masked array of flag values equal in size to that of the input.
 
@@ -72,7 +72,7 @@ def speed_test(
 
     Ref: ARGO QC Manual: 5. Impossible speed test
 
-    Args:
+    Args
     ----
         lon: Longitudes as a numeric numpy array or a list of numbers.
         lat: Latitudes as a numeric numpy array or a list of numbers.
@@ -85,7 +85,7 @@ def speed_test(
         fail_threshold: A float value representing a speed, in meters per second.
            Speeds exceeding this will be flagged as FAIL.
 
-    Returns:
+    Returns
     -------
         A masked array of flag values equal in size to that of the input.
 

ioos_qc/axds.py

@@ -39,7 +39,7 @@ def valid_range_test(
     treated as inclusive and the second span valid is treated as exclusive. To change this
     behavior you can use the parameters `start_inclusive` and `end_inclusive`.
 
-    Args:
+    Args
     ----
         inp (Sequence[any]): Data as a sequence of objects compatible with the fail_span objects
         fail_span (Tuple[any, any]): 2-tuple range which to flag outside data as FAIL. Objects
@@ -52,7 +52,7 @@ def valid_range_test(
         end_inclusive (bool): Optional. If the ending span value should be inclusive (True) or
             exclusive (False).
 
-    Returns:
+    Returns
     -------
         np.ma.core.MaskedArray: A masked array of flag values equal in size to that of the input.
 

ioos_qc/config.py

@@ -182,7 +182,7 @@ def run(self, **passedkwargs):
 def extract_calls(source) -> List[Call]:
     """Extracts call objects from a source object.
 
-    Args:
+    Args
     ----
         source ([any]): The source of Call objects, this can be a:
             * Call object
@@ -191,7 +191,7 @@ def extract_calls(source) -> List[Call]:
             * NewConfig object
             * Object with the 'calls' attribute
 
-    Returns:
+    Returns
     -------
         List[Call]: List of extracted Call objects
 
@@ -234,7 +234,7 @@ def __init__(
         version=None,
         default_stream_key="_stream",
     ) -> None:
-        """Args:
+        """Args
         ----
             source: The QC configuration representation in one of the following formats:
                 python dict or odict
@@ -349,7 +349,7 @@ def add(self, source) -> None:
         types of objects accepted as the source parameter. The changes the internal .calls
         attribute and returns None.
 
-        Args:
+        Args
         ----
             source ([any]): The source of Call objects, this can be a:
                 * Call object
@@ -489,7 +489,7 @@ def add(self, source) -> None:
         types of objects accepted as the source parameter. The changes the internal .calls
         attribute and returns None.
 
-        Args:
+        Args
         ----
             source ([any]): The source of Call objects, this can be a:
                 * Call object
@@ -522,7 +522,7 @@ def __init__(self, source, default_stream_key="_stream") -> None:
         stream. This just sets up a stream with the name passed in as the "default_stream_key"
         parameter.
 
-        Args:
+        Args
         ----
             source: The QC configuration representation in one of the following formats:
                 python dict or odict

ioos_qc/config_creator/config_creator.py

@@ -178,7 +178,7 @@ def __str__(self) -> str:
 class QcVariableConfig(dict):
     """Used to generate a QcConfig for a specific variable.
 
-    Args:
+    Args
     ----
         path_or_dict: QcVariableConfig configuration, one of the following formats:
             python dict
@@ -253,11 +253,11 @@ def __str__(self) -> str:
 class QcConfigCreator:
     """Creates level-0 configuration to create QcQonfig.
 
-    Arguments:
+    Arguments
     ---------
         creator_config (QcCreatorConfig): Configuration for datasets and variables used to create qc_config.
 
-    Attributes:
+    Attributes
     ----------
         allowed_stats (list): Specific statistics allowed to be used to configure each test.
         allowed_operators (list): Operators allowed to used to configure each test.
@@ -272,11 +272,11 @@ def __init__(self, creator_config) -> None:
     def create_config(self, variable_config):
         """Create QARTOD QC config given QcVariableConfig.
 
-        Args:
+        Args
         ----
             variable_config (QcVariableConfig): Config for variable to be quality controlled
 
-        Returns:
+        Returns
         -------
             qc_config (dict): Config for ioos_qc
 

ioos_qc/qartod.py

@@ -63,11 +63,11 @@ def qartod_compare(
 ) -> np.ma.MaskedArray:
     """Aggregates an array of flags by precedence into a single array.
 
-    Args:
+    Args
     ----
         vectors: An array of uniform length arrays representing individual flags
 
-    Returns:
+    Returns
     -------
         A masked array of aggregated flag data.
 
@@ -114,14 +114,14 @@ def location_test(
     also use a unit from the quantities library. Missing and masked data is
     flagged as UNKNOWN.
 
-    Args:
+    Args
     ----
         lon: Longitudes as a numeric numpy array or a list of numbers.
         lat: Latitudes as a numeric numpy array or a list of numbers.
         bbox: A length 4 tuple expressed in (minx, miny, maxx, maxy) [optional].
         range_max: Maximum allowed range expressed in geodesic curve distance (meters).
 
-    Returns:
+    Returns
     -------
         A masked array of flag values equal in size to that of the input.
 
@@ -192,13 +192,13 @@ def gross_range_test(
     range as FAIL data.  Optionally also flag data which falls outside of a user
     defined range as SUSPECT. Missing and masked data is flagged as UNKNOWN.
 
-    Args:
+    Args
     ----
         inp: Input data as a numeric numpy array or a list of numbers.
         fail_span: 2-tuple range which to flag outside data as FAIL.
         suspect_span: 2-tuple range which to flag outside data as SUSPECT. [optional]
 
-    Returns:
+    Returns
     -------
         A masked array of flag values equal in size to that of the input.
 
@@ -241,7 +241,7 @@ def gross_range_test(
 class ClimatologyConfig:
     """Objects to hold the config for a Climatology test.
 
-    Args:
+    Args
     ----
         tspan: 2-tuple range.
                If period is defined, then this is a numeric range.
@@ -283,7 +283,7 @@ def members(self):
         return self._members
 
     def values(self, tind: pd.Timestamp, zind=None):
-        """Args:
+        """Args
         ----
             tind: Value to test for inclusion between time bounds
 
@@ -469,7 +469,7 @@ def climatology_test(
 
     Data for which no ClimatologyConfig member exists is marked as UNKNOWN.
 
-    Args:
+    Args
     ----
         config: A ClimatologyConfig object or a list of dicts containing tuples
             that can be used to create a ClimatologyConfig object. See ClimatologyConfig
@@ -481,7 +481,7 @@ def climatology_test(
           If anything else is passed in the format is assumed to be seconds since the unix epoch.
         zinp: Z (depth) data, in meters positive down, as a numeric numpy array or a list of numbers.
 
-    Returns:
+    Returns
     -------
         A masked array of flag values equal in size to that of the input.
 
@@ -520,7 +520,7 @@ def spike_test(
     method: str = "average",
 ) -> np.ma.core.MaskedArray:
     """Check for spikes by checking neighboring data against thresholds.
-
+    
     Determine if there is a spike at data point n-1 by subtracting
     the midpoint of n and n-2 and taking the absolute value of this
     quantity, and checking if it exceeds a low or high threshold (default).
@@ -529,21 +529,20 @@ def spike_test(
     and values which exceed the high threshold are flagged FAIL.
     Missing and masked data is flagged as UNKNOWN.
 
-    Args:
+    Args
     ----
         inp: Input data as a numeric numpy array or a list of numbers.
         suspect_threshold: The SUSPECT threshold value, in observations units.
         fail_threshold: The SUSPECT threshold value, in observations units.
         method: ['average'(default),'differential'] optional input to assign the method used to detect spikes.
             * "average": Determine if there is a spike at data point n-1 by subtracting
-            the midpoint of n and n-2 and taking the absolute value of this
-            quantity, and checking if it exceeds a low or high threshold.
-            -
+                        the midpoint of n and n-2 and taking the absolute value of this
+                        quantity, and checking if it exceeds a low or high threshold.
             * "differential": Determine if there is a spike at data point n by calculating the difference
-            between n and n-1 and n+1 and n variation. To considered, (n - n-1)*(n+1 - n) should
-            be smaller than zero (in opposite direction).
+                              between n and n-1 and n+1 and n variation. To considered, (n - n-1)*(n+1 - n) should
+                              be smaller than zero (in opposite direction).
 
-    Returns:
+    Returns
     -------
         A masked array of flag values equal in size to that of the input.
 
@@ -620,17 +619,17 @@ def rate_of_change_test(
     exceed. Threshold is expressed as a rate in observations units per second.
     Missing and masked data is flagged as UNKNOWN.
 
-    Args:
+    Args
     ----
         inp: Input data as a numeric numpy array or a list of numbers.
         tinp: Time data as a sequence of datetime objects compatible with pandas DatetimeIndex.
               This includes numpy datetime64, python datetime objects and pandas Timestamp object.
-              ie. pd.DatetimeIndex([datetime.utcnow(), np.datetime64(), pd.Timestamp.now()]
+              ie. pd.DatetimeIndex([datetime.utcnow(), np.datetime64(), pd.Timestamp.now()])
               If anything else is passed in the format is assumed to be seconds since the unix epoch.
         threshold: A float value representing a rate of change over time,
                    in observation units per second.
 
-    Returns:
+    Returns
     -------
         A masked array of flag values equal in size to that of the input.
 
@@ -678,7 +677,7 @@ def flat_line_test(
     Missing and masked data is flagged as UNKNOWN.
     More information: https://github.com/ioos/ioos_qc/pull/11.
 
-    Args:
+    Args
     ----
         inp: Input data as a numeric numpy array or a list of numbers.
         tinp: Time data as a sequence of datetime objects compatible with pandas DatetimeIndex.
@@ -694,7 +693,7 @@ def flat_line_test(
             point `n`, and calculate the range of values in the window. If that range is less than `tolerance`,
             then the point is flagged.
 
-    Returns:
+    Returns
     -------
         A masked array of flag values equal in size to that of the input.
 
@@ -778,7 +777,7 @@ def attenuated_signal_test(
 
     Missing and masked data is flagged as UNKNOWN.
 
-    Args:
+    Args
     ----
         inp: Input data as a numeric numpy array or a list of numbers.
         tinp: Time input data as a numpy array of dtype `datetime64`.
@@ -797,7 +796,7 @@ def attenuated_signal_test(
         check_type: Either 'std' (default) or 'range', depending on the type of check
             you wish to perform.
 
-    Returns:
+    Returns
     -------
         A masked array of flag values equal in size to that of the input.
         This array will always contain only a single unique value since all
@@ -882,7 +881,7 @@ def density_inversion_test(
 
     Ref: Manual for Real-Time Quality Control of in-situ Temperature and Salinity Data, Version 2.0, January 2016
 
-    Args:
+    Args
     ----
         inp: Potential density values as a numeric numpy array or a list of numbers.
         zinp: Corresponding depth/pressure values for each density.
@@ -891,7 +890,7 @@ def density_inversion_test(
         fail_threshold:  A float value representing a maximum potential density(or sigma0)
             variation to be tolerated, downward density variation exceeding this will be flagged as FAIL.
 
-    Returns:
+    Returns
     -------
         A masked array of flag values equal in size to that of the input.
 

ioos_qc/utils.py

@@ -211,7 +211,7 @@ def check_timestamps(
     proper order and optionally do not have large gaps prior to processing the
     data.
 
-    Args:
+    Args
     ----
         times: Input array of timestamps
         max_time_interval: The interval between values should not exceed this