Docstrings

gnssanalysis/gn_io/clk.py

@@ -71,7 +71,12 @@ def read_clk(clk_path):
 
 
 def get_sv_clocks(clk_df: _pd.DataFrame) -> _pd.Series:
-    """Retrieve satellite clocks from a CLK or SP3 dataframe"""
+    """Retrieve satellite clocks from a CLK or SP3 dataframe
+
+    :param _pd.DataFrame clk_df: CLK or SP3 dataframe where to retreive satellite clocks from
+    :raises IndexError: Raise error if the dataframe is not indexed correctly
+    :return _pd.Series: Retrieved satellite clocks
+    """
     if clk_df.index.names == ['A', 'J2000', 'CODE']:
         # fastest method to grab a specific category!, same as clk_df.EST.loc['AS'] but >6 times faster
         AS_cat_code = clk_df.index.levels[0].categories.get_loc("AS")

gnssanalysis/gn_io/sp3.py

@@ -214,7 +214,6 @@ def _process_sp3_block(
 ) -> _pd.DataFrame:
     """Process a single block of SP3 data.
 
-
     :param    str date: The date of the SP3 data block.
     :param    str data: The SP3 data block.
     :param    List[int] widths: The widths of the columns in the SP3 data block.
@@ -251,7 +250,6 @@ def read_sp3(
 ) -> _pd.DataFrame:
     """Reads an SP3 file and returns the data as a pandas DataFrame.
 
-
     :param str sp3_path: The path to the SP3 file.
     :param bool pOnly: If True, only P* values (positions) are included in the DataFrame. Defaults to True.
     :param bool nodata_to_nan: If True, converts 0.000000 (indicating nodata) to NaN in the SP3 POS column
@@ -474,7 +472,6 @@ def getVelPoly(sp3Df: _pd.DataFrame, deg: int = 35) -> _pd.DataFrame:
     :param DataFrame sp3Df: A pandas DataFrame containing the sp3 data.
     :param int deg: Degree of the polynomial fit. Default is 35.
     :return DataFrame: A pandas DataFrame with the interpolated velocities added as a new column.
-
     """
     est = sp3Df.unstack(1).EST[["X", "Y", "Z"]]
     x = est.index.get_level_values("J2000").values
@@ -580,7 +577,6 @@ def gen_sp3_content(
     Organises, formats (including nodata values), then writes out SP3 content to a buffer if provided, or returns
     it otherwise.
 
-    Args:
     :param pandas.DataFrame sp3_df: The DataFrame containing the SP3 data.
     :param bool sort_outputs: Whether to sort the outputs. Defaults to False.
     :param io.TextIOBase  buf: The buffer to write the SP3 content to. Defaults to None.

gnssanalysis/gn_transform.py

@@ -8,8 +8,18 @@
 from . import gn_const as _gn_const
 
 
-def gen_helm_aux(pt1, pt2, dropna = True):
-    """aux function for helmert values inversion."""
+def gen_helm_aux(
+    pt1: _np.ndarray,
+    pt2: _np.ndarray,
+    dropna: bool = True,
+) -> Tuple[_np.ndarray, _np.ndarray]:
+    """Aux function for helmert values inversion.
+
+    :param _np.ndarray pt1: The first set of points.
+    :param _np.ndarray pt2: The second set of points.
+    :param bool dropna: Whether to drop NaN values in input data, defaults to True.
+    :return Tuple[_np.ndarray, _np.ndarray]: A tuple containing the design matrix and right hand side of the equation for least square estimation.
+    """
     if dropna:
         mask = ~_np.isnan(pt1).any(axis=1) & ~_np.isnan(pt2).any(axis=1)
         pt1 = pt1[mask]
@@ -37,14 +47,19 @@ def gen_helm_aux(pt1, pt2, dropna = True):
     return A, rhs
 
 
-def get_helmert7(pt1: _np.ndarray, pt2: _np.ndarray, scale_in_ppm: bool = True, dropna: bool = True) -> Tuple[_np.ndarray, _np.ndarray]:
+def get_helmert7(
+    pt1: _np.ndarray,
+    pt2: _np.ndarray,
+    scale_in_ppm: bool = True,
+    dropna: bool = True,
+) -> list:
     """Inversion of 7 Helmert parameters between 2 sets of points.
 
     :param numpy.ndarray pt1: The first set of points.
     :param numpy.ndarray pt2: The second set of points.
     :param bool scale_in_ppm: Whether the scale parameter is in parts per million (ppm).
-    :param bool dropna: Whether to drop NaN values in input data.
-    :returns uple[np.ndarray, np.ndarray]: A tuple containing the Helmert parameters and the residuals.
+    :param bool dropna: Whether to drop NaN values in input data, defaults to True.
+    :returns list: A list containing the Helmert parameters and the residuals.
     """
     A, rhs = gen_helm_aux(pt1, pt2, dropna)
     sol = list(_np.linalg.lstsq(A, rhs, rcond=-1))  # parameters
@@ -67,14 +82,18 @@ def gen_rot_matrix(v):
     return mat + _np.eye(3)
 
 
-def transform7(xyz_in: _np.ndarray, hlm_params: _np.ndarray, scale_in_ppm: bool = True) -> _np.ndarray:
+def transform7(
+    xyz_in: _np.ndarray,
+    hlm_params: _np.ndarray,
+    scale_in_ppm: bool = True,
+) -> _np.ndarray:
     """
     Transformation of xyz vector with 7 helmert parameters.
 
-    :param xyz_in: The input xyz vector.
-    :param hlm_params: The 7 helmert parameters: [Tx, Ty, Tz, Rx, Ry, Rz, Scale].
-    :param scale_in_ppm: Whether the scale parameter is in parts per million (ppm).
-    :return: The transformed xyz vector.
+    :param _np.ndarray xyz_in: The input xyz vector.
+    :param _np.ndarray hlm_params: The 7 helmert parameters: [Tx, Ty, Tz, Rx, Ry, Rz, Scale].
+    :param bool scale_in_ppm: Whether the scale parameter is in parts per million (ppm).
+    :return _np.ndarray: The transformed xyz vector.
     """
     assert hlm_params.size == 7, "There must be exactly seven parameters"