remove tgz v2 copy

nrcatalogtools/waveform.py

@@ -248,140 +248,6 @@ def load_from_targz(cls, file_path, metadata={}, verbosity=0):
             **w_attributes,
         )
 
-    @classmethod
-    def load_from_targz_v2(cls, file_path, metadata={}, verbosity=0):
-        """Method to load SWSH waveform modes from RIT
-
-        Args:
-            file_path_or_open_file (str or open file): Either the path to a tar file containing
-                the ascii modes data
-            metadata (dict): Dictionary containing metadata (Note that keys
-                will be NR group specific)
-            verbosity (int, optional): Verbosity level with which to
-                print messages during execution. Defaults to 0.
-
-        Raises:
-            RuntimeError: If inputs are invalid, or if no mode found in
-                input file.
-
-        Returns:
-            WaveformModes: Object containing time-series of SWSH modes.
-        """
-
-        if not os.path.exists(file_path) or os.path.getsize(file_path) == 0:
-            raise RuntimeError(f"Could not use or open {file_path}")
-
-        import quaternionic
-        import re
-        import tarfile
-
-        def get_tag(name):
-            return os.path.splitext(os.path.splitext(os.path.basename(name))[0])[0]
-
-        def get_el_em_from_filename(filename: str):
-            substr = re.search(pattern=r"l\d_m\d", string=filename)
-            if substr is None:
-                substr = re.search(pattern=r"l\d_m-\d", string=filename)
-            elem = substr[0].split("_")
-            return (int(elem[0].strip("l")), int(elem[1].strip("m")))
-
-        # Set the file path attribute
-        cls._filepath = file_path
-
-        # If _metadata is not already a set attribute, then set it here.
-        if not hasattr(cls, "_metadata"):
-            cls._metadata = metadata
-
-        ell_min, ell_max = 99, -1
-        t_min, t_max, dt = -1e99, 1e99, 1
-
-        file_tag = get_tag(file_path)
-        mode_data = {}
-        reference_mode_num_for_length = ()
-        possible_ascii_extensions = ["asc", "dat", "txt"]
-
-        with tarfile.open(file_path, "r:gz") as tar:
-            for dat_file in tar.getmembers():
-                dat_file_name = dat_file.name
-
-                if file_tag not in dat_file_name or np.all(
-                    [
-                        f".{ext}" not in dat_file_name
-                        for ext in possible_ascii_extensions
-                    ]
-                ):
-                    continue
-                ell, em = get_el_em_from_filename(dat_file_name)
-                with tar.extractfile(dat_file_name) as f:
-                    reference_mode_num_for_length = (ell, em)
-                    mode_data[(ell, em)] = np.loadtxt(f)
-                    # print(mode_data[(ell, em)].shape)
-                    # mode_data[get_tag(dat_file_name)] = np.loadtxt(f)
-                # get the minimum time and maximum time stamps for all modes
-                t_min = max(t_min, mode_data[(ell, em)][0, 0])
-                t_max = min(t_max, mode_data[(ell, em)][-1, 0])
-                dt = min(
-                    dt,
-                    stat_mode(np.diff(mode_data[(ell, em)][:, 0]), keepdims=True)[0][0],
-                )
-                ell_min = min(ell_min, ell)
-                ell_max = max(ell_max, ell)
-
-        # We populate LM here because it has to be ordered, as the WaveformModes
-        # class expects an ordered data set.
-        LM = []
-        for ell in range(ELL_MIN, ELL_MAX + 1):
-            for em in range(-ell, ell + 1):
-                if (ell, em) in mode_data:
-                    LM.append([ell, em])
-                else:
-                    reference_mode = mode_data[reference_mode_num_for_length]
-                    mode_data[(ell, em)] = np.zeros(np.shape(reference_mode))
-                    LM.append([ell, em])
-
-        if len(LM) == 0:
-            raise RuntimeError(
-                "We did not find even one mode in the file. Perhaps the "
-                "format `amp_l?_m?` and `phase_l?_m?` is not the "
-                "nomenclature of datagroups in the input file?"
-            )
-
-        times = np.arange(t_min, t_max + 0.5 * dt, dt)
-        data = np.empty((len(times), len(LM)), dtype=complex)
-        for idx, (ell, em) in enumerate(LM):
-            print(mode_data[(ell, em)].shape)
-            mode_time, mode_real, mode_imag, _, _, _ = mode_data[(ell, em)].T
-            mode_real_interp = InterpolatedUnivariateSpline(mode_time, mode_real)
-            mode_imag_interp = InterpolatedUnivariateSpline(mode_time, mode_imag)
-            data[:, idx] = mode_real_interp(times) + 1j * mode_imag_interp(times)
-
-        w_attributes = {}
-        w_attributes["metadata"] = metadata
-        w_attributes["history"] = ""
-        w_attributes["frame"] = quaternionic.array([[1.0, 0.0, 0.0, 0.0]])
-        w_attributes["frame_type"] = "inertial"
-        w_attributes["data_type"] = h
-        w_attributes["spin_weight"] = translate_data_type_to_spin_weight(
-            w_attributes["data_type"]
-        )
-        w_attributes["data_type"] = translate_data_type_to_sxs_string(
-            w_attributes["data_type"]
-        )
-        w_attributes["r_is_scaled_out"] = True
-        w_attributes["m_is_scaled_out"] = True
-        # w_attributes["ells"] = ell_min, ell_max
-
-        return cls(
-            data,
-            time=times,
-            time_axis=0,
-            modes_axis=1,
-            ell_min=ell_min,
-            ell_max=ell_max,
-            verbosity=verbosity,
-            **w_attributes,
-        )
-
     @property
     def filepath(self):
         """Return the data file path"""