`0.4.9` missing scipy import in timeseries/channel_ts.py

[gabelepoudre]

Windows Server 2022 Standard

Error from action runner, mth5 version 0.4.9:

natural_fields_lib\natural_fields\mt\util\mth5_helpers.py:300: in pack_runts_into_mth5
    run_group.from_runts(runts)  # FIXME: this is the slow line
C:\Users\DIASdeployment\miniforge3\envs\qamt_testing\lib\site-packages\mth5\groups\run.py:663: in from_runts
    ch = getattr(run_ts_obj, comp)
C:\Users\DIASdeployment\miniforge3\envs\qamt_testing\lib\site-packages\mth5\timeseries\run_ts.py:487: in __getattr__
    return ChannelTS(
C:\Users\DIASdeployment\miniforge3\envs\qamt_testing\lib\site-packages\mth5\timeseries\channel_ts.py:133: in __init__
    self.ts = data
C:\Users\DIASdeployment\miniforge3\envs\qamt_testing\lib\site-packages\mth5\timeseries\channel_ts.py:735: in ts
    self._update_xarray_metadata()
C:\Users\DIASdeployment\miniforge3\envs\qamt_testing\lib\site-packages\mth5\timeseries\channel_ts.py:804: in _update_xarray_metadata
    self.channel_metadata.time_period.start = self.start.iso_no_tz
C:\Users\DIASdeployment\miniforge3\envs\qamt_testing\lib\site-packages\mth5\timeseries\channel_ts.py:573: in channel_metadata
    ch_metadata.sample_rate = self.sample_rate
C:\Users\DIASdeployment\miniforge3\envs\qamt_testing\lib\site-packages\mth5\timeseries\channel_ts.py:932: in sample_rate
    self._sample_rate = self.compute_sample_rate()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <[NameError("name 'scipy' is not defined") raised in repr()] ChannelTS object at 0x2526559dab0>

    def compute_sample_rate(self):
        """
        Two cases, high_frequency (HF) data and not HF data.

        # Original comment about the HF case:
        Taking the median(diff(timestamps)) is more accurate for high sample rates, the way pandas.date_range
        rounds nanoseconds is not consistent between samples, therefore taking the median provides better results
        if the time series is long this can be inefficient so test first

        """
        if self.is_high_frequency():
            dt_array = np.diff(self.data_array.coords.indexes["time"])
>           best_dt, counts = scipy.stats.mode(dt_array)
E           NameError: name 'scipy' is not defined

C:\Users\DIASdeployment\miniforge3\envs\qamt_testing\lib\site-packages\mth5\timeseries\channel_ts.py:913: NameError

imports at top of script

# -*- coding: utf-8 -*-
"""
.. module:: timeseries
   :synopsis: Deal with MT time series

.. todo:: Check the conversion to netcdf.  There are some weird serializations of
lists and arrays that goes on, seems easiest to convert all lists to strings and then
convert them back if read in.

:copyright:
    Jared Peacock (jpeacock@usgs.gov)

:license:
    MIT
"""

# ==============================================================================
# Imports
# ==============================================================================
import inspect

import numpy as np
import pandas as pd
import xarray as xr
from loguru import logger
from scipy import signal

import mt_metadata.timeseries as metadata
from mt_metadata.timeseries.filters import ChannelResponse
from mt_metadata.utils.mttime import MTime
from mt_metadata.utils.list_dict import ListDict

from mth5.utils import fdsn_tools
from mth5.timeseries.ts_filters import RemoveInstrumentResponse
from mth5.timeseries.ts_helpers import (
    make_dt_coordinates,
    get_decimation_sample_rates,
)

from obspy.core import Trace

# =============================================================================
# make a dictionary of available metadata classes
# =============================================================================
meta_classes = dict(inspect.getmembers(metadata, inspect.isclass))

# ==============================================================================

[kujaku11]

fixed with PR #258