<commit_before>from __future__ import print_function, division
import os
import copy
import shutil
from datetime import datetime
import warnings
from .pyemu_warnings import PyemuWarning
import numpy as np
import pandas as pd
from pyemu.en import ObservationEnsemble
from pyemu.mat.mat_handler import Matrix, Jco, Cov
from pyemu.pst.pst_handler import Pst
from pyemu.utils.os_utils import _istextfile,run
from .logger import Logger



class EnDS(object):
    """Ensemble Data Space Analysis.

    Args:
        pst (varies): something that can be cast into a `pyemu.Pst`.  Can be an `str` for a
            filename or an existing `pyemu.Pst`.
        sim_ensemble (varies): something that can be cast into a `pyemu.ObservationEnsemble`.  Can be
            an `str` for a  filename or `pd.DataFrame` or an existing `pyemu.ObservationEnsemble`.
        noise_ensemble (varies): something that can be cast into a `pyemu.ObservationEnsemble` that is the
            obs+noise realizations.  If not passed, a noise ensemble is generated using either `obs_cov` or the
            information in `pst` (i.e. weights or standard deviations)
        obscov (varies, optional): observation noise covariance matrix.  If `str`, a filename is assumed and
            the noise covariance matrix is loaded from a file using
            the file extension (".jcb"/".jco" for binary, ".cov"/".mat" for PEST-style ASCII matrix,
            or ".unc" for uncertainty files).  If `None`, the noise covariance matrix is
            constructed from the observation weights (and optionally "standard_deviation")
            .  Can also be a `pyemu.Cov` instance
        forecasts (enumerable of `str`): the names of the entries in `pst.osbervation_data` (and in `ensemble`).<commit_msg>small change to docstring<commit_after>from __future__ import print_function, division
import os
import copy
import shutil
from datetime import datetime
import warnings
from .pyemu_warnings import PyemuWarning
import numpy as np
import pandas as pd
from pyemu.en import ObservationEnsemble
from pyemu.mat.mat_handler import Matrix, Jco, Cov
from pyemu.pst.pst_handler import Pst
from pyemu.utils.os_utils import _istextfile,run
from .logger import Logger



class EnDS(object):
    """Ensemble Data Space Analysis using the approach of He et al (2018) (https://doi.org/10.2118/182609-PA)

    Args:
        pst (varies): something that can be cast into a `pyemu.Pst`.  Can be an `str` for a
            filename or an existing `pyemu.Pst`.
        sim_ensemble (varies): something that can be cast into a `pyemu.ObservationEnsemble`.  Can be
            an `str` for a  filename or `pd.DataFrame` or an existing `pyemu.ObservationEnsemble`.
        noise_ensemble (varies): something that can be cast into a `pyemu.ObservationEnsemble` that is the
            obs+noise realizations.  If not passed, a noise ensemble is generated using either `obs_cov` or the
            information in `pst` (i.e. weights or standard deviations)
        obscov (varies, optional): observation noise covariance matrix.  If `str`, a filename is assumed and
            the noise covariance matrix is loaded from a file using
            the file extension (".jcb"/".jco" for binary, ".cov"/".mat" for PEST-style ASCII matrix,
            or ".unc" for uncertainty files).  If `None`, the noise covariance matrix is
            constructed from the observation weights (and optionally "standard_deviation")
            .  Can also be a `pyemu.Cov` instance
        forecasts (enumerable of `str`): the names of the entries in `pst.osbervation_data` (and in `ensemble`).
{'fraction_non_alphanumeric': 0.07215924799557645, 'fraction_numerical': 0.004423555432679016, 'mean_word_length': 3.241500586166471, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7606708', 'n_tokens_mistral': 1042, 'n_tokens_neox': 995, 'n_words': 437}