oasislmf.pytools.common.data
============================

.. py:module:: oasislmf.pytools.common.data


Attributes
----------

.. autoapisummary::

   oasislmf.pytools.common.data.oasis_int
   oasislmf.pytools.common.data.nb_oasis_int
   oasislmf.pytools.common.data.oasis_int_size
   oasislmf.pytools.common.data.oasis_float
   oasislmf.pytools.common.data.nb_oasis_float
   oasislmf.pytools.common.data.oasis_float_size
   oasislmf.pytools.common.data.areaperil_int
   oasislmf.pytools.common.data.nb_areaperil_int
   oasislmf.pytools.common.data.areaperil_int_size
   oasislmf.pytools.common.data.null_index
   oasislmf.pytools.common.data.DEFAULT_BUFFER_SIZE
   oasislmf.pytools.common.data.MEAN_TYPE_ANALYTICAL
   oasislmf.pytools.common.data.MEAN_TYPE_SAMPLE
   oasislmf.pytools.common.data.aggregatevulnerability_output
   oasislmf.pytools.common.data.amplifications_output
   oasislmf.pytools.common.data.cdf_output
   oasislmf.pytools.common.data.complex_items_meta_output
   oasislmf.pytools.common.data.correlations_output
   oasislmf.pytools.common.data.coverages_output
   oasislmf.pytools.common.data.damagebin_output
   oasislmf.pytools.common.data.eve_output
   oasislmf.pytools.common.data.footprint_event_output
   oasislmf.pytools.common.data.fm_output
   oasislmf.pytools.common.data.fm_policytc_output
   oasislmf.pytools.common.data.fm_profile_output
   oasislmf.pytools.common.data.fm_profile_step_output
   oasislmf.pytools.common.data.fm_programme_output
   oasislmf.pytools.common.data.fm_summary_xref_output
   oasislmf.pytools.common.data.fm_xref_output
   oasislmf.pytools.common.data.gul_output
   oasislmf.pytools.common.data.gul_summary_xref_output
   oasislmf.pytools.common.data.items_output
   oasislmf.pytools.common.data.lossfactors_output
   oasislmf.pytools.common.data.occurrence_output
   oasislmf.pytools.common.data.occurrence_granular_output
   oasislmf.pytools.common.data.periods_output
   oasislmf.pytools.common.data.quantile_output
   oasislmf.pytools.common.data.quantile_interval_output
   oasislmf.pytools.common.data.random_output
   oasislmf.pytools.common.data.returnperiods_output
   oasislmf.pytools.common.data.vulnerability_output
   oasislmf.pytools.common.data.vulnerability_weight_output
   oasislmf.pytools.common.data.float_equal_precision


Functions
---------

.. autoapisummary::

   oasislmf.pytools.common.data.generate_output_metadata
   oasislmf.pytools.common.data.load_as_ndarray
   oasislmf.pytools.common.data.load_as_array
   oasislmf.pytools.common.data.write_ndarray_to_fmt_csv
   oasislmf.pytools.common.data.almost_equal
   oasislmf.pytools.common.data.resolve_file


Module Contents
---------------

.. py:data:: oasis_int

.. py:data:: nb_oasis_int

.. py:data:: oasis_int_size

.. py:data:: oasis_float

.. py:data:: nb_oasis_float

.. py:data:: oasis_float_size

.. py:data:: areaperil_int

.. py:data:: nb_areaperil_int

.. py:data:: areaperil_int_size

.. py:data:: null_index

.. py:data:: DEFAULT_BUFFER_SIZE
   :value: 1000000


.. py:data:: MEAN_TYPE_ANALYTICAL
   :value: 1


.. py:data:: MEAN_TYPE_SAMPLE
   :value: 2


.. py:function:: generate_output_metadata(output)

   Generates *_header, *_dtype and *_fmt items given a list of tuples describing some output description
   output description has type List(Tuple({name: str}, {type: Any}, {format: str}))
   Args:
       output_map (list(tuple(str, Any, str))): Dictionary mapping string name to  {output description}_output list
   Returns:
       result (tuple(list[str], np.dtype, str)): Tuple containing the generated *_header list, *_dtype np.dtype, *_fmt csv format string


.. py:data:: aggregatevulnerability_output
   :value: [('aggregate_vulnerability_id', 'i4', '%d'), ('vulnerability_id', 'i4', '%d')]


.. py:data:: amplifications_output
   :value: [('item_id', 'i4', '%d'), ('amplification_id', 'i4', '%d')]


.. py:data:: cdf_output
   :value: [('event_id', 'i4', '%d'), ('areaperil_id', 'i4', '%d'), ('vulnerability_id', 'i4', '%d'),...


.. py:data:: complex_items_meta_output
   :value: [('item_id', 'u4', '%u'), ('coverage_id', 'u4', '%u'), ('group_id', 'u4', '%u'),...


.. py:data:: correlations_output

.. py:data:: coverages_output

.. py:data:: damagebin_output

.. py:data:: eve_output

.. py:data:: footprint_event_output

.. py:data:: fm_output

.. py:data:: fm_policytc_output
   :value: [('level_id', 'i4', '%d'), ('agg_id', 'i4', '%d'), ('layer_id', 'i4', '%d'), ('profile_id', 'i4', '%d')]


.. py:data:: fm_profile_output

.. py:data:: fm_profile_step_output

.. py:data:: fm_programme_output
   :value: [('from_agg_id', 'i4', '%d'), ('level_id', 'i4', '%d'), ('to_agg_id', 'i4', '%d')]


.. py:data:: fm_summary_xref_output
   :value: [('output', 'i4', '%d'), ('summary_id', 'i4', '%d'), ('summaryset_id', 'i4', '%d')]


.. py:data:: fm_xref_output
   :value: [('output', 'i4', '%d'), ('agg_id', 'i4', '%d'), ('layer_id', 'i4', '%d')]


.. py:data:: gul_output

.. py:data:: gul_summary_xref_output
   :value: [('item_id', 'i4', '%d'), ('summary_id', 'i4', '%d'), ('summaryset_id', 'i4', '%d')]


.. py:data:: items_output

.. py:data:: lossfactors_output
   :value: [('event_id', 'i4', '%d'), ('amplification_id', 'i4', '%d'), ('factor', 'f4', '%.2f')]


.. py:data:: occurrence_output
   :value: [('event_id', 'i4', '%d'), ('period_no', 'i4', '%d'), ('occ_date_id', 'i4', '%d')]


.. py:data:: occurrence_granular_output
   :value: [('event_id', 'i4', '%d'), ('period_no', 'i4', '%d'), ('occ_date_id', 'i8', '%d')]


.. py:data:: periods_output
   :value: [('period_no', 'i4', '%d'), ('weighting', 'f8', '%0.9lf')]


.. py:data:: quantile_output
   :value: [('quantile', 'f4', '%f')]


.. py:data:: quantile_interval_output

.. py:data:: random_output
   :value: [('random_no', 'f4', '%f')]


.. py:data:: returnperiods_output
   :value: [('return_period', 'i4', '%d')]


.. py:data:: vulnerability_output

.. py:data:: vulnerability_weight_output

.. py:function:: load_as_ndarray(dir_path, name, _dtype, must_exist=True, col_map=None)

   load a file as a numpy ndarray
   useful for multi-columns files
   Args:
       dir_path: path to the directory where the binary or csv file is stored
       name: name of the file
       _dtype: np.dtype
       must_exist: raise FileNotFoundError if no file is present
       col_map: name re-mapping to change name of csv columns
   Returns:
       numpy ndarray


.. py:function:: load_as_array(dir_path, name, _dtype, must_exist=True)

   load file as a single numpy array,
    useful for files with a binary version with only one type of value where their index correspond to an id.
    For example coverage.bin only contains tiv value for each coverage id
    coverage_id n correspond to index n-1
   Args:
       dir_path: path to the directory where the binary or csv file is stored
       name: name of the file
       _dtype: numpy dtype of the required array
       must_exist: raise FileNotFoundError if no file is present
   Returns:
       numpy array of dtype type


.. py:function:: write_ndarray_to_fmt_csv(output_file, data, headers, row_fmt)

   Writes a custom dtype array with headers to csv with the provided row_fmt str

   This function is a faster replacement for np.savetxt as it formats each row one at a time before writing to csv.
   We create one large string, and formats all the data at once, and writes all the data at once.

   WARNING: untested with string types in custom data.

   Args:
       output_file (io.TextIOWrapper): CSV file
       data (ndarray[<custom dtype>]): Custom dtype ndarray with column names
       headers (list[str]): Column names for custom ndarray
       row_fmt (str): Format for each row in csv


.. py:data:: float_equal_precision

.. py:function:: almost_equal(a, b)

.. py:function:: resolve_file(path, mode, stack)

   Resolve file path to open file or use sys.stdin

   Args:
       path (str | os.PathLike): File path or "-" indicationg standard input/output.
       mode (str): Mode to open file ("r", "rb, "w", "wb").
       stack (ExitStack): Context manager stack used to manage file lifecycle.

   Returns:
       file (IO): A file-like object opened in the specified mode.