Source code for mth5.groups.standards

# -*- coding: utf-8 -*-
"""
Created on Wed Dec 23 17:05:33 2020

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

:license: MIT

"""

# =============================================================================
# Imports
# =============================================================================
import inspect
import numpy as np

from mth5.groups.base import BaseGroup
from mth5.tables import MTH5Table
from mth5.utils.exceptions import MTH5TableError

from mt_metadata.base import BaseDict
from mt_metadata import timeseries
from mt_metadata.timeseries import filters
from mt_metadata.utils.validators import validate_attribute

ts_classes = dict(inspect.getmembers(timeseries, inspect.isclass))
flt_classes = dict(inspect.getmembers(filters, inspect.isclass))
# =============================================================================
# Summarize standards
# =============================================================================
[docs]def summarize_metadata_standards(): """ Summarize metadata standards into a dictionary """ # need to be sure to make copies otherwise things will get # added in not great places. # need to be sure to make copies otherwise things will get # added in not great places. summary_dict = BaseDict() for key in ["survey", "station", "run", "electric", "magnetic", "auxiliary"]: obj = ts_classes[key.capitalize()]() summary_dict.add_dict(obj._attr_dict.copy(), key) for key in [ "Coefficient", "FIR", "FrequencyResponseTable", "PoleZero", "TimeDelay", ]: key += "Filter" obj = flt_classes[key]() summary_dict.add_dict(obj._attr_dict.copy(), validate_attribute(key)) return summary_dict
# ============================================================================= # Standards Group # =============================================================================
[docs]class StandardsGroup(BaseGroup): """ The StandardsGroup is a convenience group that stores the metadata standards that were used to make the current file. This is to help a user understand the metadata directly from the file and not have to look up documentation that might not be updated. The metadata standards are stored in the summary table ``/Survey/Standards/summary`` >>> standards = mth5_obj.standards_group >>> standards.summary_table index | attribute | type | required | style | units | description | options | alias | example -------------------------------------------------------------------------- """ def __init__(self, group, **kwargs): super().__init__(group, **kwargs) self._defaults_summary_attrs = { "name": "summary", "max_shape": (500,), "dtype": np.dtype( [ ("attribute", "S72"), ("type", "S15"), ("required", np.bool_), ("style", "S72"), ("units", "S32"), ("description", "S300"), ("options", "S150"), ("alias", "S72"), ("example", "S72"), ] ), } @property def summary_table(self): return MTH5Table(self.hdf5_group["summary"])
[docs] def get_attribute_information(self, attribute_name): """ get information about an attribute The attribute name should be in the summary table. :param attribute_name: attribute name :type attribute_name: string :return: prints a description of the attribute :raises MTH5TableError: if attribute is not found >>> standars = mth5_obj.standards_group >>> standards.get_attribute_information('survey.release_license') survey.release_license -------------------------- type : string required : True style : controlled vocabulary units : description : How the data can be used. The options are based on Creative Commons licenses. For details visit https://creativecommons.org/licenses/ options : CC-0,CC-BY,CC-BY-SA,CC-BY-ND,CC-BY-NC-SA,CC-BY-NC-ND alias : example : CC-0 """ find = self.summary_table.locate("attribute", attribute_name) if len(find) == 0: msg = f"Could not find {attribute_name} in standards." self.logger.error(msg) raise MTH5TableError(msg) meta_item = self.summary_table.array[find] lines = ["", attribute_name, "-" * (len(attribute_name) + 4)] for name, value in zip(meta_item.dtype.names[1:], meta_item.item()[1:]): if isinstance(value, (bytes, np.bytes_)): value = value.decode() lines.append("\t{0:<14} {1}".format(name + ":", value)) print("\n".join(lines))
[docs] def summary_table_from_dict(self, summary_dict): """ Fill summary table from a dictionary that summarizes the metadata for the entire survey. :param summary_dict: Flattened dictionary of all metadata standards within the survey. :type summary_dict: dictionary """ for key, v_dict in summary_dict.items(): key_list = [key] for dkey in self.summary_table.dtype.names[1:]: value = v_dict[dkey] if isinstance(value, list): if len(value) == 0: value = "" else: value = ",".join(["{0}".format(ii) for ii in value]) if value is None: value = "" key_list.append(value) key_list = np.array([tuple(key_list)], self.summary_table.dtype) index = self.summary_table.add_row(key_list) self.logger.debug(f"Added {index} rows to Standards Group")
[docs] def initialize_group(self): """ Initialize the group by making a summary table that summarizes the metadata standards used to describe the data. Also, write generic metadata information. """ if self.dataset_options["compression"] is None: summary_dataset = self.hdf5_group.create_dataset( self._defaults_summary_attrs["name"], (0,), maxshape=self._defaults_summary_attrs["max_shape"], dtype=self._defaults_summary_attrs["dtype"], ) else: summary_dataset = self.hdf5_group.create_dataset( self._defaults_summary_attrs["name"], (0,), maxshape=self._defaults_summary_attrs["max_shape"], dtype=self._defaults_summary_attrs["dtype"], **self.dataset_options, ) summary_dataset.attrs.update( { "type": "summary table", "last_updated": "date_time", "reference": summary_dataset.ref, } ) self.logger.debug( "Created %s table with max_shape = %s, dtype=%s", self._defaults_summary_attrs["name"], self._defaults_summary_attrs["max_shape"], self._defaults_summary_attrs["dtype"], ) self.logger.debug( "used options: " + "; ".join(["%s = %s" % (k, v) for k, v in self.dataset_options.items()]) ) self.summary_table_from_dict(summarize_metadata_standards()) self.write_metadata()