Source code for mth5.tables.mth5_table

# -*- coding: utf-8 -*-
"""
Created on Wed Dec 23 16:53:55 2020

:author: Jared Peacock

:license: MIT

"""
# =============================================================================
# Imports
# =============================================================================
import weakref

import h5py
import numpy as np
import pandas as pd

from mth5.utils.exceptions import MTH5TableError
from mth5.utils.mth5_logger import setup_logger

# =============================================================================
# MTH5 Table Class
# =============================================================================


[docs]class MTH5Table: """ Use the underlying NumPy basics, there are simple actions in this table, if a user wants to use something more sophisticated for querying they should try using a pandas table. In this case entries in the table are more difficult to change and datatypes need to be kept track of. """ def __init__(self, hdf5_dataset): self.logger = setup_logger(f"{__name__}.{self.__class__.__name__}") self.hdf5_reference = None if isinstance(hdf5_dataset, h5py.Dataset): self.array = weakref.ref(hdf5_dataset)() self.hdf5_reference = hdf5_dataset.ref else: msg = "Input must be a h5py.Dataset not {0}".format(type(hdf5_dataset)) self.logger.error(msg) raise MTH5TableError(msg) def __str__(self): """ return a string that shows the table in text form :return: text representation of the table :rtype: string """ # if the array is empty if self.array.size == 0: length_dict = dict([(key, len(str(key))) for key in list(self.dtype.names)]) lines = [ " | ".join( ["index"] + [ "{0:^{1}}".format(name, length_dict[name]) for name in list(self.dtype.names) ] ) ] lines.append("-" * len(lines[0])) return "\n".join(lines) length_dict = dict( [ (key, max([len(str(b)) for b in self.array[key]])) for key in list(self.dtype.names) ] ) lines = [ " | ".join( ["index"] + [ "{0:^{1}}".format(name, length_dict[name]) for name in list(self.dtype.names) ] ) ] lines.append("-" * len(lines[0])) for ii, row in enumerate(self.array): line = ["{0:^5}".format(ii)] for element, key in zip(row, list(self.dtype.names)): if isinstance(element, (np.bytes_)): element = element.decode() try: line.append("{0:^{1}}".format(element, length_dict[key])) except TypeError as error: if isinstance(element, h5py.h5r.Reference): msg = "{0}: Cannot represent h5 reference as a string" self.logger.debug(msg.format(error)) line.append( "{0:^{1}}".format( "<HDF5 object reference>", length_dict[key] ) ) else: self.logger.exception(f"{error}") lines.append(" | ".join(line)) return "\n".join(lines) def __repr__(self): return self.__str__() def __eq__(self, other): if isinstance(other, MTH5Table): return self.array == other.array elif isinstance(other, h5py.Dataset): return self.array == other else: msg = "Cannot compare type={0}".format(type(other)) self.logger.error(msg) raise TypeError(msg) def __ne__(self, other): return not self.__eq__(other) def __len__(self): return self.array.shape[0] @property def dtype(self): try: return self.array.dtype except AttributeError as error: msg = "{0}, dataframe is not initiated yet".format(error) self.logger.warning(msg) return None
[docs] def check_dtypes(self, other_dtype): """ Check to make sure datatypes match """ if self.dtype == other_dtype: return True return False
@property def shape(self): return self.array.shape @property def nrows(self): return self.array.shape[0]
[docs] def locate(self, column, value, test="eq"): """ locate index where column is equal to value :param column: DESCRIPTION :type column: TYPE :param value: DESCRIPTION :type value: TYPE :type test: type of test to try * 'eq': equals * 'lt': less than * 'le': less than or equal to * 'gt': greater than * 'ge': greater than or equal to. * 'be': between or equal to * 'bt': between If be or bt input value as a list of 2 values :return: DESCRIPTION :rtype: TYPE """ if isinstance(value, str): value = np.bytes_(value) # use numpy datetime for testing against time. if column in ["start", "end", "start_date", "end_date"]: test_array = self.array[column].astype(np.datetime64) value = np.datetime64(value) else: test_array = self.array[column] if test == "eq": index_values = np.where(test_array == value)[0] elif test == "lt": index_values = np.where(test_array < value)[0] elif test == "le": index_values = np.where(test_array <= value)[0] elif test == "gt": index_values = np.where(test_array > value)[0] elif test == "ge": index_values = np.where(test_array >= value)[0] elif test == "be": if not isinstance(value, (list, tuple, np.ndarray)): msg = "If testing for between value must be an iterable of length 2." self.logger.error(msg) raise ValueError(msg) index_values = np.where((test_array > value[0]) & (test_array < value[1]))[ 0 ] else: raise ValueError("Test {0} not understood".format(test)) return index_values
[docs] def add_row(self, row, index=None): """ Add a row to the table. row must be of the same data type as the table :param row: row entry for the table :type row: TYPE :param index: index of row to add :type index: integer, if None is given then the row is added to the end of the array :return: index of the row added :rtype: integer """ if not isinstance(row, (np.ndarray)): msg = "Input must be an numpy.ndarray" + "not {0}".format(type(row)) if isinstance(row, np.ndarray): if not self.check_dtypes(row.dtype): msg = "{0}\nInput dtypes:\n{1}\n\nTable dtypes:\n{2}".format( "Data types are not equal:", row.dtype, self.dtype ) self.logger.error(msg) raise ValueError(msg) if index is None: index = self.nrows new_shape = tuple([self.nrows + 1] + [ii for ii in self.shape[1:]]) self.array.resize(new_shape) # add the row self.array[index] = row self.logger.debug("Added row as index {0} with values {1}".format(index, row)) return index
[docs] def update_row(self, entry): """ Update an entry by first locating the index and then rewriting the entry. :param entry: numpy array with same datatype as the table :type entry: np.ndarray :return: row index. This doesn't work because you cannot test for hdf5_reference, should use add row and locate by index. """ try: row_index = self.locate("hdf5_reference", entry["hdf5_reference"])[0] return self.add_row(entry, index=row_index) except IndexError: self.logger.debug("Could not find row, adding a new one") return self.add_row(entry)
[docs] def remove_row(self, index): """ Remove a row .. note:: that there is not index value within the array, so the indexing is on the fly. A user should use the HDF5 reference instead of index number that is the safest and most robust method. :param index: DESCRIPTION :type index: TYPE :return: DESCRIPTION :rtype: TYPE This isn't as easy as just deleteing an element. Need to delete the element from the weakly referenced array and then set the summary table dataset to the new array. So set to a null array for now until a more clever option is found. """ null_array = np.empty((1,), dtype=self.dtype) try: return self.add_row(null_array, index=index) except IndexError as error: msg = "Could not find index {0} in shape {1}".format(index, self.shape()) self.logger.exception(msg) raise IndexError(f"{error}\n{msg}")
[docs] def to_dataframe(self): """ Convert the table into a :class:`pandas.DataFrame` object. :return: convert table into a :class:`pandas.DataFrame` with the appropriate data types. :rtype: :class:`pandas.DataFrame` """ df = pd.DataFrame(self.array[()]) for key in [ "station", "run", "component", "measurement_type", "units", ]: setattr(df, key, getattr(df, key).str.decode("utf-8")) df.start = pd.to_datetime(df.start.str.decode("utf-8")) df.end = pd.to_datetime(df.end.str.decode("utf-8")) return df