Source code for rex.utilities.utilities

# -*- coding: utf-8 -*-
"""
Collection of helpful functions
"""
import datetime
import inspect
import json
import os
from fnmatch import fnmatch
import numpy as np
import pandas as pd
from pandas.api.types import CategoricalDtype
import re
from scipy.spatial import cKDTree
import time
from warnings import warn

from rex.utilities.exceptions import (FileInputError, JSONError, RetryError,
                                      RetryWarning)


[docs]def safe_json_load(fpath): """Perform a json file load with better exception handling. Parameters ---------- fpath : str Filepath to .json file. Returns ------- j : dict Loaded json dictionary. Examples -------- >>> json_path = "./path_to_json.json" >>> safe_json_load(json_path) {key1: value1, key2: value2} """ if not isinstance(fpath, str): raise TypeError('Filepath must be str to load json: {}'.format(fpath)) if not fpath.endswith('.json'): raise JSONError('Filepath must end in .json to load json: {}' .format(fpath)) if not os.path.isfile(fpath): raise JSONError('Could not find json file to load: {}'.format(fpath)) try: with open(fpath, 'r') as f: j = json.load(f) except json.decoder.JSONDecodeError as e: emsg = ('JSON Error:\n{}\nCannot read json file: ' '"{}"'.format(e, fpath)) raise JSONError(emsg) from e return j
[docs]def jsonify_dict(di): """Jsonify a dictionary into a string with handling for int/float keys. Parameters ---------- di : dict Dictionary to be jsonified. Returns ------- sdi : str Jsonified dictionary. Int/float keys will be represented as strings because json objects outside of python cannot have int/float keys. """ for k in list(di.keys()): try: float(k) except ValueError: pass else: di[str(k)] = di.pop(k) try: sdi = json.dumps(di) except TypeError as e: msg = ('Could not json serialize {}, received error: {}' .format(di, e)) raise TypeError(msg) from e return sdi
[docs]def dict_str_load(dict_str): """ Load jsonified string entries into dictionaries using JSON Parameters ---------- dict_str : str JSON style string provided to CLI or in config Returns ------- out_dict : dict Dictionary loaded by JSON Examples -------- >>> json_str = "{bool_key: 'True', value_key: 'None'}" >>> dict_str_load(json_str) {bool_key: True, value_key: None} """ dict_str = dict_str.replace('\'', '\"') dict_str = dict_str.replace('None', 'null') dict_str = dict_str.replace('True', 'true') dict_str = dict_str.replace('False', 'false') out_dict = json.loads(dict_str) return out_dict
[docs]def parse_year(inp, option='raise'): """ Attempt to parse a year out of a string. Parameters ---------- inp : str String from which year is to be parsed option : str Return option: - "bool" will return True if year is found, else False. - Return year int / raise a RuntimeError otherwise Returns ------- out : int | bool Year int parsed from inp, or boolean T/F (if found and option is bool). Examples -------- >>> year_str = "NSRDB_2018.h5" >>> parse_year(year_str) 2018 >>> year_str = "NSRDB_2018.h5" >>> parse_year(year_str, option='bool') True >>> year_str = "NSRDB_TMY.h5" >>> parse_year(year_str) RuntimeError: Cannot parse year from NSRDB_TMY.h5 >>> year_str = "NSRDB_TMY.h5" >>> parse_year(year_str, option='bool') False """ # char leading year cannot be 0-9 # char trailing year can be end of str or not 0-9 regex = r".*[^0-9]([1-2][0-9]{3})($|[^0-9])" match = re.match(regex, inp) if match: out = int(match.group(1)) if 'bool' in option: out = True else: if 'bool' in option: out = False else: raise RuntimeError('Cannot parse year from {}'.format(inp)) return out
[docs]def mean_irrad(arr): """Calc the annual irradiance at a site given an irradiance timeseries. Parameters ---------- arr : np.ndarray | pd.Series Annual irradiance array in W/m2. Row dimension is time. Returns ------- mean : float | np.ndarray Mean irradiance values in kWh/m2/day. Float if the input array is 1D, 1darray if the input array is 2D (multi-site). """ mean = arr.mean(axis=0) / 1000 * 24 return mean
[docs]def check_res_file(res_file): """ Check resource to see if the given path - It belongs to a multi-file handler - Is on local disk - Is a hsds path Parameters ---------- res_file : str Filepath to single resource file, unix style multi-file path like /h5_dir/prefix*suffix.h5, or an hsds filepath (filename of hsds path can also contain wildcards *) Returns ------- multi_h5_res : bool Boolean flag to use a MultiFileResource handler hsds : bool Boolean flag to use h5pyd to handle .h5 'files' hosted on AWS behind HSDS """ multi_h5_res = False hsds = False if os.path.isfile(res_file): pass elif '*' in res_file: multi_h5_res = True elif os.path.isdir(res_file): msg = ('Cannot parse directory, need to add wildcard * suffix: {}' .format(res_file)) raise FileInputError(msg) else: try: import h5pyd hsds_dir = os.path.dirname(res_file) with h5pyd.Folder(hsds_dir + '/') as f: hsds = True fps = [f'{hsds_dir}/{fn}' for fn in f if fnmatch(f'{hsds_dir}/{fn}', res_file)] if not any(fps): msg = ('{} is not a valid HSDS file path!' .format(res_file)) raise FileNotFoundError(msg) elif len(fps) > 1: multi_h5_res = True except Exception as ex: msg = ("{} is not a valid file path, and HSDS " "cannot be check for a file at this path:{}!" .format(res_file, ex)) raise FileNotFoundError(msg) from ex return multi_h5_res, hsds
[docs]def parse_date_int(s): """Parse data parameters from an integer or string of format YYYYMMDD Parmeters --------- s : str | int Date string or integer of format YYYYMMDD Returns ------- y : int Year integer parsed from input. m : int Month integer parsed from input. d : int Day integer parsed from input. """ try: s = str(int(s)) except ValueError as ex: e = ('Could not convert date string to int: "{}"' .format(s)) raise ValueError(e) from ex assert len(s) == 8, 'Bad date string, should be YYYYMMDD: {}'.format(s) y = int(s[0:4]) m = int(s[4:6]) d = int(s[6:8]) assert y > 1970, 'Bad date string, year < 1970: {}'.format(s) assert m < 13, 'Bad date string, month > 12: {}'.format(s) assert d < 32, 'Bad date string, day > 31: {}'.format(s) return y, m, d
[docs]def str_to_date(s): """Convert a date string of format YYYYMMDD to date object. Parameters ---------- s : str Date string of format YYYYMMDD Returns ------- d : datetime.date Date object. """ d = datetime.date(*parse_date_int(s)) return d
[docs]def str_to_datetime(s): """Convert a date string of format YYYYMMDD to datetime object. Parameters ---------- s : str Date string of format YYYYMMDD Returns ------- d : datetime.datetime Datetime object. """ d = datetime.datetime(*parse_date_int(s)) return d
[docs]def parse_table(table): """ Load pandas DataFrame from .csv or .json file or dictionary Parameters ---------- trans_table : str | pandas.DataFrame | dict Path to .csv or .json or dictionary containing table to parse Returns ------- table : pandas.DataFrame DataFrame table """ if isinstance(table, str): if table.endswith('.csv'): table = pd.read_csv(table) if 'Unnamed: 0' in table: table = table.drop(columns='Unnamed: 0') elif table.endswith('.json'): table = pd.read_json(table) else: raise ValueError('Cannot parse {}, expecting a .csv or .json file' .format(table)) elif isinstance(table, dict): table = pd.DataFrame(dict) elif not isinstance(table, pd.DataFrame): raise ValueError('Cannot parse table from type {}, expecting a .csv, ' '.json, or pandas.DataFrame'.format(type(table))) return table
[docs]def get_class_properties(cls): """ Get all class properties Used to check against config keys Returns ------- properties : list List of class properties, each of which should represent a valid config key/entry """ properties = [attr for attr, attr_obj in inspect.getmembers(cls) if isinstance(attr_obj, property)] return properties
[docs]def timestamp_format_to_redex(time_format): """ convert time stamp format to redex Parameters ---------- time_format : str datetime timestamp format Returns ------- redex : str redex format for timestamp """ time_keys = {'%Y': r'\d{4}', '%m': r'\d{2}', '%d': r'\d{2}', '%H': r'\d{2}', '%M': r'\d{2}', '%S': r'\d{2}'} redex = time_format for key, item in time_keys.items(): if key in redex: redex = redex.replace(key, item) return redex
[docs]def parse_timestamp(path, time_format='%Y-%m-%d_%H:%M:%S'): """ extract timestamp with given format from given path Parameters ---------- path : str file path time_format : str, optional datetime timestamp format, by default '%Y-%m-%d_%H:%M:%S' Returns ------- str Portion of path that matches given format """ pattern = timestamp_format_to_redex(time_format) pattern = re.compile(pattern) matcher = pattern.search(path) if matcher is None: raise RuntimeError("Could not find timestamp with format {} in {}!" .format(time_format, path)) return matcher.group()
[docs]def filename_timestamp(file_name, time_format='%Y-%m-%d_%H:%M:%S'): """ extract timestamp from file name Parameters ---------- file_name : str file name or file path time_format : str, optional datetime timestamp format, by default '%Y-%m-%d_%H:%M:%S' Returns ------- str Portion of file_name that matches given format """ timestamp = parse_timestamp(os.path.basename(file_name), time_format=time_format) return timestamp
[docs]class Retry: """ Retry Decorator to run a function multiple times """ def __init__(self, tries=3, n_sec=1): """ Parameters ---------- tries : int, optional Number if times to retry function, by default 2 n_sec : int, optional Number of seconds to wait between tries, by default 1 """ self._tries = tries self._wait = n_sec
[docs] def __call__(self, func, *args, **kwargs): """ Decorator call Parameters ---------- func : obj Function to retry on Exception args : tuple Function arguments kwargs : dict Function kwargs """ def new_func(*args, **kwargs): i = 0 error = None while i <= self._tries: try: new_func = func(*args, **kwargs) break except RetryError as ex: msg = ('{} failed to run {} times:\n{}' .format(func.__name__, i, ex)) raise RuntimeError(msg) from ex except Exception as ex: error = ex warn('Attempt {} failed:\n{}'.format(i, error), RetryWarning) time.sleep(self._wait) finally: i += 1 if i > self._tries: raise RetryError('Failed to run {}:\n{}' .format(func.__name__, error)) return new_func return new_func
[docs]def check_eval_str(s): """Check an eval() string for questionable code. Parameters ---------- s : str String to be sent to eval(). This is most likely a math equation to be evaluated. It will be checked for questionable code like imports and dunder statements. """ bad_strings = ('import', 'os.', 'sys.', '.__', '__.') for bad_s in bad_strings: if bad_s in s: raise ValueError('Will not eval() string which contains "{}": {}' .format(bad_s, s))
[docs]def check_tz(time_index): """ Check datetime index for timezone, if None set to UTC Parameters ---------- time_index : pandas.DatatimeIndex DatetimeIndex to check timezone for Returns ------- time_index : pandas.DatatimeIndex Updated DatetimeIndex with timezone set """ if not time_index.tz: time_index = time_index.tz_localize('utc') return time_index
[docs]def get_lat_lon_cols(df): """ Get columns that contain (latitude, longitude) coordinates Parameters ---------- df : pandas.DataFrame DataFrame to extract coordinates (lat, lon) from Returns ------- lat_lon_cols : list Column names in df that correspond to the latitude and longitude coordinates. There must be a single unique set of latitude and longitude columns. """ lat_lon_cols = ['latitude', 'longitude'] lat = False lon = False for c in df.columns: if c.lower() in ['lat', 'latitude']: if lat: msg = ("Multiple possible latitude columns were found: " "({}, {})!".format(lat_lon_cols[0], c)) raise RuntimeError(msg) lat_lon_cols[0] = c lat = True elif c.lower() in ['lon', 'long', 'longitude']: if lon: msg = ("Multiple possible longitude columns were found: " "({}, {})!".format(lat_lon_cols[1], c)) raise RuntimeError(msg) lat_lon_cols[1] = c lon = True if not lat or not lon: msg = ("A valid pair of latitude and longitude columns could not be " "found in: {}!".format(df.columns)) raise RuntimeError(msg) return lat_lon_cols
[docs]def roll_timeseries(arr, timezones): """ Roll timeseries from UTC to local time. Automatically compute time-shift from UTC offset (timezone) and time-series length. Parameters ---------- arr : ndarray Input timeseries array of form (time, sites) timezones : ndarray | list Vector of timezone shifts from UTC to local time Returns ------- local_arr : ndarray Array shifted to local time """ if arr.shape[1] != len(timezones): msg = ('Number of timezone shifts ({}) does not match number of ' 'sites ({})'.format(len(timezones), arr.shape[1])) raise ValueError(msg) time_step = arr.shape[0] // 8760 local_arr = np.zeros(arr.shape, dtype=arr.dtype) for tz in set(timezones): mask = timezones == tz local_arr[:, mask] = np.roll(arr[:, mask], int(tz * time_step), axis=0) return local_arr
[docs]def get_chunk_ranges(ds_dim, chunk_size): """ Create list of chunk slices [(s_i, e_i), ...] Parameters ---------- ds_len : int Length of dataset axis to chunk chunk_size : int Size of chunks Returns ------- chunks : list List of chunk start and end positions [(s_i, e_i), (s_i+1, e_i+1), ...] """ chunks = list(range(0, ds_dim, chunk_size)) if chunks[-1] < ds_dim: chunks.append(ds_dim) else: chunks[-1] = ds_dim chunks = list(zip(chunks[:-1], chunks[1:])) return chunks
[docs]def split_sites_slice(sites_slice, n_sites, slice_size): """ Break up sites_slice into slices of size slice_size Parameters ---------- sites_slice : slice Sites to extract as a slice object to extract n_sites : int Total number of sites to extract slice_size : int Number of sites in each slice to extract either on each worker, or in series Returns ------- slices : list List of slices to extract """ stop = sites_slice.stop if stop is None: stop = n_sites if slice_size >= n_sites: msg = ('The slice_size {} is >= the number of sites to be ' 'extracted {}! A single slice will be extracted.' .format(slice_size, n_sites)) warn(msg) slices = [slice(sites_slice.start, stop, sites_slice.step)] else: step = sites_slice.step if step is not None: slice_size *= step # Create slices of size slice_size slices = [slice(s, e, step) for s, e in get_chunk_ranges(stop, slice_size)] return slices
[docs]def split_sites_list(sites, slice_size): """ Split sites into sub-lists of ~ size slice_size Parameters ---------- sites : list Sites to extract as a list or numpy object to extract slice_size : int Number of sites in each slice to extract either on each worker, or in series Returns ------- slices : list List of slices to extract """ if slice_size >= len(sites): msg = ('The slice_size {} is >= the number of sites to be ' 'extracted {}! A single slice will be extracted.' .format(slice_size, len(sites))) warn(msg) slices = [sites] else: slices = np.array_split(sites, len(sites) // slice_size) return slices
[docs]def slice_sites(shape, chunks, sites=None, chunks_per_slice=5): """ Slice sites into given number of sub-sets with given number of chunks per sub-set Parameters ---------- shape : tuple Shape of dataset array that data is being extracted from chunks : tuple Chunk size of dataset array in .h5 file from which dataset is being extracted sites : list | slice, optional Subset of sites to extract, by default None or all sites chunks_per_slice : int, optional Number of chunks to extract in each slice, by default 5 Returns ------- slices : list List of slices to extract """ if chunks is not None: slice_size = chunks[1] * chunks_per_slice else: slice_size = chunks_per_slice * 100 if sites is None: sites = slice(None) if isinstance(sites, slice): slices = split_sites_slice(sites, shape[1], slice_size) elif isinstance(sites, (list, tuple, np.ndarray)): slices = split_sites_list(sites, slice_size) else: msg = ('sites must be of type "None", "slice", "list", "tuple", ' 'or "np.ndarray", but {} was provided'.format(type(sites))) raise TypeError(msg) return slices
[docs]def res_dist_threshold(lat_lons, tree=None, margin=1.05): """ Distance threshold for nearest neighbor searches performed on resource points. Calculated as half of the diagonal between closest resource points, with desired extra margin Parameters ---------- lat_lons : ndarray n x 2 array of resource points coordinates (lat, lon) tree : cKDTree, optional Pre-build cKDTree of resource lat, lon coordintes. If None, build the cKDTree from scratch, by default None margin : float, optional Extra margin to multiply times the computed max distance between neighboring resource points, by default 1.05 Returns ------- float Distance threshold for nearest neighbor searches performed on resource points. Calculated as half of the diagonal between closest resource points, with desired extra margin """ if tree is None: # pylint: disable=not-callable tree = cKDTree(lat_lons) dists = tree.query(lat_lons, k=2)[0][:, 1] dists = dists[(dists != 0)] return margin * (2 ** 0.5) * (dists.max() / 2)
[docs]def get_dtype(col): """ Get column dtype for converstion to records array Parameters ---------- col : pandas.Series Column from pandas DataFrame Returns ------- out : str String representation of converted dtype for column: - float = float32 - int = int16 or int32 depending on data range - object/str = U* max length of strings in col """ dtype = col.dtype if isinstance(dtype, CategoricalDtype): col = col.astype(type(col.values[0])) out = get_dtype(col) elif np.issubdtype(dtype, np.floating): out = 'float32' elif np.issubdtype(dtype, np.integer): if col.max() < 32767: out = 'int16' else: out = 'int32' elif np.issubdtype(dtype, np.object_): size = int(col.astype(str).str.len().max()) out = 'S{:}'.format(size) else: out = dtype return out
[docs]def to_records_array(df): """ Convert pandas DataFrame to numpy Records Array Parameters ---------- df : pandas.DataFrame Pandas DataFrame to be converted Returns ------- numpy.rec.array Records array of input df """ meta_arrays = [] dtypes = [] for c_name, c_data in df.iteritems(): dtype = get_dtype(c_data) if np.issubdtype(dtype, np.bytes_): data = c_data.astype(str).str.encode('utf-8').values else: data = c_data.values arr = np.array(data, dtype=dtype) meta_arrays.append(arr) dtypes.append((c_name, dtype)) return np.core.records.fromarrays(meta_arrays, dtype=dtypes)
[docs]def row_col_indices(sc_point_gids, row_length): """ Convert supply curve point gids to row and col indices given row length Parameters ---------- sc_point_gids : int | list | ndarray Supply curve point gid or list/array of gids row_length : int row length (shape[1]) Returns ------- row : int | list | ndarray row indices col : int | list | ndarray row indices """ rows = sc_point_gids // row_length cols = sc_point_gids % row_length return rows, cols
[docs]def unstupify_path(path): """ Utility to create sensical os agnostic paths from relative or local path such as: - ~/file - file - /. - ./file Parameters ---------- path : str Path or relative path Returns ------- path: str Absolute/real path """ path = os.path.expanduser(path) if not os.path.isabs(path) and not path.startswith('/'): path = os.path.realpath(path) return path
[docs]def write_json(path, data): """ Write data to given json file Parameters ---------- path : str Path to .json file to save data too data : dict Data to save to json file at path """ assert path.endswith('.json'), "path should be to a .json file" with open(path, 'w', encoding='utf-8') as f: json.dump(data, f, ensure_ascii=False, indent=4)