Source code for act.utils.datetime_utils

Module that containing utilities involving datetimes.


import datetime as dt
import warnings

import numpy as np
import pandas as pd
from scipy import stats

[docs]def dates_between(sdate, edate): """ Ths procedure returns all of the dates between *sdate* and *edate*. Parameters ---------- sdate : str The string containing the start date. The string is formatted YYYYMMDD. edate : str The string containing the end date. The string is formatted YYYYMMDD. Returns ------- all_dates : array of datetimes The array containing the dates between *sdate* and *edate*. """ days = dt.datetime.strptime(edate, '%Y%m%d') - dt.datetime.strptime(sdate, '%Y%m%d') all_dates = [ dt.datetime.strptime(sdate, '%Y%m%d') + dt.timedelta(days=d) for d in range(days.days + 1) ] return all_dates
[docs]def numpy_to_arm_date(_date, returnTime=False): """ Given a numpy datetime64, return an ARM standard date (yyyymmdd). Parameters ---------- date : numpy.datetime64 Numpy datetime64 date. returnTime : boolean If set to true, returns time instead of date Returns ------- arm_date : string or None Returns an arm date. """ from dateutil.parser._parser import ParserError try: date = pd.to_datetime(str(_date)) if returnTime is False: date = date.strftime('%Y%m%d') else: date = date.strftime('%H%M%S') except ParserError: date = None return date
[docs]def reduce_time_ranges(time, time_delta=60, broken_barh=False): """ Given a time series, this function will return a list of tuples of time ranges representing the contineous times where no data is detected missing. Parameters ---------- time : numpy datetime64 array The numpy array of date time values. time_delta : int The number of seconds to use as default time step in time array. broken_barh : boolean Option to return start time and duration instead of start time and end time. This is used with the pyplot.broken_barh() plotting routine. Returns ------- time_ranges : list of tuples with 2 numpy datetime64 times The time range(s) of contineous data. """ # Convert integer sections to numpy datetime64 time_delta = np.timedelta64(int(time_delta * 1000), 'ms') # Make a difference array to find where time difference is great than time_delta diff = np.diff(time) dd = np.where(diff > time_delta)[0] if len(dd) == 0: return [(time[0], time[-1] - time[0])] # A add to start and end of array for beginning and end values dd = np.insert(dd, 0, -1) dd = np.append(dd, len(time) - 1) # Create a list of tuples containg time ranges or start time with duration if broken_barh: return [ (time[dd[ii] + 1], time[dd[ii + 1]] - time[dd[ii] + 1]) for ii in range(len(dd) - 1) ] else: return [(time[dd[ii] + 1], time[dd[ii + 1]]) for ii in range(len(dd) - 1)]
[docs]def determine_time_delta(time, default=60): """ Returns the most likely time step in seconds by analyzing the difference in time steps. Parameters ---------- time : numpy datetime64 array The numpy array of date time values. default : int or float The default number to return if unable to calculate a value. Returns ------- time_delta : float Returns the number of seconds for the most common time step. If can't calculate a value the default value is returned. """ with warnings.catch_warnings(): warnings.filterwarnings('ignore', category=RuntimeWarning) if time.size > 1: try: mode = stats.mode(np.diff(time), keepdims=True) except TypeError: mode = stats.mode(np.diff(time)) time_delta = mode.mode[0] time_delta = time_delta.astype('timedelta64[s]').astype(float) else: time_delta = default return float(time_delta)
[docs]def datetime64_to_datetime(time): """ Given a numpy datetime64 array time series, return datetime (y, m, d, h, m, s) Parameters ---------- time : numpy datetime64 array, list of numpy datetime64 values or scalar numpy datetime64. The numpy array of date time values. Returns ------- datetime : list Returns a list of datetimes (y, m, d, h, m, s) from a time series. YYYY-MM-DD, DD.MM.YYYY, DD/MM/YYYY or YYYYMMDD. """ if isinstance(time, (tuple, list)): time = np.array(time) if len(time.shape) == 0: time = np.array([time]) datetime_array = [ dt.datetime.fromtimestamp( tm.astype('datetime64[ms]').astype('float') / 1000.0, tz=dt.timezone.utc ).replace(tzinfo=None) for tm in time ] return datetime_array
[docs]def date_parser(date_string, output_format='%Y%m%d', return_datetime=False): """Converts one datetime string to another or to a datetime object. Parameters ---------- date_string : str datetime string to be parsed. Accepted formats are YYYY-MM-DD, DD.MM.YYYY, DD/MM/YYYY or YYYYMMDD. output_format : str Format for datetime.strftime to output datetime string. return_datetime : bool If true, returns str as a datetime object. Default is False. returns ------- datetime_str : str A valid datetime string. datetime_obj : datetime.datetime A datetime object. """ date_fmts = [ '%Y-%m-%d', '%d.%m.%Y', '%d/%m/%Y', '%Y%m%d', '%Y/%m/%d', '%Y-%m-%dT%H:%M:%S', '%d.%m.%YT%H:%M:%S', '%d/%m/%YT%H:%M:%S', '%Y%m%dT%%H:%M:%S', '%Y/%m/%dT%H:%M:%S', ] for fmt in date_fmts: try: datetime_obj = dt.datetime.strptime(date_string, fmt) if return_datetime: return datetime_obj else: return datetime_obj.strftime(output_format) except ValueError: pass fmt_strings = ', '.join(date_fmts) raise ValueError('Invalid Date format, please use one of these formats ' + fmt_strings)
[docs]def adjust_timestamp(ds, time_bounds='time_bounds', align='left', offset=None): """ Will adjust the timestamp based on the time_bounds or other information so that the timestamp aligns with user preference. Will work to adjust the times based on the time_bounds variable but if it's not available will rely on the user supplied input Parameters ---------- ds : Xarray Dataset Dataset to adjust time_bounds : str Name of the time_bounds variable align : str Alignment of the time when using time_bounds. left: Sets timestamp to start of sample interval right: Sets timestamp to end of sample interval center: Sets timestamp to middle of sample interval offset : int Time in seconds to offset the timestamp. This overrides the time_bounds variable and can be positive or negative. Required to be in seconds Returns ------- ds : Xarray DataSet Adjusted DataSet """ if time_bounds in ds and offset is None: time_bounds = ds[time_bounds].values if align == 'left': time_start = [np.datetime64(t[0]) for t in time_bounds] elif align == 'right': time_start = [np.datetime64(t[1]) for t in time_bounds] elif align == 'center': time_start = [ np.datetime64(t[0]) + (np.datetime64(t[0]) - np.datetime64(t[1])) / 2.0 for t in time_bounds ] else: raise ValueError('Align should be set to one of [left, right, center]') elif offset is not None: time = ds['time'].values time_start = [t + np.timedelta64(offset, 's') for t in time] else: raise ValueError('time_bounds variable is not available') time_start = np.array(time_start).astype('datetime64[ns]') ds = ds.assign_coords({'time': time_start}) return ds