From 19d8dae5f8b8ba6dab29488f4761db0e320835ae Mon Sep 17 00:00:00 2001 From: John Volk Date: Mon, 24 Sep 2018 15:28:36 -0700 Subject: [PATCH 01/11] fix ignore to update built docs --- .gitignore | 2 +- build/lib/prms_python/__init__.py | 24 + build/lib/prms_python/data.py | 263 + build/lib/prms_python/models/lbcd/control | 362 + build/lib/prms_python/models/lbcd/data | 1571 + build/lib/prms_python/models/lbcd/parameters | 1079959 ++++++++++++++ build/lib/prms_python/optimizer.py | 888 + build/lib/prms_python/parameters.py | 575 + build/lib/prms_python/scenario.py | 601 + build/lib/prms_python/scripts/__init__.py | 0 build/lib/prms_python/scripts/prmspy.py | 225 + build/lib/prms_python/simulation.py | 411 + build/lib/prms_python/util.py | 346 + build/lib/test/test_prms_python.py | 476 + docs/build/doctrees/changelog.doctree | Bin 0 -> 10019 bytes .../html/_modules/prms_python/simulation.html | 518 + docs/build/html/_sources/changelog.rst.txt | 2 + docs/build/html/changelog.html | 162 + 18 files changed, 1086384 insertions(+), 1 deletion(-) create mode 100644 build/lib/prms_python/__init__.py create mode 100644 build/lib/prms_python/data.py create mode 100644 build/lib/prms_python/models/lbcd/control create mode 100644 build/lib/prms_python/models/lbcd/data create mode 100644 build/lib/prms_python/models/lbcd/parameters create mode 100644 build/lib/prms_python/optimizer.py create mode 100644 build/lib/prms_python/parameters.py create mode 100644 build/lib/prms_python/scenario.py create mode 100644 build/lib/prms_python/scripts/__init__.py create mode 100644 build/lib/prms_python/scripts/prmspy.py create mode 100644 build/lib/prms_python/simulation.py create mode 100644 build/lib/prms_python/util.py create mode 100644 build/lib/test/test_prms_python.py create mode 100644 docs/build/doctrees/changelog.doctree create mode 100644 docs/build/html/_modules/prms_python/simulation.html create mode 100644 docs/build/html/_sources/changelog.rst.txt create mode 100644 docs/build/html/changelog.html diff --git a/.gitignore b/.gitignore index 9137d37..67cdf2c 100644 --- a/.gitignore +++ b/.gitignore @@ -11,7 +11,7 @@ # builds for uploading to PyPI dist/ -build/ +./build/ prms_python.egg-info/ venv diff --git a/build/lib/prms_python/__init__.py b/build/lib/prms_python/__init__.py new file mode 100644 index 0000000..300fd8c --- /dev/null +++ b/build/lib/prms_python/__init__.py @@ -0,0 +1,24 @@ +""" +PRMS-Python provides a Python interface to PRMS data files and manages +PRMS simulations. This module aims to improve the efficiency of PRMS +workflows by giving access to PRMS data structures while providing +"pythonic" tools to do scenario-based PRMS simulations. By +"scenario-based" we mean testing model hypotheses associated with model +inputs, outputs, and model structure. For example, parameter sensitivity +analysis, where each "scenario" is an iterative perturbation of one or +many parameters. Another example "scenario-based" modeling exercise +would be climate scenario modeling: what will happen to modeled outputs +if the input meteorological data were to change? + +""" + +__name__ = 'prms_python' +__author__ = 'John Volk and Matthew Turner' +__version__ = '1.0.1' + +from prms_python.data import Data +from prms_python.optimizer import Optimizer, OptimizationResult +from prms_python.parameters import Parameters, modify_params +from prms_python.simulation import Simulation, SimulationSeries +from prms_python.scenario import Scenario, ScenarioSeries +from prms_python.util import load_statvar, load_data, nash_sutcliffe diff --git a/build/lib/prms_python/data.py b/build/lib/prms_python/data.py new file mode 100644 index 0000000..2e2f831 --- /dev/null +++ b/build/lib/prms_python/data.py @@ -0,0 +1,263 @@ +# -*- coding: utf-8 -*- +''' +data.py -- holds ``Data`` class for standard PRMS climate input data. +''' + +import pandas as pd +from shutil import copyfile + +class Data(object): + """ + Object to access or create a PRMS data file with ability to load/assign it to a + date-time indexed pandas.DataFrame for data management, analysis and visualization. + It can be used to build a new PRMS data file from user defined metadata and a + ``pandas.DataFrame`` of PRMS datetime-indexed climatic forcing and observation + variables. + + The class properties ``metadata`` and ``data_frame`` can be later assigned if no + ``base_file`` is given on initialization, allowing for the creation of PRMS climatic + forcing file in a Python environment. + + Keyword Arguments: + base_file (str, optional): path to standard PRMS data file + na_rep (int, optional): how to represent missing values default = -999 + + Attributes: + date_header (list): date and time header for PRMS data file + valid_input_variables (tuple): valid hydro-climate variables for PRMS data file + + Note: + If using the ``Data`` class to create a new data file, it is up to the user + to ensure that the metadata and :class:`pandas.DataFrame` assigned are correct + and compatible. + + """ + + ## data file constant attributes + date_header = ['year', + 'month', + 'day', + 'hh', + 'mm', + 'sec'] + + valid_input_variables = ('gate_ht', + 'humidity', + 'lake_elev', + 'pan_evap', + 'precip', + 'rain_day', + 'runoff', + 'snowdepth', + 'solrad', + 'tmax', + 'tmin', + 'wind_speed') + + def __init__(self, base_file=None, na_rep=-999): + self.base_file = base_file + self.na_rep = na_rep + self._metadata = None + self._data_frame = None + + @property + def metadata(self): + """ + :obj:`dict`:A property that gets and sets the header information from + a standard PRMS climate input data file held in a Python dictionary. As + a property it can be assigned directly to overwrite or create a new PRMS + data file. As such the user is in control and must supply the correct + syntax for PRMS standard data files, e.g. text lines before header should + begin with "//". Here is an example of the information gathered and held in + this attribute: + + Example: + >>> data.metadata + { + 'data_startline' : 6, + 'data_variables' : ['runoff 1', 'runoff 2', 'tmin', 'tmax', 'ppt'] + 'text_before_header' : "Title of data file \\n //some comments\\nrunoff 2 + \\ntmin 1\\ntmax 1\\nppt 1\\nrunoff 2\\ntmin 1 + \\ntmax 1\\nppt 1\\n + ########################################\\n" + } + + Note: + When assigning or creating a new data file, the ``Data.write`` method will + assign the appropriate date header that follows the line of number signs "#". + + Raises: + ValueError: if data in metadata is accessed before data is assigned, + e.g. if accessed to write a PRMS data file from a ``Data`` instance + that was initialized without a valid PRMS data file. + TypeError: if an object that is not a Python dictionary is assigned. + + """ + # to avoid overwriting pre-assigned data, check if already exists + if isinstance(self._metadata, dict): + return self._metadata + elif not self.base_file: + raise ValueError('No data file was given on initialization') + + ## starting list for variable names in data file + input_data_names = [] + text_before_header = str() + ## open data file and read header information + with open(self.base_file, 'r') as inf: + for idx,l in enumerate(inf): + text_before_header+=l + if idx == 0: ## first line always string identifier of the file- may use later + data_head = l.rstrip() + elif l.startswith('/'): ## comment lines + continue + if l.startswith(Data.valid_input_variables): + h = l.split() ## split, first name and second number of columns + if int(h[1]) > 1: ## more than one input time series of a particular variable + for el in range(int(h[1])): + tmp = '{var_name} {var_ind}'.format(var_name = h[0], var_ind = el+1) + input_data_names.append(tmp) + elif int(h[1]) == 1: + input_data_names.append(h[0]) + if l.startswith('#'): ## end of header info and begin time series input data + data_startline = idx+1 ## 0 indexed line of first data entry + break + + self._metadata = dict([('data_startline',data_startline), + ('data_variables',input_data_names), + ('text_before_header',text_before_header)]) + return self._metadata + + @metadata.setter + def metadata(self, dic): + if not isinstance(dic, dict): + raise TypeError('Must assign a Python dictionary for new Data object/file metadata') + self._metadata = dic + + @property + def data_frame(self): + """ + A property that gets and sets the climatic forcing data for a standard PRMS + climate input data file as a :class:`pandas.DataFrame`. + + Example: + d is a Data instance, calling + + >>> d.data_frame + input variables runoff 1 runoff 2 runoff 3 precip tmax tmin + date + 1996-12-27 0.54 1.6 NaN 0.0 46 32.0 + 1996-12-28 0.65 1.6 NaN 0.0 45 24.0 + 1996-12-29 0.80 1.6 NaN 0.0 44 28.0 + 1996-12-30 0.90 1.6 NaN 0.0 51 33.0 + 1996-12-31 1.00 1.7 NaN 0.0 47 32.0 + + shows the date-indexed ``pd.DataFrame`` of the input data that is created + when a ``Data`` object is initiated if given a valid ``base_file``, i.e. + file path to a PRMS climate data file. + + Raises: + ValueError: if attribute is accessed before either assigning a PRMS data + file on ``Data`` initialization or not assigning a compatabile + date-indexed ``pandas.DataFrame`` of hydro-climate variables. + TypeError: if a data type other than ``pandas.DataFrame`` is assigned. + """ + if not self._metadata: + self.metadata + elif not isinstance(self._data_frame, pd.DataFrame) and self.base_file == None: + raise ValueError('No data base_file given on initialization, '\ + 'therefore you must assign a DataFrame'\ + +' before accessing the .data_frame attribute!') + # to avoid overwriting pre-assigned data + elif isinstance(self._data_frame, pd.DataFrame): + return self._data_frame + + df = pd.read_csv(self.base_file, header = -1, skiprows = self.metadata['data_startline'], + delim_whitespace = True, na_values = [self.na_rep]) ## read data file + df.columns = Data.date_header + self.metadata['data_variables'] + date = pd.Series(pd.to_datetime(df.year * 10000 + df.month * 100 +\ + df.day, format = '%Y%m%d'), index = df.index) + df.index = pd.to_datetime(date) ## assign datetime index + df.drop(Data.date_header, axis = 1, inplace = True) ## unneeded columns + df.columns.name = 'input variables' ; df.index.name = 'date' + self._data_frame = df + return self._data_frame + + @data_frame.setter + def data_frame(self, df): + if not isinstance(df, pd.DataFrame): + raise TypeError("Must assign a Pandas.DataFrame object for PRMS data input") + self._data_frame = df + + def modify(self, func, vars_to_adjust): + """ + Apply a user defined function to one or more variable(s) in the data file. + + The ``modify`` method allows for inplace modification of one or more + time series inputs in the data file based on a user defined function. + + Arguments: + func (function): function to apply to each variable in vars_to_adjust + vars_to_adjust (list or tuple): collection of variable names to apply func to. + + Returns: + None + + Example: + Here is an example of loading a data file, modifying the temperature inputs + (*tmin* and *tmax*) by adding two degrees to each element, and rewritting the + modified data to disk, + + >>> d = Data('path_to_data_file') + >>> def f(x): + return x + 2 + >>> d.modify(f,['tmax','tmin']) + >>> d.write('data_temp_plus_2') + """ + + if not isinstance(self._data_frame, pd.DataFrame): + self.data_frame # will raise ValueError from data_frame property + for v in vars_to_adjust: + self._data_frame[v] = self._data_frame[v].apply(func) + + def write(self, out_path): + """ + Writes the current state of the ``Data`` to PRMS text format + utilizing the ``Data.metadata`` and ``Data.data_frame`` instance + properties. If ``Data.data_frame`` was never accessed or assigned + new values then this method simply copies the original PRMS + data file to ``out_path``. + + Arguments: + out_path (str): full path to save or copy the current PRMS data + in PRMS text format. + + Returns: + None + + Raises: + ValueError: if the ``write`` method is called without assigning either + an initial data (``base_file``) path or assigning correct ``metadata`` + and ``data_frame`` properties. + + """ + # if file data was never accessed- unchanged + if not isinstance(self._data_frame, pd.DataFrame): + if self.base_file: + copyfile(self.base_file, out_path) + else: # if data not from original file and dataframe never assigned + raise ValueError('No data base_file was given and'\ + +' no data was assigned!') + + ## reconstruct PRMS data file format, don't overwrite date-indexed + else: + df = self._data_frame[self.metadata['data_variables']] + df['year'] = self._data_frame.index.year + df['month'] = self._data_frame.index.month + df['day'] = self._data_frame.index.day + df['hh'] = df['mm'] = df['sec'] = 0 + df = df[Data.date_header + self._metadata['data_variables']] + with open(out_path,'w') as outf: # write comment header then data + outf.write(self._metadata['text_before_header']) + df.to_csv(outf, sep=' ', header=None,\ + index=False, na_rep=self.na_rep) + diff --git a/build/lib/prms_python/models/lbcd/control b/build/lib/prms_python/models/lbcd/control new file mode 100644 index 0000000..1033436 --- /dev/null +++ b/build/lib/prms_python/models/lbcd/control @@ -0,0 +1,362 @@ +GSFLOW Control File for Lehman and Baker Creek, John Volk 2014 all rights reserved +#### +aniOutON_OFF +1 +1 +0 +#### +print_debug +1 +1 +-1 +#### +aniOutVar_names +5 +4 +upslope_interflow +upslope_dunnianflow +soil_moist_tot +upslope_hortonian +sroff +#### +ani_output_file +1 +4 +animation.out +#### +csv_output_file +1 +4 +gsflow.csv +#### +model_output_file +1 +4 +prms.out +#### +data_file +1 +4 +data +#### +dispGraphsBuffSize +1 +4 +15 +#### +dispVar_element +15 +4 +2 +2 +2 +2 +2 +1 +2 +1 +1 +1 +2 +1 +1 +1 +1 +#### +dispVar_names +15 +4 +runoff +sub_cfs +sub_gwflow +sub_interflow +sub_sroff +basin_pweqv +subinc_pkweqv +basin_dunnian +basin_prefflow +basin_slowflow +subinc_precip +basin_rain +basin_snow +basin_tmax +basin_tmin +#### +dispVar_plot +15 +1 +1 +1 +2 +2 +2 +3 +3 +4 +4 +4 +5 +5 +5 +6 +6 +#### +end_time +6 +1 +1996 +12 +31 +0 +0 +0 +#### +et_module +1 +4 +potet_jh +#### +executable_desc +1 +4 +GSFLOW model +#### +executable_model +1 +4 +c:\workspace\mows_software\bin\prms-iv +#### +gsflow_output_file +1 +4 +gsflow.out +#### +init_vars_from_file +1 +1 +0 +#### +initial_deltat +1 +2 +24.0 +#### +mms_user_dir +1 +4 +.\ +#### +mms_user_out_dir +1 +4 +.\ +#### +model_mode +1 +4 +PRMS +#### +modflow_name +1 +4 +lbcd.nam +#### +naniOutVars +1 +1 +5 +#### +ndispGraphs +1 +1 +6 +#### +nstatVars +1 +1 +41 +#### +param_file +1 +4 +parameters +#### +precip_module +1 +4 +precip_1sta +#### +param_print_file +1 +4 +lbcd.parprt +#### +save_vars_to_file +1 +1 +1 +#### +solrad_module +1 +4 +ddsolrad +#### +srunoff_module +1 +4 +srunoff_smidx +#### +start_time +6 +1 +1992 +10 +1 +0 +0 +0 +#### +statVar_element +41 +4 +1 +1 +1 +1 +1 +1 +1 +1 +2 +2 +2 +2 +3 +3 +3 +3 +1 +2 +3 +1 +2 +3 +1 +2 +3 +1 +2 +3 +1 +2 +3 +1 +2 +3 +1 +2490 +1 +1 +1 +1 +2 +#### +statVar_names +41 +4 +basin_gwflow_cfs +basin_sroff_cfs +basin_ssflow_cfs +basin_cfs +sub_cfs +sub_interflow +sub_sroff +sub_gwflow +sub_cfs +sub_interflow +sub_sroff +sub_gwflow +sub_cfs +sub_interflow +sub_sroff +sub_gwflow +subinc_actet +subinc_actet +subinc_actet +subinc_snowcov +subinc_snowcov +subinc_snowcov +sub_sroff +sub_sroff +sub_sroff +subinc_pkweqv +subinc_pkweqv +subinc_pkweqv +subinc_precip +subinc_precip +subinc_precip +subinc_snowmelt +subinc_snowmelt +subinc_snowmelt +basin_potet +swrad +basin_ppt +basin_hortonian +basin_dunnian +subinc_snowmelt +subinc_snowmelt +#### +stat_var_file +1 +4 +statvar.dat +#### +statsON_OFF +1 +1 +1 +#### +cascade_flag +1 +1 +1 +#### +cascadegw_flag +1 +1 +1 +#### +temp_module +1 +4 +temp_1sta +#### +var_save_file +1 +4 +prms_ic.out +#### +stats_output_file +1 +4 +stats1.out +#### +var_init_file +1 +4 +vars +#### +rpt_days +1 +1 +1 +#### +capillary_module +1 +4 +soilzone_prms +#### +strmflow_module +1 +4 +strmflow diff --git a/build/lib/prms_python/models/lbcd/data b/build/lib/prms_python/models/lbcd/data new file mode 100644 index 0000000..857f114 --- /dev/null +++ b/build/lib/prms_python/models/lbcd/data @@ -0,0 +1,1571 @@ +Data file created by John Volk 2014 all rights reserved +//////////////////////////////////////////////////////////// +// Station metadata (listed in the same order as the data): +// ID Type Latitude Longitude Elevation Stream +// 10243240 runoff 38.9908 -114.2067 6750 Baker +// 10243260 runoff 39.0116 -114.2144 6700 Lehman +// 10243265 runoff 39.0086 -114.2078 6580 Rowland +// 263340 precip 39.0056 -114.2206 6850 +// 263340 tmax 39.0056 -114.2206 6850 +// 263340 tmin 39.0056 -114.2206 6850 +//////////////////////////////////////////////////////////// +// Unit: runoff = ft3 per "sec " precip = "in " temperature = deg "F " elevation = feet +//////////////////////////////////////////////////////////// +runoff 3 +precip 1 +tmax 1 +tmin 1 +######################################## +1992 10 1 0 0 0 1.2 1.9 -999 0 77 52 +1992 10 2 0 0 0 1.3 2 -999 0 75 53 +1992 10 3 0 0 0 1.4 2.1 -999 0 62 44 +1992 10 4 0 0 0 1.5 2.2 -999 0 62 34 +1992 10 5 0 0 0 1.5 2.2 -999 0 62 35 +1992 10 6 0 0 0 1.5 2.2 -999 0 67 38 +1992 10 7 0 0 0 1.5 2.1 -999 0 64 30 +1992 10 8 0 0 0 1.5 2 -999 0 72 30 +1992 10 9 0 0 0 1.5 1.8 -999 0 68 46 +1992 10 10 0 0 0 1.5 1.6 -999 0 66 37 +1992 10 11 0 0 0 1.4 1.6 -999 0 70 37 +1992 10 12 0 0 0 1.4 1.5 -999 0 76 47 +1992 10 13 0 0 0 1.5 1.5 -999 0 77 54 +1992 10 14 0 0 0 1.5 1.5 -999 0 75 41 +1992 10 15 0 0 0 1.5 1.5 -999 0 73 34 +1992 10 16 0 0 0 1.5 1.6 -999 0 65 41 +1992 10 17 0 0 0 1.5 1.6 -999 0 64 40 +1992 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b/build/lib/prms_python/models/lbcd/parameters @@ -0,0 +1,1079959 @@ +PRMS parameter file for Lehman and Baker Creek, John Volk 2014 all rights reserved +Version: 1.7 +** Dimensions ** +#### +ngwcell +10791 +#### +ntemp +1 +#### +nrain +1 +#### +nreach +289 +#### +ndays +366 +#### +nsegment +15 +#### +ncascade +13884 +#### +nevap +0 +#### +nobs +3 +#### +nsub +3 +#### +ndeplval +22 +#### +ngw +10791 +#### +nhrucell +10791 +#### +nmonths +12 +#### +nsol +0 +#### +nssr +10791 +#### +one +1 +#### +ndepl +2 +#### +nform +0 +#### +ncascdgw +13884 +#### +nhru +10791 +** Parameters ** +#### +smidx_exp +1 +nhru +10791 +2 +0.29952 +0.3 +0.30147 +0.30156 +0.30048 +0.30108 +0.30147 +0.30156 +0.30363 +0.30678 +0.30738 +0.30363 +0.30138 +0.30009 +0.2997 +0.29922 +0.29952 +0.30267 +0.3072 +0.31062 +0.31515 +0.3192 +0.32274 +0.32352 +0.32244 +0.31929 +0.31605 +0.31161 +0.30768 +0.30444 +0.30117 +0.29724 +0.29418 +0.29007 +0.2874 +0.28584 +0.28455 +0.28347 +0.28239 +0.28092 +0.27894 +0.27708 +0.27471 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management conducted on PRMS parameters. +''' +from __future__ import print_function +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import os, sys, json, re, shutil +import multiprocessing as mp +from copy import copy +from copy import deepcopy +from numpy import log10 +from datetime import datetime +from .data import Data +from .parameters import Parameters +from .simulation import Simulation, SimulationSeries +from .util import load_statvar, nash_sutcliffe, percent_bias, rmse + +OPJ = os.path.join + + +class Optimizer: + ''' + Container for PRMS parameter optimization and related routines. + + Currently the ``monte_carlo`` method provides random parameter + resampling routines using uniform and normal random variables. + + Example: + >>> from prms_python import Data, Optimizer, Parameters + >>> params = Parameters('path/to/parameters') + >>> data = Data('path/to/data') + >>> control = 'path/to/control' + >>> work_directory = 'path/to/create/simulations' + >>> optr = Optimizer( + params, + data, + control, + work_directory, + title='the title', + description='desc') + >>> measured = 'path/to/measured/csv' + >>> statvar_name = 'basin_cfs' # or any other valid statvar + >>> params_to_resample = ['dday_intcp', 'dday_slope'] # list of params + >>> optr.monte_carlo(measured, params_to_resample, statvar_name) + + ''' + + #dic for min/max of parameter allowable ranges, add more when needed + param_ranges = {'dday_intcp': (-60.0, 10.0), + 'dday_slope': (0.2, 0.9), + 'jh_coef': (0.005, 0.06), + 'pt_alpha': (1.0, 2.0), + 'potet_coef_hru_mo': (1.0, 2.0), + 'tmax_index': (-10.0, 110.0), + 'tmin_lapse': (-10.0, 10.0), + 'soil_moist_max': (0.001, 10.0), + 'rain_adj': (0.5, 2.0), + 'ppt_rad_adj': (0.0, 0.5), + 'radadj_intcp': (0.0, 1.0), + 'radadj_slope': (0.0, 1.0), + 'radj_sppt': (0.0, 1.0), + 'radj_wppt': (0.0, 1.0), + 'radmax': (0.1, 1.0) + } + + def __init__(self, parameters, data, control_file, working_dir, + title, description=None): + + if isinstance(parameters, Parameters): + self.parameters = parameters + else: + raise TypeError('parameters must be instance of Parameters') + + if isinstance(data, Data): + self.data = data + + else: + raise TypeError('data must be instance of Data') + + input_dir = '{}'.format(os.sep).join(control_file.split(os.sep)[:-1]) + if not os.path.isfile(OPJ(input_dir, 'statvar.dat')): + print('You have no statvar.dat file in your model directory') + print('Running PRMS on original data in {} for later comparison'\ + .format(input_dir)) + sim = Simulation(input_dir) + sim.run() + + if not os.path.isdir(working_dir): + os.mkdir(working_dir) + + self.input_dir = input_dir + self.control_file = control_file + self.working_dir = working_dir + self.title = title + self.description = description + self.measured_arb = None # for arbitrary output methods + self.statvar_name = None + self.arb_outputs = [] + + def monte_carlo(self, reference_path, param_names, statvar_name, \ + stage, n_sims=10, method='uniform', mu_factor=1,\ + noise_factor=0.1, nproc=None): + ''' + The ``monte_carlo`` method of ``Optimizer`` performs parameter + random resampling techniques to a set of PRMS parameters and + executes and manages the corresponding simulations. + + Arguments: + reference_path (str): path to measured data for optimization + param_names (list): list of parameter names to resample + statvar_name (str): name of statisical variable output name for + optimization + stage (str): custom name of optimization stage e.g. 'ddsolrad' + + Keyword Arguments: + n_sims (int): number of simulations to conduct + parameter optimization/uncertaitnty analysis. + method (str): resampling method for parameters (normal or uniform) + mu_factor (float): coefficient to scale mean of the parameter(s) + to resample from when using the normal distribution to resample + i.e. a value of 1.5 will sample from a normal rv with mean + 50% higher than the original parameter mean + noise_factor (float): scales the variance of noise to add to + parameter values when using normal rv (method='normal') + nproc (int): number of processors available to run PRMS simulations + + Returns: + None + ''' + if '_' in stage: + raise ValueError('stage name cannot contain an underscore') + # assign the optimization object a copy of measured data for plots + self.measured_arb = pd.Series.from_csv(reference_path,\ + parse_dates=True) + # statistical variable output name + self.statvar_name = statvar_name + + start_time = datetime.now().isoformat() + + # resample params for all simulations- potential place to serialize + params = [] + for name in param_names: # create list of lists of resampled params + tmp = [] + for idx in range(n_sims): + tmp.append(resample_param(self.parameters, name, how=method,\ + mu_factor=mu_factor, noise_factor=noise_factor)) + params.append(list(tmp)) + + # SimulationSeries comprised of each resampled param set + series = SimulationSeries( + Simulation.from_data( + self.data, _mod_params(self.parameters,\ + [params[n][i] for n in range(len(params))],\ + param_names), + self.control_file, + OPJ( + self.working_dir, # name of sim: first param and mean value + '{0}_{1:.10f}'.format(param_names[0], np.mean(params[0][i])) + ) + ) + for i in range(n_sims) + ) + + if not nproc: + nproc = mp.cpu_count() // 2 + + # run + outputs = list(series.run(nproc=nproc).outputs_iter()) + self.arb_outputs.extend(outputs) # for current instance- add outputs + + end_time = datetime.now().isoformat() + + # json metadata for Monte Carlo run + meta = { 'params_adjusted' : param_names, + 'statvar_name' : self.statvar_name, + 'optimization_title' : self.title, + 'optimization_description' : self.description, + 'start_datetime' : start_time, + 'end_datetime' : end_time, + 'measured' : reference_path, + 'method' : 'Monte Carlo', + 'mu_factor' : mu_factor, + 'noise_factor' : noise_factor, + 'resample': method, + 'sim_dirs' : [], + 'stage': stage, + 'original_params' : self.parameters.base_file, + 'nproc': nproc, + 'n_sims' : n_sims + } + + for output in outputs: + meta['sim_dirs'].append(output['simulation_dir']) + + json_outfile = OPJ(self.working_dir, _create_metafile_name(\ + self.working_dir, self.title, stage)) + + with open(json_outfile, 'w') as outf: + json.dump(meta, outf, sort_keys=True, indent=4,\ + ensure_ascii=False) + + print('{0}\nOutput information sent to {1}\n'.\ + format('-' * 80, json_outfile)) + + def plot_optimization(self, freq='daily', method='time_series',\ + plot_vars='both', plot_1to1=True, return_fig=False,\ + n_plots=4): + """ + Basic plotting of current optimization results with limited options. + Plots measured, original simluated, and optimization simulated variabes + Not recommended for plotting results when n_sims is very large, instead + use options from an OptimizationResult object, or employ a user-defined + method using the result data. + + Keyword Arguments: + freq (str): frequency of time series plots, value can be 'daily' + or 'monthly' for solar radiation + method (str): 'time_series' for time series sub plot of each + simulation alongside measured radiation. Another choice is + 'correlation' which plots each measured daily solar radiation + value versus the corresponding simulated variable as subplots + one for each simulation in the optimization. With coefficients + of determiniationi i.e. square of pearson correlation coef. + plot_vars (str): what to plot alongside simulated srad: + 'meas': plot simulated along with measured swrad + 'orig': plot simulated along with the original simulated swrad + 'both': plot simulated, with original simulation and measured + plot_1to1 (bool): if True plot one to one line on correlation + scatter plot, otherwise exclude. + return_fig (bool): flag whether to return matplotlib figure + + Returns: + f (:obj:`matplotlib.figure.Figure`): If kwarg return_fig=True, then return + copy of the figure that is generated to the user. + """ + if not self.arb_outputs: + raise ValueError('You have not run any optimizations') + var_name = self.statvar_name + X = self.measured_arb + idx = X.index.intersection(load_statvar(self.arb_outputs[0]\ + ['statvar'])['{}'.format(var_name)].index) + X = X[idx] + orig = load_statvar(OPJ(self.input_dir, 'statvar.dat'))['{}'\ + .format(var_name)][idx] + meas = self.measured_arb[idx] + sims = [load_statvar(out['statvar'])['{}'.format(var_name)][idx] for \ + out in self.arb_outputs] + simdirs = [out['simulation_dir'].split(os.sep)[-1].\ + replace('_', ' ') for out in self.arb_outputs] + var_name = '{}'.format(self.statvar_name) + n = len(self.arb_outputs) # number of simulations to plot + # user defined number of subplots from first n_plots results + if (n > n_plots): n = n_plots + # styles for each plot + ms = 4 # markersize for all points + orig_sty = dict(linestyle='none',markersize=ms,\ + markerfacecolor='none', marker='s',\ + markeredgecolor='royalblue', color='royalblue') + meas_sty = dict(linestyle='none',markersize=ms+1,\ + markerfacecolor='none', marker='1',\ + markeredgecolor='k', color='k') + sim_sty = dict(linestyle='none',markersize=ms,\ + markerfacecolor='none', marker='o',\ + markeredgecolor='r', color='r') + ## number of subplots and rows (two plots per row) + nrow = n//2 # round down if odd n + ncol = 2 + odd_n = False + if n/2. - nrow == 0.5: + nrow+=1 # odd number need extra row + odd_n = True + ######## + ## Start plots depnding on key word arguments + ######## + if freq == 'daily' and method == 'time_series': + fig, ax = plt.subplots(n, sharex=True, sharey=True,\ + figsize=(12,n*3.5)) + axs = ax.ravel() + for i,sim in enumerate(sims[:n]): + if plot_vars in ('meas', 'both'): + axs[i].plot(meas, label='Measured', **meas_sty) + if plot_vars in ('orig', 'both'): + axs[i].plot(orig, label='Original sim.', **orig_sty) + axs[i].plot(sim, **sim_sty) + axs[i].set_ylabel('sim: {}'.format(simdirs[i]), fontsize=10) + if i == 0: axs[i].legend(markerscale=5, loc='best') + fig.subplots_adjust(hspace=0) + fig.autofmt_xdate() + #monthly means + elif freq == 'monthly' and method == 'time_series': + # compute monthly means + meas = meas.groupby(meas.index.month).mean() + orig = orig.groupby(orig.index.month).mean() + # change line styles for monthly plots to lines not points + for d in (orig_sty, meas_sty, sim_sty): + d['linestyle'] = '-' + d['marker'] = None + + fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=(12,n*3.5)) + axs = ax.ravel() + for i,sim in enumerate(sims[:n]): + if plot_vars in ('meas', 'both'): + axs[i].plot(meas, label='Measured', **meas_sty) + if plot_vars in ('orig', 'both'): + axs[i].plot(orig, label='Original sim.', **orig_sty) + sim = sim.groupby(sim.index.month).mean() + axs[i].plot(sim, **sim_sty) + axs[i].set_ylabel('sim: {}\nmean'.format(simdirs[i]),\ + fontsize=10) + axs[i].set_xlim(0.5,12.5) + if i == 0: axs[i].legend(markerscale=5, loc='best') + if odd_n: # empty subplot if odd number of simulations + fig.delaxes(axs[n]) + fig.text(0.5, 0.1, 'month') + #x-y scatter + elif method == 'correlation': + ## figure + fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=(12,n*3)) + axs = ax.ravel() + ## subplot dimensions + meas_min = min(X) + meas_max = max(X) + + for i, sim in enumerate(sims[:n]): + Y = sim + sim_max = max(Y) + sim_min = min(Y) + m = max(meas_max,sim_max) + if plot_1to1: + axs[i].plot([0, m], [0, m], 'k--', lw=2) ## one to one line + axs[i].set_xlim(meas_min,meas_max) + axs[i].set_ylim(sim_min, sim_max) + axs[i].plot(X, Y, **sim_sty) + axs[i].set_ylabel('sim: {}'.format(simdirs[i])) + axs[i].set_xlabel('Measured {}'.format(var_name)) + axs[i].text(0.05, 0.95,r'$R^2 = {0:.2f}$'.format(\ + X.corr(Y)**2), fontsize=16,\ + ha='left', va='center', transform=axs[i].transAxes) + if odd_n: # empty subplot if odd number of simulations + fig.delaxes(axs[n]) + + if return_fig: + return fig + +def _create_metafile_name(out_dir, opt_title, stage): + """ + Search through output directory where simulations are conducted + look for all metadata simulation json files and find out if the + current simulation is a replicate. Then use that information to + build the correct file name for the output json file. The series + are typically run in parallel that is why this step has to be + done after running multiple simulations from an optimization stage. + + Arguments: + out_dir (str): path to directory with model results, i.e. + location where simulation series outputs and optimization + json files are located, aka Optimizer.working_dir + opt_title (str): optimization instance title for file search + stage (str): stage of optimization, e.g. 'swrad', 'pet' + + Returns: + name (str): file name for the current optimization simulation series + metadata json file. E.g 'dry_creek_swrad_opt.json', or if + this is the second time you have run an optimization titled + 'dry_creek' the next json file will be returned as + 'dry_creek_swrad_opt1.json' and so on with integer increments + """ + meta_re = re.compile(r'^{}_{}_opt(\d*)\.json'.format(opt_title, stage)) + reps = [] + for f in os.listdir(out_dir): + if meta_re.match(f): + nrep = meta_re.match(f).group(1) + if nrep == '': + reps.append(0) + else: + reps.append(nrep) + + if not reps: + name = '{}_{}_opt.json'.format(opt_title, stage) + else: + # this is the nth optimization done under the same title + n = max(map(int, reps)) + 1 + name = '{}_{}_opt{}.json'.format(opt_title, stage, n) + return name + +def resample_param(params, param_name, how='uniform', mu_factor=1,\ + noise_factor=0.1): + """ + Resample PRMS parameter by shifting all values by a constant that is + taken from a uniform distribution, where the range of the uniform + values is equal to the difference between the min and max of the allowable + range. The parameter min and max are set in ``Optimizer.param_ranges``. + If the resampling method (``how`` argument) is set to 'normal', randomly + sample a normal distribution with mean = mean(parameter) X ``mu_factor`` and + sigma = param allowable range multiplied by ``noise_factor``. If parameters have + array length <= 366 then individual parameter values are resampled otherwise + resample all param values at once, e.g. by taking a single random value + from the uniform distribution. If they are taking all at once using the + normal method then the original values are scaled by mu_factor and a normal + random variable with mean=0 and std dev = parameter range X ``noise_factor``. + + Arguments: + params (:class:`prms_python.Parameters`): ``Parameters`` object + param_name (str): name of PRMS parameter to resample + + Keyword Arguments: + how (str): distribution to resample parameters from in the case + that each parameter element can be resampled (len <=366) + Currently works for uniform and normal distributions. + noise_factor (float): factor to multiply parameter range by, + use the result as the standard deviation for the normal rand. + variable used to add element wise noise. i.e. higher + noise_factor will result in higher variance. Must be > 0. + + Returns: + ret (:obj:`numpy.ndarry`): ndarray of param after resampling + + Raises: + KeyError: if ``param_name`` not a valid parameter name + ValueError: if the parameter range has not been set in + ``Optimizer.param_ranges`` + """ + p_min, p_max = Optimizer.param_ranges.get(param_name,(-1,-1)) + + # create dictionary of parameter basic info (not values) + param_dic = {param['name']: param for param in params.base_params} + if not param_dic.get(param_name): + raise KeyError('{} is not a valid parameter'.format(param_name)) + + if p_min == p_max == -1: + raise ValueError("""{} has not been added to the dictionary of + parameters to resample, add it's allowable min and max value + to the Optimizer.param_ranges attribute in + Optimizer.py""".format(param_name)) + + dim_case = None + nhru = params.dimensions['nhru'] + ndims = param_dic.get(param_name)['ndims'] + dimnames = param_dic.get(param_name)['dimnames'] + length = param_dic.get(param_name)['length'] + param = deepcopy(params[param_name]) + + # could expand list and check parameter name also e.g. cascade_flg + # is a parameter that should not be changed + dims_to_not_change = set(['ncascade','ncascdgw','nreach',\ + 'nsegment']) + if (len(set.intersection(dims_to_not_change, set(dimnames))) > 0): + raise ValueError("""{} should not be resampled as + it relates to the location of cascade flow + parameters.""".format(param_name)) + + # use param info to get dimension info- e.g. if multidimensional + if (ndims == 1 and length <= 366): + dim_case = 'resample_each_value' # for smaller param dimensions + elif (ndims == 1 and length > 366): + dim_case = 'resample_all_values_once' # covers nssr, ngw, etc. + elif (ndims == 2 and dimnames[1] == 'nmonths' and \ + nhru == params.dimensions[dimnames[0]]): + dim_case = 'nhru_nmonths' + elif not dim_case: + raise ValueError('The {} parameter is not set for resampling'.\ + format(param_name)) +# #testing purposes +# print('name: ', param_name) +# print('max_val: ', p_max) +# print('min_val: ', p_min) +# print('ndims: ', ndims) +# print('dimnames: ', dimnames) +# print('length: ', length) +# print('resample_method: ', dim_case) + + s = (p_max - p_min) * noise_factor # std_dev (s) default: param_range/10 + #do resampling based on param dimensions and sampling distribution + if dim_case == 'resample_all_values_once': + if how == 'uniform': + ret = np.random.uniform(low=p_min, high=p_max, size=param.shape) + elif how == 'normal': # scale parameter mean if mu_factor given + param *= mu_factor + tmp = np.random.normal(0, s, size=param.shape) + ret = tmp + param + + elif dim_case == 'resample_each_value': + ret = param + if how == 'uniform': + ret = np.random.uniform(low=p_min, high=p_max, size=param.shape) + elif how == 'normal': # the original value is considered the mean + if len(ret.shape) != 0: + for i, el in enumerate(param): + mu = el * mu_factor + ret[i] = np.random.normal(mu, s) + else: # single value parameter + mu = param * mu_factor + ret = np.random.normal(mu, s) + + # nhru by nmonth dimensional params + elif dim_case == 'nhru_nmonths': + ret = param + if how == 'uniform': + for month in range(12): + ret[month] = np.random.uniform(low=p_min, high=p_max,\ + size=param[0].shape) + elif how == 'normal': + for i, el in enumerate(param): + el *= mu_factor + tmp = np.random.normal(0, s, size=el.shape) + ret[i] = tmp + el + + return ret + +def _mod_params(parameters, params, param_names): + # loop through list of params and assign their values to Parameter instance + ret = copy(parameters) + for idx, param in enumerate(params): + ret[param_names[idx]] = np.array(param) + return ret + + +class OptimizationResult: + """ + The ``OptimizationResult`` object serves to collect and manage output + from an ``Optimizer`` method. Upon initialization and a given optimization + stage that was used when running the Optimizer method, e.g. ``monte_carlo``, + the class gathers all JSON metadata that was produced for the given stage. + The ``OptimizationResult`` has three main user methods: first ``result_table`` + which returns the top n simulations according to four model performance + metrics (Nash-Sutcliffe efficiency (NSE), root-mean squared-error (RMSE), + percent bias (PBIAS), and the coefficient of determination (COEF_DET) as + calculated against measured data. For example the table may look like: + + >>> ddsolrad_res = OptimizationResult(work_directory, stage=stage) + >>> top10 = ddsolrad_res.result_table(freq='monthly',top_n=10) + >>> top10 + ======================== ======== ======= ========= ======== + ddsolrad parameters NSE RMSE PBIAS COEF_DET + ======================== ======== ======= ========= ======== + orig_params 0.956267 39.4725 -0.885715 0.963116 + tmax_index_54.2224631748 0.921626 47.6092 -0.849256 0.94402 + tmax_index_44.8823940703 0.879965 58.9194 5.79603 0.922021 + tmax_index_47.6835387480 0.764133 82.5918 -4.78896 0.837582 + ======================== ======== ======= ========= ======== + + Second, the ``get_top_ranked_sims`` which returns a dictionary that map + key information about the top n ranked simulations, an example returned + dictionary may look like: + + >>> { + 'dir_name' : ['pathToSim1', 'pathToSim2'], + 'param_path' : ['pathToSim1/input/parameters', 'pathToSim2/input/parameters'], + 'statvar_path' : ['pathToSim1/output/statvar.dat', 'pathToSim2/output/statvar.dat'], + 'params_adjusted' : [[param_names_sim1], [param_names_sim2]] + } + + The third method of ``OptimizationResult`` is ``archive`` which essentially + opens all parameter and statvar files from each simulation of the given + stage and archives the parameters that were modified and their modified values + and the statistical variable (PRMS time series output) that is associated with + the optimization stage. Other ``Optimizer`` simulation metadata is also gathered + and new JSON metadata containing only this information is created and written + within a newly created "archived" subdirectory within the same directory that + the ``Optimizer`` routine managed simulations. The ``OptimizationResult.archive`` + method then recursively deletes the simulation data for each of the given stage. + """ + + def __init__(self, working_dir, stage): + """ + Create an ``OptimizationResult`` instance to manage output and analyse parameter- + output relationships as produced by the use of an ``Optimizer`` method of a user + defined optimization stage. + """ + self.working_dir = working_dir + self.stage = stage + self.metadata_json_paths = self._get_optr_jsons(working_dir, stage) + self.total_sims = self._count_total_sims() + self.statvar_name = self._get_statvar_name(stage) + self.measured = self._get_measured(stage) + self.input_dir = self._get_input_dir(stage) + self.input_params = self._get_input_params(stage) + + # if there are more than one input param for given stage + if len(self.input_params) > 1: + print( +"""Warning: there were more than one initial parameter sets used for the + optimization for stage: {}. Make sure to compare the the correct input + params with their corresponding output sims.""".format(stage)) + print('\nThis optimization stage used the\ + following input parameter files:\n{}'.format('\n'.join(self.input_params))) + + def _count_total_sims(self): + # total number of simulations of given stage in working directory + tracked_dirs = [] + for f in self.metadata_json_paths[self.stage]: + with open(f) as fh: + json_data = json.load(fh) + tracked_dirs.extend(json_data.get('sim_dirs')) + return len(tracked_dirs) + + def _get_optr_jsons(self, work_dir, stage): + """ + Retrieve locations of optimization output jsons which contain + metadata needed to understand optimization results. + Create dictionary of each optimization with stage as key and lists + of corresponding json file paths as values. + + Arguments: + work_dir (str): path to directory with model results, i.e. + location where simulation series outputs and optimization + json files are located, aka Optimizer.working_dir + stage (str): the stage ('ddsolrad', 'jhpet', 'flow', etc.) of + the optimization in which to gather the jsons + + Returns: + ret (dict): dictionary of stage (keys) and lists of + json file paths for that stage (values). + """ + + ret = {} + optr_metafile_re = re.compile(r'^.*_{}_opt(\d*)\.json'.\ + format(stage)) + ret[stage] = [OPJ(work_dir, f) for f in\ + os.listdir(work_dir) if\ + optr_metafile_re.match(f)] + return ret + + def _get_input_dir(self, stage): + # retrieves the input directory from the first json file of given stage + json_file = self.metadata_json_paths[stage][0] + with open(json_file) as jf: + meta_dic = json.load(jf) + return '{}'.format(os.sep).join(meta_dic['original_params'].\ + split(os.sep)[:-1]) + + def _get_input_params(self, stage): + json_files = self.metadata_json_paths[stage] + param_paths = [] + for json_file in json_files: + with open(json_file) as jf: + meta_dic = json.load(jf) + param_paths.append(meta_dic['original_params']) + return list(set(param_paths)) + + def _get_sim_dirs(self, stage): + jsons = self.metadata_json_paths[stage] + json_files = [] + sim_dirs = [] + for inf in jsons: + with open(inf) as json_file: + json_files.append(json.load(json_file)) + for json_file in json_files: + sim_dirs.extend(json_file['sim_dirs']) + # list of all simulation directory paths for stage + return sim_dirs + + def _get_measured(self, stage): + # only need to open one json file to get this information + if not self.metadata_json_paths.get(stage): + return # no optimization json files exist for given stage + first_json = self.metadata_json_paths[stage][0] + with open(first_json) as json_file: + json_data = json.load(json_file) + measured_series = pd.Series.from_csv(json_data.get('measured'),\ + parse_dates=True) + return measured_series + + def _get_statvar_name(self, stage): + # only need to open one json file to get this information + try: + first_json = self.metadata_json_paths[stage][0] + except: + raise ValueError("""No optimization has been run for + stage: {}""".format(stage)) + with open(first_json) as json_file: + json_data = json.load(json_file) + var_name = json_data.get('statvar_name') + + return var_name + + def result_table(self, freq='daily', top_n=5, latex=False): + ##TODO: add stats for freq options annual (means or sum) + + sim_dirs = self._get_sim_dirs(self.stage) + if top_n >= len(sim_dirs): + top_n = len(sim_dirs) + 1 # for returning inclusive last sim + sim_names = [path.split(os.sep)[-1] for path in sim_dirs] + meas_var = self._get_measured(self.stage) + statvar_name = self._get_statvar_name(self.stage) + orig_statvar = load_statvar(OPJ(self.input_dir,'statvar.dat'))\ + [statvar_name] + + result_df = pd.DataFrame(columns=\ + ['NSE','RMSE','PBIAS','COEF_DET','ABS(PBIAS)']) + orig_results = pd.DataFrame(index=['orig_params'],\ + columns=['NSE','RMSE','PBIAS','COEF_DET']) + # get datetime indices that overlap from measured and simulated + sim_out = load_statvar(OPJ(sim_dirs[0], 'outputs', 'statvar.dat'))\ + [statvar_name] + idx = meas_var.index.intersection(sim_out.index) + meas_var = copy(meas_var[idx]) + #sim_out = sim_out[idx] + orig_statvar = orig_statvar[idx] + + if freq == 'monthly': + meas_mo = meas_var.groupby(meas_var.index.month).mean() + orig_mo = orig_statvar.groupby(orig_statvar.index.month).mean() + + for i, sim in enumerate(sim_dirs): + try: + sim_out = load_statvar(OPJ(sim, 'outputs', 'statvar.dat'))\ + ['{}'.format(statvar_name)] + except: # simulation might have been removed or missing + pass + + sim_out = sim_out[idx] + if freq == 'daily': + result_df.loc[sim_names[i]] = [\ + nash_sutcliffe(meas_var, sim_out),\ + rmse(meas_var, sim_out),\ + percent_bias(meas_var,sim_out),\ + meas_var.corr(sim_out)**2,\ + np.abs(percent_bias(meas_var, sim_out)) ] + orig_results.loc['orig_params'] = [\ + nash_sutcliffe(orig_statvar,meas_var),\ + rmse(orig_statvar,meas_var),\ + percent_bias(orig_statvar,meas_var),\ + orig_statvar.corr(meas_var)**2] + + elif freq == 'monthly': + sim_out = sim_out.groupby(sim_out.index.month).mean() + result_df.loc[sim_names[i]] = [\ + nash_sutcliffe(meas_mo, sim_out),\ + rmse(meas_mo, sim_out),\ + percent_bias(meas_mo, sim_out),\ + meas_mo.corr(sim_out)**2,\ + np.abs(percent_bias(meas_mo, sim_out)) ] + orig_results.loc['orig_params'] = [\ + nash_sutcliffe(orig_mo,meas_mo),\ + rmse(orig_mo,meas_mo),\ + percent_bias(orig_mo,meas_mo),\ + orig_mo.corr(meas_mo)**2] + + sorted_result = result_df.sort_values(by=['NSE','RMSE','ABS(PBIAS)',\ + 'COEF_DET'], ascending=[False,True,True,False]) + sorted_result.columns.name = '{} parameters'.format(self.stage) + sorted_result = sorted_result[['NSE','RMSE','PBIAS','COEF_DET']] + sorted_result = pd.concat([orig_results,sorted_result]) + + if latex: return sorted_result[:top_n].to_latex(escape=False) + else: return sorted_result[:top_n] + + def get_top_ranked_sims(self, sorted_df): + # use result table to make dic with best param and statvar paths + # index of table are simulation directory names + ret = { + 'dir_name' : [], + 'param_path' : [], + 'statvar_path' : [], + 'params_adjusted' : [] + } + + json_paths = self.metadata_json_paths[self.stage] + + for el in sorted_df.drop('orig_params').index: + ret['dir_name'].append(el) + ret['param_path'].append(OPJ(self.working_dir,el,'inputs',\ + 'parameters')) + ret['statvar_path'].append(OPJ(self.working_dir,el,'outputs',\ + 'statvar.dat')) + for f in json_paths: + with open(f) as fh: + json_data = json.load(fh) + if OPJ(self.working_dir, el) in json_data.get('sim_dirs'): + ret['params_adjusted'].append(\ + json_data.get('params_adjusted')) + + return ret + + def archive(self, remove_sims=True, remove_meta=False, metric_freq='daily'): + """ + Create archive directory to hold json files that contain + information of adjusted parameters, model output, and performance + metrics for each Optimizer simulation of the + OptimizationResult.stage in the OptimizationResult.working_dir. + + Keyword Arguments: + remove_sims (bool): If True recursively delete all folders + and files associated with original simulations of the + OptimizationResult.stage in the + OptimizationResult.working_dir, if False do not delete + simulations. + remove_meta (bool): Whether to delete original Optimizer + JSON metadata files, default is False. + metric_freq (Str): Frequency of output metric computation + for recording of model performance. Can be 'daily' + (default) or 'monthly'. Note, other results can be computed + later with archived results. + + Returns: + None + """ + # create output archive directory + archive_dir = OPJ(self.working_dir,"{}_archived".format(self.stage)) + if not os.path.isdir(archive_dir): + os.mkdir(archive_dir) + + # create table and use to make mapping dic + table = self.result_table(freq=metric_freq,\ + top_n=self.total_sims, latex=False) + map_dic = self.get_top_ranked_sims(table) + + metadata_json_paths = self.metadata_json_paths[self.stage] + # get measured optimization variable path + first_json = metadata_json_paths[0] + + with open(first_json) as json_file: + json_data = json.load(json_file) + measured_path = json_data.get('measured') + + # record info for each simulation and archive to JSONs + # pandas series and numpy arrays are converted to Python lists + # for JSON serialization + for i, sim in enumerate(map_dic.get('dir_name')): + json_path = OPJ(archive_dir, '{sim}.json'.format(sim=sim)) + try: + output_series = load_statvar(OPJ(self.working_dir, sim,\ + 'outputs', 'statvar.dat'))\ + [self.statvar_name] + except: # simulation directory was already removed + continue + + # look for resampling method info for the particular simulation + for f in metadata_json_paths: + with open(f) as tmp_file: + tmp = json.load(tmp_file) + if OPJ(self.working_dir, sim) in tmp.get('sim_dirs'): + resample = tmp.get('resample') + noise_factor = tmp.get('noise_factor') + mu_factor = tmp.get('mu_factor') + + json_data = { + 'param_names' : [], + 'param_values' : [], + 'original_param_path' : self.input_params, + 'measured_path' : measured_path, + 'output_name' : self.statvar_name, + 'output_date_index' : output_series.\ + index.astype(str).tolist(), + 'output_values' : output_series.values.tolist(), + 'metric_freq' : metric_freq, + 'resample' : resample, + 'stage' : self.stage, + 'mu_factor' : mu_factor, + 'noise_factor' : noise_factor, + 'NSE' : table.loc[sim, 'NSE'], + 'RMSE' : table.loc[sim, 'RMSE'], + 'PBIAS' : table.loc[sim, 'PBIAS'], + 'COEF_DET' : table.loc[sim, 'COEF_DET'] + } + + for param in map_dic.get('params_adjusted')[i]: + json_data['param_names'].append(param) + json_data['param_values'].append(Parameters(\ + map_dic.get('param_path')[i])[param]\ + .tolist()) + # save JSON file into archive directory + with open(json_path, 'w') as outf: + json.dump(json_data, outf, sort_keys = True, indent = 4,\ + ensure_ascii = False) + + # recursively delete all simulation directories after archiving + if remove_sims: + path = OPJ(self.working_dir, sim) + for dirpath, dirnames, filenames in os.walk(path,\ + topdown=False): + shutil.rmtree(dirpath, ignore_errors=True) + else: + continue + + # optional delete the original JSON metadata + if remove_meta: + for meta_file in self.metadata_json_paths[self.stage]: + try: + os.remove(meta_file) + except: + continue + + diff --git a/build/lib/prms_python/parameters.py b/build/lib/prms_python/parameters.py new file mode 100644 index 0000000..77a8ad6 --- /dev/null +++ b/build/lib/prms_python/parameters.py @@ -0,0 +1,575 @@ +# -*- coding: utf-8 -*- +''' +parameters.py -- holds ``Parameter`` class with multiple functionality for +the standard PRMS parameters input file. +''' + +import datetime, calendar +import io, os +import itertools +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +from matplotlib.backends.backend_pdf import PdfPages +from mpl_toolkits.axes_grid1 import make_axes_locatable +from collections import OrderedDict + +OPJ = os.path.join + +class Parameters(object): + ''' + Disk-based representation of a PRMS parameter file. + + For the sake of memory efficiency, we only load parameters from + ``base_file`` that get modified through item assignment or accessed directly. + Internally, a reference is kept to only previously accessed parameter data, + so when ``write`` is called most copying is from ``base_file`` directly. + When parameters are accessed or modified using the dictionary-like syntax, + a ``np.ndarray`` representation of the parameter is returned. As a result + ``numpy`` mathematical rules including efficient vectorization of math applied + to arrays can be applied to modify parameters directly. The ``Parameter`` + objects user methods allow for visualization of most PRMS parameters, function + based modification of parameters, and a write function that writes the data + back to PRMS text format. + + Arguments: + base_file (str): path to PRMS parameters file + + Attributes: + base_file (str): path to PRMS parameters file + base_file_reader (file): file handle of PRMS parameters file + dimensions (:obj:`collections.OrderedDict`): dictionary with + parameter dimensions as defined in parameters file loaded on + initialization + base_params (list of dicts): list of dictionaries of parameter + metadata loaded on initialization e.g. name, dimension(s), data + type, length of data array, and lines where data starts and ends + in file + param_arrays (dict): dictionary with parameteter names as keys and + ``numpy.array`` and ``numpy.ndarray`` representations of parameter + values as keys. Initially empty, uses getter and setter functions. + + Example: + >>> p = Parameters('path/to/a/parameter/file') + >>> p['jh_coef'] = p['jh_coef']*1.1 + >>> p.write('example_modified_params') + + will read parameter information from the params file to check that + *jh_coef* is present in the parameter file, read the lines corresponding + to *jh_coef* data and assign the new value as requested. Calling + the ``write`` method next will copy all parameters except *jh_coef* + to the new parameter file and append the newly modified *jh_coef* + to the end of the new file from the modified values stored in the + parameter instance ``p``. + ''' + + def __init__(self, base_file): + self.base_file = base_file + self.base_file_reader = open(base_file) + self.dimensions, self.base_params = self.__read_base(base_file) + self.param_arrays = dict() + + def write(self, out_name): + """ + Writes current state of ``Parameters`` to disk in PRMS text format + + To reduce memory usage the ``write`` method copies parameters + from the initial ``base_file`` parameter file for all parameters + that were never modified. + + Arguments: + out_name (str): path to write ``Parameters`` data to PRMS text + format. + + Returns: + None + """ + data_type_dic = {'1': 'int', + '2': 'float'} # retain PRMS data types + + with open(self.base_file, 'r') as base_file: + with open(out_name, 'w') as out_file: + # write metadata + out_file.write('File Auto-generated by PRMS-Python\n') + out_file.write(datetime.datetime.now().isoformat() + '\n') + + # # write dimensions + out_file.write('** Dimensions **\n') + + # write parameters; pre-sorted by data start line on read + name_is_next = False + params_start = False + write_params_lines = False + for l in base_file: + + if not params_start and l.strip() == '** Parameters **': + out_file.write('** Parameters **\n') + params_start = True + + elif l.strip() == '####': + name_is_next = True + + elif name_is_next: + name = l.strip().split()[0] + if name not in self.param_arrays: + out_file.write('####\n') + out_file.write(name + '\n') + name_is_next = False + write_params_lines = True + else: + write_params_lines = False + name_is_next = False + + elif write_params_lines: + out_file.write(l.strip() + '\n') + + # write all parameters that had been accessed and/or modified + for param, new_arr in self.param_arrays.items(): + + out_file.write('####\n') + + param_info = [el for el in self.base_params + if el['name'] == param].pop() + + out_file.write(str(param_info['name']) + '\n') + out_file.write(str(param_info['ndims']) + '\n') + for dimname in param_info['dimnames']: + out_file.write(dimname + '\n') + out_file.write(str(param_info['length']) + '\n') + out_file.write(str(param_info['vartype']) + '\n') + out_file.writelines([str(a) + '\n' + for a in new_arr.flatten().\ + astype(data_type_dic[param_info\ + ['vartype']])]) + + def plot(self, nrows, which='all', out_dir=None, xlabel=None,\ + ylabel=None, cbar_label=None, title=None, mpl_style=None): + """ + Versatile method that plots most parameters in a standard PRMS parameter + file assuming the PRMS model was built on a uniform spatial grid. + + Plots parameters as line plots for series or 2D spatial grid depending on + parameter dimension. The PRMS parameter file is assumed to hold parameters + for a model that was set up on a uniform rectangular grid with the spatial + index of HRUs starting in the upper left corner and moving left to + right across columns and down rows. Default function is to print four + files, each with plots of varying parameter dimensions as explained + under Kwargs ``which`` and more detailed explanation in the example + `Jupyter notebook `_. + + Arguments: + nrows (int): The number of rows in the PRMS model grid for plotting + spatial parameters. Will only work correctly for rectangular gridded models + with HRU indices starting in the upper left cell moving left to right + across columns and down across rows. + + Keyword Arguments: + which (str): name of PRMS parameter to plot or 'all'. If 'all' then + the function will print 3 multipage pdfs, one for nhru + dimensional parameters, one for nhru by monthly parameters, one + for other parameters of length > 1, and one html file containing + single valued parameters. + out_dir (str): path to an output dir, default current directory + xlabel (str): x label for plot(s) + ylabel (str): y label for plot(s) + cbar_label (str): label for colorbar on spatial plot(s) + title (str): plot title + mpl_style (str, list): name or list of names of matplotlib style sheets to + use for plot(s). + + Returns: + None + + Examples: + If the plot method is called with the keyword argument ``which`` set + to a parameter that has length one, i.e. single valued it will simply + print out the value e.g.: + + >>> p = Parameters('path/to/parameters') + >>> p.plot(nrows=10, which='radj_sppt') + radj_sppt is single valued with value: 0.4924942352224324 + + The default action is particularly useful which makes four multi-page + pdfs of most PRMS parameters where each file contains parameters + of different dimensions e.g.: + + >>> p.plot(nrows=10, which='all', mpl_style='ggplot') + + will produce the following four files named by parameters of certain + dimensions: + + >>> import os + >>> os.listdir(os.getcwd()) # list files in current directory + nhru_param_maps.pdf + nhru_by_nmonths_param_maps.pdf + non_spatial_param_plots.pdf + single_valued_params.html + + """ + params = self + + if not isinstance(params, Parameters): + raise TypeError('params must be instance of Parameters, not '\ + + str(type(params))) + if not out_dir: + out_dir = os.getcwd() + + if not os.path.isdir(out_dir): + os.mkdir(out_dir) + + nhru = params.dimensions['nhru'] + ncols = nhru // nrows + + if not mpl_style: + mpl_style = 'classic' + plt.style.use(mpl_style) + + # make pdfs and html of all parameters seperated in 4 files based on dimension + if which == 'all': + ## spatial parameters with dimension of length nhru + p_names = [param['name'] for param in params.base_params if\ + param['length'] == nhru and len(param['dimnames'])==1] + with PdfPages(OPJ(out_dir,'nhru_param_maps.pdf')) as pdf: + for p in p_names: + try: + plt.figure() + ax = plt.gca() + im = ax.imshow(params['{}'.format(p)].reshape(nrows,ncols), origin='upper') + # origin upper- assumes indices of parameters starts in upper left + divider = make_axes_locatable(ax) + cax = divider.append_axes("right", size="5%", pad=0.05) + plt.colorbar(im, cax=cax) + ax.set_title('{}'.format(p)) + ax.tick_params(left='off', bottom='off', labelleft='off',labelbottom='off') + pdf.savefig() + plt.close() + except: + print('{param} parameter failed to plot'.format(param=p)) + + ## monthly spatial parameters (on plot per month) + p_names = [param['name'] for param in params.base_params if\ + param['dimnames'][0] == 'nhru' and len(param['dimnames'])==2 ] + with PdfPages(OPJ(out_dir,'nhru_by_nmonths_param_maps.pdf')) as pdf: + for p in p_names: + try: + for i in range(12): #month + plt.figure() + ax = plt.gca() + im = ax.imshow(params['{}'.format(p)][i].reshape(nrows, ncols), origin='upper') + divider = make_axes_locatable(ax) + cax = divider.append_axes("right", size="5%", pad=0.05) + plt.colorbar(im, cax=cax) + ax.set_title('{} {}'.format(p, calendar.month_name[i+1])) + ax.tick_params(left='off', bottom='off', labelleft='off', labelbottom='off') + pdf.savefig() + plt.close() + except: + print('{param} for {month} failed to plot'.\ + format(param=p, month=calendar.month_name[i+1])) + + ## non spatial parameters with dimension length > 1 to be plotted as time series + p_names = [param['name'] for param in params.base_params if\ + ( 1 < param['length'] <= 366 )\ + and param['dimnames'][0] != 'nhru' ] + with PdfPages(OPJ(out_dir,'non_spatial_param_plots.pdf')) as pdf: + for p in p_names: + try: + param_dict = [param for param in params.base_params if param['name'] == p][0] + plt.plot(np.arange(1, param_dict['length']+1, 1), params[p]) + plt.xlabel(param_dict['dimnames'][0]) + plt.ylabel(p) + plt.xlim(0.5, param_dict['length']+0.5) + pdf.savefig() + plt.close() + except: + print('{param} parameter failed to plot'.format(param=p)) + + ## html table of parameters with dimension length = 1 + p_names = [param['name'] for param in params.base_params if param['length'] == 1] + df = pd.DataFrame() + df.index.name = 'parameter' + for p in p_names: + df.set_value(p, 'value', params[p]) + df.to_html(OPJ(out_dir,'single_valued_params.html')) + ################################################################ + # plot single parameter, in case of nhru by monthly param, + # save multi-page pdf + else: + param_name = which + try: + params[which] + except: + print('{param} is not a valid PRMS parameter'.format(param=param_name)) + return + + param_dict = [param for param in params.base_params if param['name'] == param_name][0] + + # labels for single plots + if not cbar_label: + cbar_label = param_name + if not title: + title = '' + + # if parameter is not spatial, one dimensional, with length greater than one, plot as line + if param_dict['ndims'] == 1 and ( 1 < param_dict['length'] <= 366 )\ + and param_dict['dimnames'][0] != 'nhru': + if not xlabel: + xlabel = param_dict['dimnames'][0] + if not ylabel: + ylabel = param_name + plt.plot(np.arange(1, param_dict['length']+1,1), params[param_name]) + plt.xlim(0.5, param_dict['length']+0.5) + plt.xlabel(xlabel) + plt.ylabel(ylabel) + plt.title(title) + # if spatial and one dimensional, plot + elif param_dict['ndims'] == 1 and param_dict['length'] == params.dimensions['nhru']: + if not xlabel: + xlabel = '' + if not ylabel: + ylabel = '' + plt.figure() + ax = plt.gca() + im = ax.imshow(params[param_name].reshape(nrows,ncols), origin='upper') + divider = make_axes_locatable(ax) + cax = divider.append_axes("right", size="5%", pad=0.05) + plt.colorbar(im, cax=cax,label=cbar_label) + ax.tick_params(left='off', bottom='off', labelleft='off', labelbottom='off') + ax.set_title(title) + ax.set_ylabel(ylabel) + ax.set_xlabel(xlabel) + # spatial monthly parameter + elif param_dict['dimnames'][0] == 'nhru' and param_dict['dimnames'][1] == 'nmonths'\ + and param_dict['ndims'] == 2: + if not xlabel: + xlabel = '' + if not ylabel: + ylabel = '' + file_name = '{}.pdf'.format(param_name) + with PdfPages(OPJ(out_dir, file_name)) as pdf: + for i in range(12): #month + plt.figure() + ax = plt.gca() + im = ax.imshow(params['{}'.format(param_name)][i].reshape(nrows,ncols), origin='upper') + divider = make_axes_locatable(ax) + cax = divider.append_axes("right", size="5%", pad=0.05) + plt.colorbar(im, cax=cax) + ax.set_title('{} {}'.format(param_name, calendar.month_name[i+1])) + ax.set_ylabel(ylabel) + ax.set_xlabel(xlabel) + ax.tick_params(left='off', bottom='off', labelleft='off', labelbottom='off') + pdf.savefig() + plt.close() + else: + val = params[param_name] + print('{param} is single valued with value: {v}'.format(param=param_name, v=val)) + + def __read_base(self, base_file): + "Read base file returning 2-tuple of dimension and params dict" + + params_startline, dimensions = self.__make_dimensions_dict(base_file) + base_params = self.__make_parameter_dict(base_file, params_startline) + + return (dimensions, base_params) + + def __make_dimensions_dict(self, base_file): + """ + Extract dimensions and each dimension length. Runs before + __make_parameter_dict. + """ + ret = OrderedDict() + + dim_name = '' + dim_len = 0 + # finished = False + found_dim_start = False + # while not finished: + for idx, l in enumerate(self.base_file_reader): + + if l.strip() == '** Dimensions **': # start of dimensions + found_dim_start = True + + elif '#' in l: # comments + pass + + elif l.strip() == '** Parameters **': # start of parameters + dimlines = idx + # finished = True + break + + elif found_dim_start: + + if dim_name == '': + dim_name = l.strip() + else: + dim_len = int(l) + ret.update({dim_name: dim_len}) + dim_name = '' + + return (dimlines, ret) + + def __make_parameter_dict(self, base_file, params_startline=0): + ret = [] + + name = '' + ndims = 0 + dimnames = [] + length = 0 + vartype = '' + + dimnames_read = 0 + data_startline = 0 + + for idx, l in enumerate(self.base_file_reader): + + if '#' in l: + # we have a comment; the next lines will be new + # parameter metadata. No data for the first time through, so + # we don't want to append an metadata blob with empty values + if name: + ret.append( + dict( + name=name, + ndims=ndims, + dimnames=dimnames, + length=length, + vartype=vartype, + data_startline=data_startline + ) + ) + + name = '' + ndims = 0 + dimnames = [] + length = 0 + vartype = '' + dimnames_read = 0 + + elif not name: + name = l.strip().split()[0] # in case old format with integer after name + + elif not ndims: + ndims = int(l.strip()) + + elif not (dimnames_read == ndims): + dimnames.append(l.strip()) + dimnames_read += 1 + + elif not length: + length = int(l.strip()) + + elif not vartype: + vartype = l.strip() + # advance one from current position and account for starting + # to count from zero + data_startline = params_startline + idx + 2 + + # need to append one more time since iteration will have stopped after + # last line + ret.append( + dict( + name=name, + ndims=ndims, + dimnames=dimnames, + length=length, + vartype=vartype, + data_startline=data_startline + ) + ) + + return ret + + def __getitem__(self, key): + """ + Look up a parameter by its name. + + Raises: + KeyError if parameter name is not valid + """ + def load_parameter_array(param_metadata): + + startline = param_metadata['data_startline'] + endline = startline + param_metadata['length'] + 1 + + param_slice = itertools.islice( + io.open(self.base_file, 'rb'), startline, endline + ) + + arr = np.genfromtxt(param_slice) + + if param_metadata['ndims'] > 1: + dimsizes = [ + self.dimensions[d] for d in param_metadata['dimnames'] + ] + dimsizes.reverse() + arr = arr.reshape(dimsizes) + + return arr + + if key in self.param_arrays: + return self.param_arrays[key] + + else: + try: + param_metadata = [ + el for el in self.base_params if el['name'] == key + ].pop() + + except IndexError: + raise KeyError(key) + + arr = load_parameter_array(param_metadata) + + # cache the value for future access (but maybe shouldn't?) + self.param_arrays.update({key: arr}) + + return arr + + def __setitem__(self, key, value): + + if key in self.param_arrays: + cur_arr = self.param_arrays[key] + if not value.shape == cur_arr.shape: + raise ValueError('New array does not match existing') + + self.param_arrays[key] = value + +def modify_params(params_in, params_out, param_mods=None): + ''' + Given a parameter file in and a dictionary of param_mods, write modified + parameters to params_out. + + + Arguments: + params_in (str): location on disk of the base parameter file + params_out (str): location on disk where the modified parameters will + be written + + Keyword Arguments: + param_mods (dict): param name-keyed, param modification function-valued + + Returns: + None + + Example: + Below we modify the monthly *jh_coef* parameter by increasing it 10% + for every month, + + >>> params_in = 'models/lbcd/parameters' + >>> params_out = 'scenarios/jh_coef_1.1/params' + >>> scale_10pct = lambda x: x * 1.1 + >>> modify_params(params_in, params_out, {'jh_coef': scale_10pct}) + + So param_mods is a dictionary of with keys being parameter names and + values a function that operates on a single value. Currently we only + accept functions that operate without reference to any other + parameters. The function will be applied to every cell, month, or + cascade routing rule for which the parameter is defined. + ''' + p_in = Parameters(params_in) + + for k in param_mods: + p_in[k] = param_mods[k](p_in[k]) + + p_in.write(params_out) diff --git a/build/lib/prms_python/scenario.py b/build/lib/prms_python/scenario.py new file mode 100644 index 0000000..7dea799 --- /dev/null +++ b/build/lib/prms_python/scenario.py @@ -0,0 +1,601 @@ +# -*- coding: utf-8 -*- +''' +scenario.py -- holds ``Scenario`` and ``ScenarioSeries`` classes for PRMS +managing parameter-based model scenarios that may be used for hypotheses +testing. +''' + +import inspect +import json +import multiprocessing as mp +import os +import shutil +import uuid + +from datetime import datetime +from .parameters import modify_params, Parameters +from .data import Data +from .util import load_statvar +from .simulation import Simulation + + +class ScenarioSeries(object): + """ + Create and manage a series of model runs where parameters are modified. + + First initialize the series with an optional title and description. + Then to build the series the user provides a list of dictionaries with + parameter-function key-value pairs, and optionally a title and + description for each dictionary defining the individual scenario. + + The ScenarioSeries' ``build`` method creates a file structure under + the series directory (``scenarios_dir``) where each subdirectory is + named with a :mod:`uuid` which can be later matched to its title using + the metadata in ``scenario_dir/series_metadata.json`` (see :mod:`json`). + In the future we may add a way for the user to access the results of + the scenario simulations directly through the ``ScenarioSeries`` + instance, but for now the results are written to disk. Therefore each + scenario's title metadata can be used to refer to which parmaters were + modified and how for post-processing and analysis. One could also use + the description metadata for this purpose. + + Arguments: + base_dir (str): path to base inputs; 'control', 'parameters', + and 'data' must be present there + scenarios_dir (str): directory where scenario data will be written + to; will be overwritten or created if it does not exist + + Keyword Arguments: + title (str, optional): title of the ScenarioSeries instance + description (str, optional): description of the ScenarioSeries + instance + + Attributes: + metadata (dict): dictionary with title, description, and UUID map + dictionary for individiual ``Scenario`` output directories, + the UUID dictionary (``uuid_title_map``)is left empty until + calling :meth:`ScenarioSeries.build`. + scenarios (list): empty list that will be filled with ``Scenario``s + after defining them by calling :meth:`ScenarioSeries.build`. + + Example: + There are three steps to both ``Scenario`` and ScenarioSeries, + first we initialize the object + + >>> series = ScenarioSeries( + base_dir = 'dir_with_input_files', + scenarios_dir = 'dir_to_run_series', + title = 'title_for_group_of_scenarios', + description = 'description_for_scenarios' + ) + + The next step is to "build" the ``ScenarioSeries`` by calling the + :meth:`ScenarioSeries.build` method which defines which parameters + to modify, how to modify them, and then performs the modification + which readies the series to be "run" (the last step). See the + :meth:`ScenarioSeries.build` method for the next step example. + + Also see :ref:`scenario_and_scenarioseries_tutorial` for full example + """ + + def __init__(self, base_dir, scenarios_dir, title=None, description=None): + """ + """ + self.base_dir = base_dir + self.scenarios_dir = scenarios_dir + if os.path.exists(scenarios_dir): + shutil.rmtree(scenarios_dir) + + os.mkdir(scenarios_dir) + + shutil.copytree(base_dir, os.path.join(scenarios_dir, 'base_inputs')) + + self.metadata = dict(title=title, + description=description, + uuid_title_map={}) + self.scenarios = [] + + self.outputs = None + + @classmethod + def from_parameters_iter(cls, base_directory, parameters_iter, + title=None, description=None): + ''' + Alternative way to initialize and build a ``ScenarioSeries`` in one + step. + + Create and build a ``ScenarioSeries`` by including the param-keyed- + function-valued dictionary (``parameters_iter``) that is otherwise + passed in :meth:`ScenarioSeries.build`. + + Arguments: + base_directory (str): directory that contains model input files + parameters_iter (list of dicts): list of dictionaries for each + ``Scenario`` as described in :class:`Scenario` and + :meth:`ScenarioSeries.build`. + title (str): title for group of scenarios + description (str): description for group of scenarios + + Returns: + None + ''' + series = cls(base_directory, base_directory, + title=title, description=description) + + for parameters in parameters_iter: + + title = parameters['title'] if 'title' in parameters else None + + uu = str(uuid.uuid4()) + + series.metadata['uuid_title_map'].update({uu: title}) + + scenario_dir = os.path.join(series.scenarios_dir, uu) + + scenario = Scenario(series.base_dir, scenario_dir, title=title) + + scenario.build() + + series.scenarios.append(scenario) + + with open( + os.path.join(series.scenarios_dir, 'series_metadata.json'), 'w' + ) as f: + f.write(json.dumps(series.metadata, indent=2)) + + def __len__(self): + return len(self.scenarios) + + def build(self, scenarios_list): + """ + Build the scenarios from a list of scenario definitions in dicitonary + form. + + Each element of ``scenarios_list`` can have any number of parameters + as keys with a function for each value. The other two acceptable keys + are ``title`` and ``description`` which will be passed on to each + individual Scenario's metadata in ``series_metadata.json`` for future + lookups. The ``build`` method also creates a file structure that uses + UUID values as individiual ``Scenario`` subdirectories as shown below. + + Arguments: + scenarios_list (list): list of dictionaries with key-value + pairs being parameter-function definition pairs or + title-title string or description-description string. + Returns: + None + + Examples: + Following the initialization of a ``ScenarioSeries`` instance as + shown the example docstring there, we "build" the series by + defining a list of param named-keyed function-valued + dictionaries. This example uses arbitrary functions on two + PRMS parameters *snowinfil_max* and *snow_adj*, + + >>> def _function1(x): #Note, function must start with underscore + return x * 0.5 + >>> def _function2(x): + return x + 5 + >>> dic1 = {'snowinfil_max': _function1, 'title': 'scenario1'} + >>> dic2 = {'snowinfil_max': _function2, + 'snow_adj': function1, + 'title': 'scenario2', + 'description': 'we adjusted two snow parameters' + } + >>> example_scenario_list = [dic1, dic2] + >>> # now we can build the series + >>> series.build(example_scenario_list) + + In this example that follows from :class:`ScenarioSeries` example the + file structure that is created by the ``build`` method is as follows:: + + dir_to_run_series + ├── 670d6352-2852-400a-997e-7b12ba34f0b0 + │   ├── control + │   ├── data + │   └── parameters + ├── base_inputs + │   ├── control + │   ├── data + │   └── parameters + ├── ee9526a9-8fe6-4e88-b357-7dfd7111208a + │   ├── control + │   ├── data + │   └── parameters + └── series_metadata.json + + As shown the build method has copied the original inputs from the + ``base_dir`` given on initialization of ``ScenarioSeries`` to a new + subdirectory of the ``scenarios_dir``, it also applied the + modifications to the parameters for both scenarios above and move the + input files to their respective directories. At this stage the + ``metadata`` will not have updated the UUID map dictionary to each + scenarios subdirectory because they have not yet been run. See the + :meth:`ScenarioSeries.run` method for further explanation including + the final file structure and metadata file contents. + """ + + title = None + description = None + for s in scenarios_list: + + if 'title' in s: + title = s['title'] + del s['title'] + + if 'description' in s: + description = s['description'] + del s['description'] + + uu = str(uuid.uuid4()) + + self.metadata['uuid_title_map'].update({uu: title}) + + scenario_path = os.path.join(self.scenarios_dir, uu) + + # create Scenario + scenario = Scenario( + self.base_dir, scenario_path, title=title, + description=description + ) + + # s now only contains parameter keys and function references vals + scenario.build(s) + + self.scenarios.append(scenario) + + with open( + os.path.join(self.scenarios_dir, 'series_metadata.json'), 'w' + ) as f: + + f.write(json.dumps(self.metadata, indent=2)) + + def run(self, prms_exec='prms', nproc=None): + """ + Run a "built" ``ScenarioSeries`` and make final updates + to file structure and metadata. + + Keyword Arguments: + prms_exec (str): name of PRMS executable on $PATH or path to + executable. Default = 'prms' + nproc (int or None): number of processceors available to + parallelize PRMS simulations, if None (default) then use + half of what the :mod:`multiprocessing` detects on the + machine. + + Returns: + None + + Examples: + This example starts where the example ends in + :meth:`ScenarioSeries.build`, calling ``run`` will run the + models for all scenarios and then update the file structure + as well as create individual ``Scenario`` metadata files as such:: + + dir_to_run_series + ├── 5498c21d-d064-45f4-9912-044734fd230e + │   ├── inputs + │   │   ├── control + │   │   ├── data + │   │   └── parameters + │   ├── metadata.json + │   └── outputs + │   ├── prms_ic.out + │   ├── prms.out + │   └── statvar.dat + ├── 9d28ec5a-b570-4abb-8000-8dac113cbed3 + │   ├── inputs + │   │   ├── control + │   │   ├── data + │   │   └── parameters + │   ├── metadata.json + │   └── outputs + │   ├── prms_ic.out + │   ├── prms.out + │   └── statvar.dat + ├── base_inputs + │   ├── control + │   ├── data + │   └── parameters + └── series_metadata.json + + As we can see the file structure follows the combined structures + as defined by :class:`Simulation` and :class:`Scenario`. The content + of the top-level metadata file ``series_metadata.json`` is as such:: + + { + "title": "title_for_group_of_scenarios", + "description": "description_for_scenarios", + "uuid_title_map": { + "5498c21d-d064-45f4-9912-044734fd230e": "scenario1", + "9d28ec5a-b570-4abb-8000-8dac113cbed3": "scenario2" + } + } + + Therefore one can use the :mod:`json` file to track between UUID's and + individual scenario titles. The json files are read as a Python + dictionary which makes them particularly convenient. The contents of + an individual scenarios ``metadata.json`` file included a string + representation of the function(s) that were applied to the + paramter(s):: + + { + "description": null, + "end_datetime": "2018-09-03T00:00:40.793817", + "mod_funs_dict": { + "snowinfil_max": "def _function1(x): + return x * 0.5" + }, + "start_datetime": "2018-09-03T00:00:30.421353", + "title": "scenario1" + } + + Note: + As shown, it is important to give appropriate scenario titles when + building a ``ScenarioSeries`` dictionary in order to later + understand how parameters were modified in each scenario. If not + one would have to rely on the individual ``metadata.json`` files + in each scenario directory which may be more cumbersome. + """ + if not nproc: + nproc = mp.cpu_count()//2 + + pool = mp.Pool(processes=nproc) + pool.map(_scenario_runner, self.scenarios) + + +# multiprocessing req the function be def'd at root scope so it's picklable +def _scenario_runner(scenario, prms_exec='prms'): + scenario.run(prms_exec=prms_exec) + + +class Scenario: + """ + Container for the process in which one modifies input parameters then + runs a simulation while tracking metadata. + + Metadata includes a title and description, if provided, plus start/end + datetime, and parameter names of parameters that were modified including + string representations of the Python modification functions that were + applied to each parameter. The metadata file is in :mod:`json` format + making it conveniently read as a Python dictionary. + + Arguments: + base_dir (str): path to directory that contains initial *control*, + *parameter*, and *data* files to be used for ``Scenario``. + The *parameters* file in ``base_dir`` will not be modifed + instead will be copied to ``scenario_dir`` and them modified. + scenario_dir (str): directory path to bundle inputs and outputs + title (str, optional): title of ``Scenario``, if given will be + added to ``Scenario.metadata`` attribute as well as the + ``metadata.json`` file in ``scenario_dir`` written after + calling the :func:`Scenario.build()` and :func:`Scenario.run()` + methods. + description (str, optional): description of ``Scenario``, also + is added to ``Scenario.metadata`` as ``title``. + + Attributes: + metadata (:class:`scenario.ScenarioMetadata`): a dictionary-like + class in :mod:`prms_python.scenario` that tracks ``Scenario`` and + ``ScenarioSeries`` imformation including user-defined parameter + modifications and descriptions, and file structure. + + Examples: + This example is kept simple for clarity, here we adjust a + single PRMS parameter *tmin_lapse* by using a single arbitrary + mathematical function. We use the example PRMS model included + with PRMS-Python for this example, + + >>> input_dir = 'PRMS-Python/test/data/models/lbcd' + >>> scenario_directory = 'scenario_testing' + >>> title = 'Scenario example' + >>> desc = 'adjust tmin_lapse using sine wave function' + >>> # create Scenario instance + >>> scenario_obj = Scenario + ( + base_dir=input_dir, + scenario_dir=scenario_directory, + title=title, + description=desc + ) + + Next we need to build a dictionary to modify, in this case + *tmin_lapse*, here we use a vectorized sine function + + >>> # build the modification function and dictionary + >>> def a_func(arr): + return 4 + np.sin(np.linspace(0,2*np.pi,num=len(arr))) + >>> # make dictionary with parameter names as keys and modification + >>> # function as values + >>> param_mod_dic = dict(tmin_lapse=a_func) + >>> scenario_obj.build(param_mod_funs=param_mod_dic) + + After building a ``Scenario`` instance the input files are + copied to ``scenario_dir`` which was assigned 'scenario_testing':: + + scenario_testing + ├── control + ├── data + └── parameters + + + After calling ``build`` the input files from ``input_dir`` were + first copied to ``scenario_dir`` and then the functions in + ``param_mod_dic`` are applied the the parameters names (key) + in ``param_mod_dic``. To run the ``Scenario`` use the the ``run`` + method + + >>> scenario_obj.run() + + Now the simulation is run and the ``metadata.json`` file is created, + the final file structure will be similar to this:: + + scenario_testing + ├── inputs + │   ├── control + │   ├── data + │   └── parameters + ├── metadata.json + └── outputs + ├── prms_ic.out + ├── prms.out + └── statvar.dat + + Finally, here is what is contained in ``metadata.json`` for this example + which is also updates in the :attr:`Scenario.metadata` + + >>> scenario_obj.metadata + { + 'title': 'Scenario example', + 'description': 'adjust tmin_lapse using sine wave function', + 'start_datetime': '2018-09-01T19:20:21.723003', + 'end_datetime': '2018-09-01T19:20:31.117004', + 'mod_funs_dict': { + 'tmin_lapse': 'def parab(arr): + return 4 + np.sin(np.linspace(0,2*np.pi,num=len(arr)))' + } + } + + As shown the metadata retirieved the parameter modification function + as a string representation of the exact Python function(s) used for + modifying the user-defined parameter(s). + + Note: + The main differentiator between :class:`Scenario` and + :class:`ScenarioSeries` is that ``Scenario`` is designed for modifying + one or more parameters of a **single** *parameters* file whereas + ``ScenarioSeries`` is designed for modifying and tracking the + modification of one or more parameters in **multiple** PRMS + *parameters* files, therefore resulting in multiple PRMS simulations. + + """ + + def __init__(self, base_dir, scenario_dir, + title=None, description=None): + + self.title = title + self.description = description + + self.base_dir = base_dir + self.scenario_dir = scenario_dir + + self.metadata = ScenarioMetadata(title=title, description=description) + + self.__simulation_ready = False + + def build(self, param_mod_funs=None): + """ + Take a user-defined dictionary with param names as keys and Python + functions as values, copy the original input files as given when + initializing a :class:`Scenario` instance to the ``simulation_dir`` + then apply the functions in the user-defined dictionary to the + parameters there. The ``build`` method must be called before running + the ``Scenario`` (calling :func:`Scenario.run()` ). + + Keyword Arguments: + param_mod_funs (dict): dictionary with parameter names as keys + and Python functions as values to apply to the names (key) + + Returns: + None + + Example: + see :class:`Scenario` for a full example. + + Note: + If the ``scenario_dir`` that was assigned for the current + instance already exists, it will be overwritten when ``build`` + is invoked. + """ + + if isinstance(param_mod_funs, dict): + + # create scenario_dir that will be used as Simulation input dir + if os.path.isdir(self.scenario_dir): + shutil.rmtree(self.scenario_dir) + + os.makedirs(self.scenario_dir) + shutil.copy( + os.path.join(self.base_dir, 'control'), self.scenario_dir + ) + shutil.copy( + os.path.join(self.base_dir, 'data'), self.scenario_dir + ) + + old_params_path = os.path.join(self.base_dir, 'parameters') + new_params_path = os.path.join(self.scenario_dir, 'parameters') + if not param_mod_funs: + shutil.copy(old_params_path, self.scenario_dir) + else: + modify_params(old_params_path, new_params_path, param_mod_funs) + + param_mod_funs_metadata = { + param_name: inspect.getsource(param_mod_fun) + for param_name, param_mod_fun in param_mod_funs.items() + } + + self.metadata['mod_funs_dict'] = param_mod_funs_metadata + + self.simulation = Simulation(self.scenario_dir, self.scenario_dir) + + else: + self.simulation = Simulation(self.scenario_dir) + + self.__simulation_ready = True + + def run(self, prms_exec='prms'): + """ + Run the PRMS simulation for a *built* ``Scenario`` instance. + + Keyword Arguments: + prms_exec (str): name of PRMS executable on $PATH or path to + executable + + Returns: + None + + Examples: + see :class:`Scenario` for full example + + Raises: + RuntimeError: if the :func:`Scenario.build` method has not yet + been called. + """ + if not self.__simulation_ready: + raise RuntimeError( + 'Scenario has not yet been prepared: run build_scenario first' + ) + + self.metadata['start_datetime'] = datetime.now().isoformat() + self.simulation.run(prms_exec=prms_exec) + self.metadata['end_datetime'] = datetime.now().isoformat() + + self.metadata.write(os.path.join(self.scenario_dir, 'metadata.json')) + + +class ScenarioMetadata: + + def __init__(self, title=None, description=None, start_datetime=None, + end_datetime=None, mod_funs_dict=None): + + self.metadata_dict = dict(title=title, + description=description, + start_datetime=start_datetime, + end_datetime=end_datetime, + mod_funs_dict=mod_funs_dict) + + def __getitem__(self, key): + return self.metadata_dict[key] + + def __setitem__(self, key, value): + self.metadata_dict[key] = value + + def __repr__(self): + return self.metadata_dict.__repr__() + + def write(self, output_path): + with open(output_path, 'w') as f: + f.write(json.dumps(self.metadata_dict, ensure_ascii=False,\ + indent=4, sort_keys=True)) + + diff --git a/build/lib/prms_python/scripts/__init__.py b/build/lib/prms_python/scripts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build/lib/prms_python/scripts/prmspy.py b/build/lib/prms_python/scripts/prmspy.py new file mode 100644 index 0000000..7749473 --- /dev/null +++ b/build/lib/prms_python/scripts/prmspy.py @@ -0,0 +1,225 @@ +import click +import io +import itertools +import json +import matplotlib.pyplot as plt +import numpy as np +import os +import re + +from datetime import datetime +from matplotlib.backends.backend_pdf import PdfPages + +from ..scenario import ScenarioSeries +from ..util import load_data, load_statvar, nash_sutcliffe + + +@click.group() +def prmspy(): + "access PRMS-Python functionality from the command line" + click.echo('\n*** Welcome to PRMS-Python! ***\n') + + +@prmspy.command() +@click.argument('base_data_dir', nargs=1) +@click.option('--params', '-p', nargs=1, type=str, multiple=True, + help='list of parameters to edit') +@click.option('--scale_vals', '-s', nargs=1, type=str, multiple=True, + help='list of scaling values; must be divisible by len(params), ' + 'e.g. "[0.8, 0.9, 1.0, 1.1, 1.2]"' + ) +@click.option('--output-dir', '-o', nargs=1, type=str, + help='directory where scenario data should be written') +@click.option('--title', '-t', nargs=1, type=str, + help='title for this parameter scaling experiment') +@click.option('--description', '-d', nargs=1, type=str, + help='description for this parameter scaling experiment') +@click.option('--run-prms', is_flag=True, + help='run PRMS after creating scenario input data') +@click.option('--prms-exec', '-e', default='prms', + help='PRMS executable to be used, eg prmsV4') +@click.option('--nproc', '-n', default=None, type=int, + help='number of processors to use') +@click.option('--analyze-output', is_flag=True, + help='create analysis of model output in the form of a ' + '"Nash-Sutcliffe Matrix"') +def param_scale_sim(base_data_dir, + params, + scale_vals, + output_dir, + title, + description, + run_prms, + prms_exec, + nproc, + analyze_output): + 'Provide params and scaling values; run PRMS scenarios' + + # scale_vals must be divisible by the number of params + # valid_input = len(scale_vals) % len(params) == 0 + print(scale_vals) + scale_vals = [eval(el) for el in scale_vals] + + # if not valid_input: + # raise IOError('The length of scale_vals list is not divisible ' + # 'by the length of the parameter list') + + if not output_dir: + output_dir =\ + re.sub(r'\/|\\\\', '-', base_data_dir) + \ + '-modified-params-' + datetime.now().isoformat() + + os.mkdir(output_dir) + + s = ScenarioSeries( + base_data_dir, output_dir, title=title, description=description + ) + + def _scale_fun(val): + def scale(x): + return x * val + + return scale + + # assign scaling values to parameter names + # dimensionality correctness guaranteed from valid_input check + pval_len = len(scale_vals)/len(params) + + def _slice(idx): + return slice(pval_len*idx, pval_len*(idx+1)) + + param_val_dict = { + param: zip(itertools.repeat(param), scale_vals[idx]) + for idx, param in enumerate(params) + } + # param_val_dict = { + # param: zip(itertools.repeat(param, pval_len), scale_vals[_slice(idx)]) + # for idx, param in enumerate(params) + # } + # for every combination we need to iterate over every value of + # each param we have + combinations = itertools.product(*param_val_dict.values()) + + # now build the scenario_list with re-callable titles + scenario_list = [] + for combo in combinations: + + scenario_def = { + 'title': _build_title(combo) + } + scenario_def.update( + { + param: _scale_fun(val) + for param, val in combo + } + ) + + scenario_list.append(scenario_def) + + s.build(scenario_list) + + if run_prms: + s.run(prms_exec=prms_exec, nproc=nproc) + + if analyze_output: + print('\n** Sorry, analyze_output has not yet been implemented! **\n') + + +@prmspy.command() +@click.argument('data_dir', nargs=1) +@click.argument('output_pdf_path', nargs=1) +def nash_sutcliffe_matrix(data_dir, output_pdf_path): + 'Save a PDF of the Nash-Sutcliffe created from to ' + + observed = load_data( + os.path.join(data_dir, 'base_inputs', 'data') + ).runoff_1 + + series_metadata = json.loads( + io.open(os.path.join(data_dir, 'series_metadata.json')).read() + ) + modeled_flows = { + + title: load_statvar( + os.path.join(data_dir, uu, 'outputs', 'statvar.dat') + ).basin_cfs_1 + + for uu, title in series_metadata['uuid_title_map'].items() + } + + titles = list(modeled_flows.keys()) + + params = [pair.split(':')[0] for pair in titles[0].split('|')] + + if len(params) > 2: + print('This command is only supported for two covarying parameters!') + exit(1) + + p1_vals = sorted({ + float(pair[0].split(':')[1]) + for pair in map(lambda t: t.split('|'), titles) + }) + + p2_vals = sorted({ + float(pair[1].split(':')[1]) + for pair in map(lambda t: t.split('|'), titles) + }) + + print(p1_vals) + + nash_sutcliffe_mat = np.zeros((len(p1_vals), len(p2_vals))) + + for p1_idx, p1_val in enumerate(p1_vals): + for p2_idx, p2_val in enumerate(p2_vals): + + coord = (p1_idx, p2_idx) + title = '{p1}:{p1val:.3f}|{p2}:{p2val:.3f}'.format( + p1=params[0], p2=params[1], p1val=p1_val, p2val=p2_val + ) + modeled = modeled_flows[title] + + nash_sutcliffe_mat[coord] = nash_sutcliffe(observed, modeled) + + with PdfPages(output_pdf_path) as pdf: + + fig, ax = plt.subplots() + + cax = ax.matshow(nash_sutcliffe_mat, cmap='viridis') + + ytix = p1_vals + xtix = p2_vals + plt.yticks(range(len(ytix)), ytix) + plt.xticks(range(len(xtix)), xtix) + + ax.xaxis.set_ticks_position('bottom') + + plt.ylabel(params[0].replace('"', '')) + plt.xlabel(params[1].replace('"', '')) + + for i, j in itertools.product(range(len(ytix)), range(len(xtix))): + plt.text(j, i, '%.2f' % nash_sutcliffe_mat[i, j], + horizontalalignment='center', + color='w' + if nash_sutcliffe_mat[i, j] < + np.mean(nash_sutcliffe_mat.flatten()) + else 'k') + + plt.title('Nash-Sutcliffe Matrix') + + plt.grid(b=False) + fig.colorbar(cax) + + pdf.savefig() + plt.close() + + +def _build_title(combo): + ''' + Given a list of tuples with parameter/scale_val pairs, build the title + for the parameterization. For example, + >>> combo = [('snow_adj', 0.8), ('rad_trncf', 0.9), ('jh_coef', 1.1)] + >>> assert _build_title(combo) ==\ + '"snow_adj":0.800|"rad_trncf":0.900|"jh_coef":1.100' + ''' + + return '|'.join('"{0}":{1:.3f}'.format(param, val) for param, val in combo) diff --git a/build/lib/prms_python/simulation.py b/build/lib/prms_python/simulation.py new file mode 100644 index 0000000..7776198 --- /dev/null +++ b/build/lib/prms_python/simulation.py @@ -0,0 +1,411 @@ +# -*- coding: utf-8 -*- +""" +simulation.py -- Contains ``Simulation`` and ``SimulationSeries`` classes and +associated functions for managing PRMS simulations at a low level. +""" + +from __future__ import print_function +import glob +import multiprocessing as mp +import os +import shutil +import subprocess +import time + +from .data import Data +from .parameters import Parameters +from .util import load_statvar + +OPJ = os.path.join + +class SimulationSeries(object): + ''' + Series of simulations all to be run through a common interface. + + Utilizes :class:`multiprocessing.Pool` class to parallelize the + execution of series of PRMS simulations. SimulationSeries also + allows the user to define the PRMS executable command which is + set to "prms" as default. It is best to add the prms executable to + your $PATH environment variable. Each simulation that is run through + ``SimulationSeries`` will follow the strict file structure as defined + by :func:`Simulation.run()`. This class is useful particularly for + creating new programatic workflows not provided yet by PRMS-Python. + + Arguments: + simulations (list or tuple): list of :class:`Simulation` objects + to be run. + + Example: + Lets say you have already created a series of PRMS models by modifying + the input climatic forcing data, e.g. you have 100 *data* files and + you want to run each using the same *control* and *parameters* file. + For simplicity lets say there is a directory that contains all 100 + *data* files e.g. data1, data2, ... or whatever they are named and + nothing else. This example also assumes that you want each simulation + to be run and stored in directories named after the *data* files as + shown. + + >>> data_dir = 'dir_that_contains_all_data_files' + >>> params = Parameters('path_to_parameter_file') + >>> control_path = 'path_to_control' + >>> # a list comprehension to make multiple simulations with + >>> # different data files, alternatively you could use a for loop + >>> sims = [ + Simulation.from_data + ( + Data(data_file), + params, + control_path, + simulation_dir='sim_{}'.format(data_file) + ) + for data_file in os.listdir(data_dir) + ] + + Next we can use ``SimulationSeries`` to run all of these + simulations in parrallel. For example we may use 8 logical cores + on a common desktop computer. + + >>> sim_series = SimulationSeries(sims) + >>> sim_series.run(nprocs=8) + + The ``SimulationSeries.run()`` method will run all 100 simulations + where chunks of 8 at a time will be run in parrallel. Inputs and + outputs of each simulation will be sent to each simulation's + ``simulation_dir`` following the file structure of + :func:`Simulation.run()`. + + Note: + The :class:`Simulation` and :class:`SimulationSeries` classes + are low-level in that they alone do not create metadata for + PRMS simulation scenarios. In other words they do not produce + any additional files that may help the user know what differs + between individual simulations. + + ''' + + def __init__(self, simulations): + self.series = list(simulations) + + def run(self, prms_exec='prms', nproc=None): + """ + Method to run multiple :class:`Simulation` objects in parrallel. + + Keyword Arguments: + prms_exec (str): name of PRMS executable on $PATH or path to + executable + nproc (int or None): number of logical or physical processors + for parrallel execution of PRMS simulations. + + Example: + see :class:`SimulationSeries` + + Note: + If ``nproc`` is not assigned the deault action is to use half + of the available processecors on the machine using the Python + :mod:`multiprocessing` module. + + """ + if not nproc: + nproc = mp.cpu_count() // 2 + + pool = mp.Pool(processes=nproc) + pool.map(_simulation_runner, self.series) + pool.close() + + return self + + def outputs_iter(self): + ''' + Return a :class:`generator` of directories with the path to the + ``simulation_dir`` as well as paths to the *statvar.dat* output + file, and *data* and *parameters* input files used in the simulation. + + Yields: + :obj:`dict`: dictionary of paths to simulation directory, + input, and output files. + + Example: + >>> ser = SimulationSeries(simulations) + >>> ser.run() + >>> g = ser.outputs_iter() + + Would return something like + + >>> print(g.next()) + { + 'simulation_dir': 'path/to/sim/', + 'statvar': 'path/to/statvar', + 'data': 'path/to/data', + 'parameters': 'path/to/parameters' + } + ''' + dirs = list(s.simulation_dir for s in self.series) + print(dirs) + + return ( + { + 'simulation_dir': d, + 'statvar': OPJ(d, 'outputs', 'statvar.dat'), + 'data': OPJ(d, 'inputs', 'data'), + 'parameters': OPJ(d, 'inputs', 'parameters') + } + for d in dirs + ) + + def __len__(self): + return len(list(self.outputs_iter())) + + +def _simulation_runner(sim): + sim.run(prms_exec='prms') + + +class Simulation(object): + """ + Class that runs and manages file structure for a single PRMS simulation. + + The ``Simulation`` class provides low-level managment of a PRMS simulation + by copying model input files from ``input_dir`` argument to an output dir + ``simulation_dir``. The file stucture for an individual simulation after + calling the ``run`` method is simple, two subdirectories "inputs" and + "outputs" are created under ``simulation_dir`` and the respective input + and output files from the current PRMS simulation are transfered there after + the ``Simulation.run()`` method is called which executes the PRMS model, + (see examples below in :func:`Simulation.run`). + + A ``Simulation`` instance checks that all required PRMS inputs (control, + parameters, data) exist in the expected locations. If simulation_dir is + provided and does not exist, it will be created. If it does exist it will + be overwritten. + + Keyword Arguments: + input_dir (str): path to directory that contains control, parameter, + and data files for the simulation + simulation_dir (str): directory path to bundle inputs and outputs + + Example: + see :func:`Simulation.run()` + + Raises: + RuntimeError: if input directory does not contain a PRMS *data*, + *parameters*, and *control* file. + + """ + def __init__(self, input_dir=None, simulation_dir=None): + # check if model input paths exist + idir = input_dir + self.input_dir = idir + self.simulation_dir = simulation_dir + if idir is not None: + self.control_path = os.path.join(idir, 'control') + self.parameters_path = os.path.join(idir, 'parameters') + self.data_path = os.path.join(idir, 'data') + + if not os.path.exists(self.control_path): + raise RuntimeError('Control file missing from ' + idir) + + if not os.path.exists(self.parameters_path): + raise RuntimeError('Parameter file missing from ' + idir) + + if not os.path.exists(self.data_path): + raise RuntimeError('Data file missing from ' + idir) + # build output (simulation_dir) and move input files there + if simulation_dir is not None: + self.simulation_dir = simulation_dir + if simulation_dir and simulation_dir != input_dir: + + if os.path.exists(simulation_dir): + shutil.rmtree(simulation_dir) + + os.mkdir(simulation_dir) + + shutil.copy(self.control_path, simulation_dir) + shutil.copy(self.data_path, simulation_dir) + shutil.copy(self.parameters_path, simulation_dir) + + self.control_path = os.path.join(simulation_dir, 'control') + self.parameters_path = os.path.join(simulation_dir, + 'parameters') + self.data_path = os.path.join(simulation_dir, 'data') + + else: + self.control_path = None + self.parameters_path = None + self.data_path = None + self.simulation_dir = None + + self.has_run = False + + @classmethod + def from_data(cls, data, parameters, control_path, simulation_dir): + ''' + Create a ``Simulation`` from a :class:`Data` and :class:`Parameter` object, + plus a path to the *control* file, and providing a ``simulation_dir`` + where the simulation should be run. + + Arguments: + data (:class:`Data`): ``Data`` object for simulation + parameters (:class:`Parameters`): ``Parameters`` object for simulation + control_path (str): path to control file + simulation_dir (str): path to directory where simulations will be + run and where input and output will be stored. If it exists it will + be overwritten. + + Returns: + :class:`Simulation` ready to be run using ``simulation_dir`` for + inputs and outputs + + Example: + + >>> d = Data('path_to_data_file') + >>> p = Parameters('path_to_parameters_file') + >>> c = 'path_to_control_file' + >>> sim_dir = 'path_to_create_simulation' + >>> sim = Simulation.from_data(d, p, c, sim_dir) + >>> sim.run() + + Raises: + TypeError: if ``data`` and ``parameters`` arguments are not of type + :class:`Data` and :class:`Parameters` + ''' + + if not isinstance(data, Data): + raise TypeError('data must be instance of Data') + + if not isinstance(parameters, Parameters): + raise TypeError('parameters must be instance of Parameters, not '\ + + str(type(parameters))) + + if os.path.exists(simulation_dir): + shutil.rmtree(simulation_dir) + + os.makedirs(simulation_dir) + + sim = cls() + sim.simulation_dir = simulation_dir + + sd = simulation_dir + + data_path = OPJ(sd, 'data') + data.write(data_path) + params_path = OPJ(sd, 'parameters') + parameters.write(params_path) + shutil.copy(control_path, OPJ(sd, 'control')) + + return sim + + def run(self, prms_exec='prms'): + """ + Run a ``Simulation`` instance using PRMS input files from ``input_dir`` + and copy to the ``Simulation`` file structure under ``simulation_dir`` if + given, otherwise leave PRMS input output unstructured and in ``input_dir`` + + This method runs a single PRMS simulation from a ``Simulation`` instance, + waits until the process has completed and then transfers model input and + output files to respective newly created directories. See example of the + file structure that is created under different workflows of the ``run`` + method below. + + Keyword Arguments: + prms_exec (str): name of PRMS executable on $PATH or path to executable + + Examples: + If we create a :class:`Simulation` instance by only assigning the + ``input_dir`` argument and call its ``run`` method the model will be + run in the ``input_dir`` and all model input and output files will + remain in ``input_dir``, + + >>> import os + >>> input_dir = os.path.join( + 'PRMS-Python', + 'prms_python', + 'models', + 'lbcd' + ) + >>> os.listdir(input_dir) + ['data', + 'data_3deg_upshift', + 'parameters', + 'parameters_adjusted', + 'control'] + >>> sim = Simulation(input_dir) + >>> sim.run() + >>> os.listdir(input_dir) # all input and outputs in input_dir + ['data', + 'data_3deg_upshift', + 'parameters', + 'parameters_adjusted', + 'control', + 'statvar.dat', + 'prms_ic.out', + 'prms.out' ] + + Instead if we assigned a path for ``simulation_dir`` keyword + argument and then called ``run``, i.e. + + >>> sim = Simulation(input_dir, 'path_simulation') + >>> sim.run() + + the files structure for the PRMS simulation created by ``Simulation.run()`` + would be:: + + path_simulation + ├── inputs + │   ├── control + │   ├── data + │   └── parameters + └── outputs + ├── data_3deg_upshift + ├── parameters_adjusted + ├── prms_ic.out + ├── prms.out + └── statvar.dat + + Note: + As shown in the last example, currently the ``Simulation.run`` routine only + recognizes the *data*, *parameters*. and *control* file as PRMS inputs, + all other files found in ``input_dir`` before *and* after normal completion + of the PRMS simulation will be transferred to ``simulation_dir/outputs/``. + """ + cwd = os.getcwd() + + if self.simulation_dir: + os.chdir(self.simulation_dir) + + else: + os.chdir(self.input_dir) + + p = subprocess.Popen( + prms_exec + ' control', shell=True, stdout=subprocess.PIPE, + stderr=subprocess.PIPE + ) + + prms_finished = False + checked_once = False + while not prms_finished: + + if not checked_once: + p.communicate() + checked_once = True + + poll = p.poll() + prms_finished = poll >= 0 + + self.has_run = True + # avoid too many files open error + p.stdout.close() + p.stderr.close() + + if self.simulation_dir: + os.mkdir('inputs') + os.mkdir('outputs') + shutil.move('data', 'inputs') + shutil.move('parameters', 'inputs') + shutil.move('control', 'inputs') + + # all remaining files are outputs + for g in glob.glob('*'): + if not os.path.isdir(g): + shutil.move(g, 'outputs') + + os.chdir(cwd) diff --git a/build/lib/prms_python/util.py b/build/lib/prms_python/util.py new file mode 100644 index 0000000..c7fa14b --- /dev/null +++ b/build/lib/prms_python/util.py @@ -0,0 +1,346 @@ +# -*- coding: utf-8 -*- +""" +util.py -- Utilities for working with PRMS data or other functionality that aren't +appropriate to put elsewhere at this time. +""" +import warnings +import os, shutil, json +import numpy as np +import pandas as pd + +def calc_emp_CDF(data): + # changed function name for PEP 8 style + warnings.warn("calc_emp_CDF is deprecated, please use "+\ + "util.calc_emp_cdf instead", DeprecationWarning) + return calc_emp_cdf(data) + +def calc_emp_cdf(data): + """ + Create empirical CDF of arbitrary data + + Arguments: + data (array_like) : array to calculate CDF on + + Returns: + X (numpy.ndarray) : array of x values of CDF (sorted data) + + F (numpy.ndarray) : array of CDF values for each X value or cumulative + exceedence probability, in [0,1]. + """ + n_bins = len(data) + X = np.sort(data) + F = np.array(range(n_bins))/float(n_bins) + return X,F + +def Kolmogorov_Smirnov(uncond, cond, n_bins=10000): + # changed function name for PEP 8 style + warnings.warn("Kolmogorov_Smirnov is deprecated, please use "+\ + "util.komogorov_smirnov instead", DeprecationWarning) + return kolmogorov_smirnov(uncond, cond, n_bins=10000) + +def kolmogorov_smirnov(uncond, cond, n_bins=10000): + """ + Calculate the Kolmogorov-Smirnov statistic between two datasets by first + computing their empirical CDFs + + Arguments: + uncond (array_like) : data for creating the unconditional CDF. + cond (array_like) : data for creating the conditional CDF + n_bins (int) : number of bins for both CDFs, note if n_bins > length + of either dataset then CDF values are interpolated by numpy + + Returns: + KS (float) : Kolmogorov-Smirnov statistic, i.e. absolute max distance + between uncond and cond CDFs + """ + # create unconditional CDF (F_Uc) + H,X = np.histogram(uncond, bins=n_bins, normed=True) + dx = X[1] - X[0] + F_Uc = np.cumsum(H)*dx + # create conditional CDF (F_C) + H,X = np.histogram(cond, bins=n_bins, normed=True) + dx = X[1] - X[0] + F_C = np.cumsum(H)*dx + # Calc max absolulte divergence + KS = np.max(np.abs(F_Uc - F_C)) + return KS + +def remove_all_optimization_sims_of_other_stage(work_directory, stage): + """ + Track number of simulation directories not tracked by a specific stage + and recursively delete them and their contents. This was created to avoid + having nutracked simulations in an optimizer working directory for example + when an optimization method was interupted before data was saved to a meta + data file. + + Arguments: + work_directory (str) : Directory to look for Optimization metadata + json files and simulation directories to keep or remove. + stage (str) : Optimization stage that will not have its simulation + data deleted. All other stages if any are found in metadata files + will have their associated simulation directories deleted. + Returns: + None + """ + from .optimizer import OptimizationResult # avoid circular import + + try: + result = OptimizationResult(work_directory,stage=stage) + tracked_dirs = [] + for f in result.metadata_json_paths[stage]: + with open(f) as fh: + json_data = json.load(fh) + tracked_dirs.extend(json_data.get('sim_dirs')) + count = 0 + for d in os.listdir(result.working_dir): + path = os.path.join(result.working_dir, d) + if path in tracked_dirs: + continue + elif os.path.isdir(path) and '_archived' not in path: + count+=1 + for dirpath, dirnames, filenames in os.walk(path,\ + topdown=False): + shutil.rmtree(dirpath, ignore_errors=True) + + # if no json file in working dir for given stage, delete any other sim dirs + except: + count = 0 + for d in os.listdir(work_directory): + path = os.path.join(work_directory, d) + if os.path.isdir(path) and '_archived' not in path: + count+=1 + for dirpath, dirnames, filenames in os.walk(path,\ + topdown=False): + shutil.rmtree(dirpath, ignore_errors=True) + + print('deleted {} simulations that were either not tracked by a JSON file'\ + .format(count) + ' or were not part of {} optimization stage'\ + .format(stage)) + + +def delete_files(work_directory, file_name=''): + """ + Recursively delete all files of a certain name from multiple PRMS + simulations that are within a given directory. Can be useful to removw + large files that are no longer needed. For example initial condition + output files are often large and not always used, similarly animation, + data, control, ... files may no longer be needed. + + Arguments: + work_directory (str) : path to directory with simulations. + file_name (str) : Name of the PRMS input or output file(s) to be + removed, default = '' empty string- nothing will be deleted. + + Example: + e.g. if you have several simulation directories: + + >>> "test/results/intcp:-26.50_slope:0.49", + "test/results/intcp:-11.68_slope:0.54", + "test/results/intcp:-4.70_slope:0.51", + "test/results/intcp:-35.39_slope:0.39", + "test/results/intcp:-20.91_slope:0.41" + + each of these contains an '/inputs' folder with a duplicate data + file that you would like to delete. In this case, delete all + data files like so: + + >>> work_dir = 'test/results/' + >>> delete_files(work_dir, file_name='data') + + Returns: + None + """ + for dirpath, dirnames, filenames in os.walk(work_directory, topdown=False): + paths = (os.path.join(dirpath, filename) for filename in filenames\ + if filename == file_name) + for path in paths: + os.remove(path) + +def load_statvar(statvar_file): + """ + Read the statvar file and load into a datetime indexed + Pandas dataframe object + + Arguments: + statvar_file (str): statvar file path + Returns: + (pandas.DataFrame) Pandas DataFrame of PRMS variables date indexed + from statvar file + """ + # make list of statistical output variables for df header + column_list = ['index', + 'year', + 'month', + 'day', + 'hh', + 'mm', + 'sec'] + + # append to header list the variables present in the file + with open(statvar_file, 'r') as inf: + for idx, l in enumerate(inf): + # first line is always number of stat variables + if idx == 0: + n_statvars = int(l) + elif idx <= n_statvars and idx != 0: + column_list.append(l.rstrip().replace(' ', '_')) + else: + break + + # arguments for read_csv function + missing_value = -999 + skiprows = n_statvars+1 + df = pd.read_csv( + statvar_file, delim_whitespace=True, skiprows=skiprows, + header=-1, na_values=[missing_value] + ) + + # apply correct header names using metadata retrieved from file + df.columns = column_list + date = pd.Series( + pd.to_datetime(df.year*10000+df.month*100+df.day, format='%Y%m%d'), + index=df.index + ) + + # make the df index the datetime for the time series data + df.index = pd.to_datetime(date) + + # drop unneeded columns + df.drop(['index', 'year', 'month', 'day', 'hh', 'mm', 'sec'], + axis=1, inplace=True) + + # name dataframe axes (index,columns) + df.columns.name = 'statistical_variables' + df.index.name = 'date' + + return df + + +def load_data_file(data_file): + # changed function name for PEP 8 style + warnings.warn("load_data_file is deprecated, please use "+\ + "util.load_data instead", DeprecationWarning) + return load_data(data_file) + +def load_data(data_file): + """ + Read the data file and load into a datetime indexed Pandas dataframe object. + + Arguments: + data_file (str): data file path + Returns: + df (pandas.DataFrame): Pandas dataframe of input time series data + from data file with datetime index + """ + # valid input time series that can be put into a data file + valid_input_variables = ('gate_ht', + 'humidity', + 'lake_elev', + 'pan_evap', + 'precip', + 'rain_day', + 'runoff', + 'snowdepth', + 'solrad', + 'tmax', + 'tmin', + 'wind_speed') + # starting list of names for header in dataframe + column_list = ['year', + 'month', + 'day', + 'hh', + 'mm', + 'sec'] + # append to header list the variables present in the file + with open(data_file, 'r') as inf: + for idx, l in enumerate(inf): + + # first line always string identifier of the file- may use later + if idx == 0: + data_head = l.rstrip() + + elif l.startswith('/'): # comment lines + continue + + # header lines with name and number of input variables + if l.startswith(valid_input_variables): + # split line into list, first element name and + # second number of columns + h = l.split() + + # more than one input time series of a particular variable + if int(h[1]) > 1: + for el in range(int(h[1])): + tmp = '{var_name}_{var_ind}'.format(var_name=h[0], + var_ind=el+1) + column_list.append(tmp) + elif int(h[1]) == 1: + column_list.append(h[0]) + # end of header info and begin time series input data + if l.startswith('#'): + skip_line = idx+1 + break + + # read data file into pandas dataframe object with correct header names + missing_value = -999 # missing data representation + df = pd.read_csv(data_file, header=-1, skiprows=skip_line, + delim_whitespace=True, na_values=[missing_value]) + + # apply correct header names using metadata retrieved from file + df.columns = column_list + + # create date column + date = pd.Series( + pd.to_datetime(df.year*10000+df.month*100+df.day, format='%Y%m%d'), + index=df.index + ) + + df.index = pd.to_datetime(date) # make the df index the datetime + + # drop unneeded columns + df.drop(['year', 'month', 'day', 'hh', 'mm', 'sec'], axis=1, inplace=True) + df.columns.name = 'input variables' + df.index.name = 'date' # name dataframe axes (index,columns) + return df + + +def nash_sutcliffe(observed, modeled): + """ + Calculates the Nash-Sutcliffe Goodness-of-fit + + Arguments: + observed (numpy.ndarray): historic observational data + + modeled (numpy.ndarray): model output with matching time index + """ + numerator = sum((observed - modeled)**2) + denominator = sum((observed - np.mean(observed))**2) + + return 1 - (numerator/denominator) + +def percent_bias(observed, modeled): + """ + Calculates percent bias + + Arguments: + observed (numpy.ndarray): historic observational data + + modeled (numpy.ndarray): model output with matching time index + """ + return 100 * ( sum( modeled - observed ) / sum( observed ) ) + + +def rmse(observed, modeled): + """ + Calculates root mean squared error + + Arguments: + observed (numpy.ndarray): historic observational data + + modeled (numpy.ndarray): model output with matching time index + """ + return np.sqrt( sum((observed - modeled)**2) / len(observed) ) + + + diff --git a/build/lib/test/test_prms_python.py b/build/lib/test/test_prms_python.py new file mode 100644 index 0000000..0cce97b --- /dev/null +++ b/build/lib/test/test_prms_python.py @@ -0,0 +1,476 @@ +from copy import copy +import json +import glob +import numpy as np +import os +import re +import shutil +import unittest + +from difflib import Differ +from numpy.testing import assert_array_almost_equal + +from prms_python import ( + modify_params, Parameters, Scenario, ScenarioSeries, Simulation, + SimulationSeries, Data +) + + +OPJ = os.path.join + + +class TestSimulationSeries(unittest.TestCase): + ''' + Expect that a SimulationSeries + ''' + def setUp(self): + self.test_model_data_dir = os.path.join( + 'test', 'data', 'models', 'lbcd' + ) + + self.simulation_dir = os.path.join(self.test_model_data_dir, 'tmp_sim') + + def tearDown(self): + for g in glob.glob(OPJ(self.simulation_dir, '*')): + shutil.rmtree(g) + + def test_simulation_series(self): + tdd = self.test_model_data_dir + data = Data(OPJ(tdd, 'data')) + base_parameters = Parameters(OPJ(tdd, 'parameters')) + + def _copy_mod(base_parameters, val): + ret = copy(base_parameters) + ret['dday_intcp'][:] = val + + return ret + + parameters_gen = ( + (val, _copy_mod(base_parameters, val)) + for val in (-50, -40, -30) + ) + control_path = OPJ(tdd, 'control') + + series = SimulationSeries( + Simulation.from_data( + data, parameters, control_path, + self.simulation_dir + str(val) + ) + for val, parameters in parameters_gen + ) + + outputs = list(series.run().outputs_iter()) + + self.assertEqual(len(series), 3) + self.assertEqual(len(outputs), 3) + + for out in outputs: + sdir = out['simulation_dir'] + assert os.path.isdir(out['simulation_dir']) + assert os.path.exists(OPJ(sdir, 'outputs', 'statvar.dat')) + assert os.path.exists(OPJ(sdir, 'inputs', 'data')) + assert os.path.exists(OPJ(sdir, 'inputs', 'parameters')) + + shutil.rmtree(sdir) + + +class TestSimulation(unittest.TestCase): + """ + Simulations should take a base directory and return a simulation directory + """ + + def setUp(self): + + self.test_data_dir = os.path.join('test', 'data') + + self.test_model_data_dir = os.path.join( + 'test', 'data', 'models', 'lbcd' + ) + + self.simulation_dir = os.path.join(self.test_data_dir, 'tmp_sim') + + def tearDown(self): + + if os.path.exists(self.simulation_dir): + shutil.rmtree(self.simulation_dir) + + def test_simulation_no_simdir(self): + "Simulation should run and write outputs to input directory when simulation_dir is not specified" + s = Simulation(self.test_model_data_dir) + s.run() + + g = [ + os.path.basename(f) + for f in glob.glob(os.path.join(self.test_model_data_dir, '*')) + ] + + self.assertIn('prms_ic.out', g) + self.assertIn('prms.out', g) + self.assertIn('statvar.dat', g) + + def test_simulation_w_simdir(self): + "Simulation should create sim dir with inputs and outputs directory when simulation_dir is specified" + s = Simulation(self.test_model_data_dir, self.simulation_dir) + s.run() + + gs = [ + os.path.basename(f) + for f in glob.glob(os.path.join(self.simulation_dir, '*')) + ] + self.assertIn('inputs', gs) + self.assertIn('outputs', gs) + + assert_valid_input_dir( + self, os.path.join(self.simulation_dir, 'inputs') + ) + assert_valid_output_dir( + self, os.path.join(self.simulation_dir, 'outputs') + ) + + def test_simulation_from_data(self): + """ + Use @classmethod from_data to build a simulation from Parameters and Data instances + """ + tdd = self.test_model_data_dir + + data = Data(OPJ(tdd, 'data')) + parameters = Parameters(OPJ(tdd, 'parameters')) + ctrl = OPJ(tdd, 'control') + + test_dir = OPJ(tdd, 'test-sim-dir') + if os.path.isdir(test_dir): + shutil.rmtree(test_dir) + + s = Simulation.from_data(data, parameters, ctrl, test_dir) + + s.run() + + g = [ + os.path.basename(f) + for f in glob.glob(OPJ(test_dir, 'outputs', '*')) + ] + + self.assertIn('prms_ic.out', g) + self.assertIn('prms.out', g) + self.assertIn('statvar.dat', g) + + # clean up + shutil.rmtree(test_dir) + + +class TestScenario(unittest.TestCase): + + def setUp(self): + + self.test_data_dir = os.path.join('test', 'data') + + self.test_model_data_dir = os.path.join( + 'test', 'data', 'models', 'lbcd' + ) + + self.scenario_dir = os.path.join(self.test_data_dir, 'tmp_scenario') + + def tearDown(self): + + if os.path.exists(self.scenario_dir): + shutil.rmtree(self.scenario_dir) + + def test_create_scenario(self): + """ + a simulation setup should create a simulation directory with correct scenario data + """ + + s = Scenario( + self.test_model_data_dir, self.scenario_dir, + title='Scenario Uno', description='test scenario for prms_python' + ) + + param_mods = { + 'snow_adj': lambda x: 1.1*x, + 'rad_trncf': lambda x: 0.9*x + } + s.build(param_mod_funs=param_mods) + + assert_valid_input_dir(self, self.scenario_dir) # os.path.join(self.scenario_dir, 'inputs')) + + s.run() + + assert_valid_input_dir( + self, os.path.join(self.scenario_dir, 'inputs') + ) + assert_valid_output_dir( + self, os.path.join(self.scenario_dir, 'outputs') + ) + + md_json_path = os.path.join(self.scenario_dir, 'metadata.json') + assert os.path.isfile(md_json_path) + + md_json = json.loads(open(md_json_path).read()) + assert md_json['title'] == 'Scenario Uno' + assert md_json['description'] == 'test scenario for prms_python' + assert 'start_datetime' in md_json + assert 'end_datetime' in md_json + assert 'mod_funs_dict' in md_json + + p_base = Parameters( + os.path.join(self.test_model_data_dir, 'parameters') + ) + p_scen = Parameters( + os.path.join(self.scenario_dir, 'inputs', 'parameters') + ) + + assert_array_almost_equal(p_base['snow_adj']*1.1, p_scen['snow_adj']) + assert_array_almost_equal(p_base['rad_trncf']*0.9, p_scen['rad_trncf']) + + +class TestScenarios(unittest.TestCase): + + def setUp(self): + + self.test_data_dir = os.path.join('test', 'data') + + self.test_model_data_dir = os.path.join( + self.test_data_dir, 'models', 'lbcd' + ) + + self.scenarios_dir = os.path.join(self.test_data_dir, 'tmp_scenarios') + + def tearDown(self): + + if os.path.exists(self.scenarios_dir): + shutil.rmtree(self.scenarios_dir) + + def test_scenario_series(self): + "create_many_simulations should create many simulation directories and correct data" + + s = ScenarioSeries( + self.test_model_data_dir, self.scenarios_dir, + title='scenario series uno', + description=''' +Each scenario is given a title with the schema +'"":|"":', which can be +easily parsed into a Python dictionary later. We use the pipe instead of +comma for easier visual inspection, though this is entirely up to the +user/developer. The scenario consists of parameters scaled for each of the +specified parameters. +''' + ) + + def _scale_param(val): + def scale_by_val(x): + return x*val + return scale_by_val + + scale_arange = np.arange(0.7, 0.9, 0.1) + series_funs = [ + { + 'title': '"rad_trncf":{0:.1f}|"snow_adj":{0:.1f}'.format(val), + 'rad_trncf': _scale_param(val), + 'snow_adj': _scale_param(val) + } + for val in scale_arange + ] + + s.build(series_funs) + + g_series = glob.glob(os.path.join(self.scenarios_dir, '*')) + + # this should be 5 because of series_metadata.json and inputs_dir + assert len(g_series) == 5, g_series + + uuid_pattern = re.compile( + r'^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$' + ) + + uuid_dir_count = 0 + non_uuid_dir_count = 0 + series_dirs = [] + found_metadata = False + for d in g_series: + # XXX TODO this is in flux. at this point maybe all will be dirs + if os.path.isdir(d): + # at this point the files are in the base scenario dir; + # they will be moved later when the scenario is run + assert_valid_input_dir(self, os.path.join(d)) + if re.match(uuid_pattern, os.path.basename(d)): + uuid_dir_count += 1 + assert re.match(uuid_pattern, os.path.basename(d)), os.path.basename(d) + else: + non_uuid_dir_count += 1 + + else: + found_metadata = True + + series_dirs.append(d) + + assert uuid_dir_count == 3 + assert non_uuid_dir_count == 1 + assert found_metadata + + s.run() + + g_series = glob.glob(os.path.join(self.scenarios_dir, '*')) + + series_md_path = os.path.join( + self.scenarios_dir, 'series_metadata.json' + ) + self.assertIn(series_md_path, g_series) + + series_md = json.loads(open(series_md_path).read()) + dir_titles = series_md['uuid_title_map'] + titles = dir_titles.values() + + assert '"rad_trncf":0.7|"snow_adj":0.7' in titles + assert '"rad_trncf":0.8|"snow_adj":0.8' in titles + assert '"rad_trncf":0.9|"snow_adj":0.9' in titles + + p_base = Parameters( + os.path.join(self.test_model_data_dir, 'parameters') + ) + for g in g_series: + if os.path.isdir(g) and 'input' not in g: + assert_valid_output_dir(self, os.path.join(g, 'outputs')) + + md = json.loads(open(os.path.join(g, 'metadata.json')).read()) + + title = md['title'] + scale_vals = eval('{' + title.replace('|', ',') + '}') + + p_scen = Parameters(os.path.join(g, 'inputs', 'parameters')) + + rad_base = p_base['rad_trncf'] + rad_scen = p_scen['rad_trncf'] + + snow_base = p_base['snow_adj'] + snow_scen = p_scen['snow_adj'] + + assert_array_almost_equal( + rad_base*scale_vals['rad_trncf'], rad_scen) + assert_array_almost_equal( + snow_base*scale_vals['snow_adj'], snow_scen) + + +def assert_valid_input_dir(test_case, d): + + gs = [ + os.path.basename(f) + for f in glob.glob(os.path.join(d, '*')) + ] + + test_case.assertIn('control', gs, d) + test_case.assertIn('parameters', gs, d) + test_case.assertIn('data', gs, d) + + +def assert_valid_output_dir(test_case, d): + + go = [ + os.path.basename(f) + for f in + glob.glob(os.path.join(d, '*')) + ] + test_case.assertIn('prms_ic.out', go) + test_case.assertIn('prms.out', go) + test_case.assertIn('statvar.dat', go) + + +class TestParameters(unittest.TestCase): + + def setUp(self): + + self.test_data_dir = os.path.join('test', 'data') + self.test_param = os.path.join(self.test_data_dir, 'parameters') + self.temp_dir = os.path.join('test', 'data', 'tmp') + if os.path.isdir(self.temp_dir): + shutil.rmtree(self.temp_dir) + + os.mkdir(self.temp_dir) + + def tearDown(self): + + if os.path.isdir(self.temp_dir): + shutil.rmtree(self.temp_dir) + + def test_faithful_copy(self): + """ + Parameters class should load and write identical file + """ + test_copy = os.path.join(self.temp_dir, 'param_copy_test') + p = Parameters(self.test_param) + p.write(test_copy) + + expected = open(self.test_param) + generated = open(test_copy) + + # ignore first two lines ignore now date written on Parameters.write + expected = expected.readlines()[2:] + generated = generated.readlines()[2:] + + assert expected == generated + + def test_modify_params(self): + "parameter and data file modification access should work as expected" + + # test parameters mod with 1d rad_trncf + params_out = os.path.join(self.temp_dir, 'modify_params_out') + + mod_d = {'rad_trncf': lambda x: 1.1*x} + modify_params(self.test_param, params_out, mod_d) + + generated = open(params_out).readlines()[2:] + + expected = open( + os.path.join( + self.test_data_dir, 'expected_rad_trncf_1.1' + ) + ).readlines()[2:] + + assert expected == generated + + # test parameters mod with 2d snow_adj + params_out = os.path.join(self.temp_dir, 'modify_params_out') + + mod_d = {'snow_adj': lambda x: 1.1*x} + modify_params(self.test_param, params_out, mod_d) + + generated = open(params_out).readlines()[2:] + + expected = open( + os.path.join( + self.test_data_dir, 'expected_snow_adj_1.1' + ) + ).readlines()[2:] + + for a, b in zip(expected, generated): + try: + a = float(a) + b = float(b) + self.assertAlmostEqual(a, b, places=5) + except ValueError: + assert a == b + + +def colored_string_diff(s1, s2): + """ Writes differences between strings s1 and s2 """ + d = Differ() + diff = d.compare(s1.splitlines(), s2.splitlines()) + diffList = [el for el in diff + if el[0] != ' ' and el[0] != '?'] + + for l in diffList: + + if l[0] == '+': + print(bcolors.GREEN + '+' + l[1:] + bcolors.ENDC) + elif l[0] == '-': + print(bcolors.RED + '-' + l[1:] + bcolors.ENDC) + else: + assert False, 'Error, diffList entry must start with + or -' + + +class bcolors: + HEADER = '\033[95m' + BLUE = '\033[94m' + GREEN = '\033[92m' + YELLOW = '\033[93m' + RED = '\033[91m' + ENDC = '\033[0m' diff --git a/docs/build/doctrees/changelog.doctree b/docs/build/doctrees/changelog.doctree new file mode 100644 index 0000000000000000000000000000000000000000..22b407df7add7deeb00b3efab29d1a9f2d00a284 GIT binary patch literal 10019 zcmd5?O^h5z6<*uB_U?Fp>`m+t;-HoxiIeQi?8Z()90esLS+BFs*s@I&A*`OB>6)pY z?LWGzcXtp!0R_b&%>_CK%#EnZN4hXK?K;nB<-96p2YiX8EBwJoh 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prms_python.simulation — PRMS-Python v1.0.0 + + + + + + + + + + + + + + + +
+
+
+
+ +

Source code for prms_python.simulation

+# -*- coding: utf-8 -*-
+"""
+simulation.py -- Contains ``Simulation`` and ``SimulationSeries`` classes and 
+associated functions for managing PRMS simulations at a low level.
+"""
+
+from __future__ import print_function
+import glob
+import multiprocessing as mp
+import os
+import shutil
+import subprocess
+import time
+
+from .data import Data
+from .parameters import Parameters
+from .util import load_statvar
+
+OPJ = os.path.join
+
+
[docs]class SimulationSeries(object):
+    '''
+    Series of simulations all to be run through a common interface. 
+    
+    Utilizes :class:`multiprocessing.Pool` class to parallelize the 
+    execution of series of PRMS simulations. SimulationSeries also
+    allows the user to define the PRMS executable command which is 
+    set to "prms" as default. It is best to add the prms executable to 
+    your $PATH environment variable. Each simulation that is run through
+    ``SimulationSeries`` will follow the strict file structure as defined
+    by :func:`Simulation.run()`. This class is useful particularly for
+    creating new programatic workflows not provided yet by PRMS-Python.
+
+    Arguments:
+        simulations (list or tuple): list of :class:`Simulation` objects
+            to be run.
+
+    Example:
+        Lets say you have already created a series of PRMS models by modifying
+        the input climatic forcing data, e.g. you have 100 *data* files and
+        you want to run each using the same *control* and *parameters* file.
+        For simplicity lets say there is a directory that contains all 100
+        *data* files e.g. data1, data2, ... or whatever they are named and 
+        nothing else. This example also assumes that you want each simulation 
+        to be run and stored in directories named after the *data* files as 
+        shown.
+
+        >>> data_dir = 'dir_that_contains_all_data_files'
+        >>> params = Parameters('path_to_parameter_file')
+        >>> control_path = 'path_to_control'
+        >>> # a list comprehension to make multiple simulations with
+        >>> # different data files, alternatively you could use a for loop
+        >>> sims = [
+                    Simulation.from_data
+                      (  
+                        Data(data_file),
+                        params,
+                        control_path,
+                        simulation_dir='sim_{}'.format(data_file)
+                      )
+                    for data_file in os.listdir(data_dir)
+                    ]
+
+        Next we can use ``SimulationSeries`` to run all of these 
+        simulations in parrallel. For example we may use 8 logical cores
+        on a common desktop computer.
+
+        >>> sim_series = SimulationSeries(sims)
+        >>> sim_series.run(nprocs=8)
+
+        The ``SimulationSeries.run()`` method will run all 100 simulations
+        where chunks of 8 at a time will be run in parrallel. Inputs and 
+        outputs of each simulation will be sent to each simulation's
+        ``simulation_dir`` following the file structure of 
+        :func:`Simulation.run()`.
+
+    Note:
+        The :class:`Simulation` and :class:`SimulationSeries` classes
+        are low-level in that they alone do not create metadata for
+        PRMS simulation scenarios. In other words they do not produce
+        any additional files that may help the user know what differs
+        between individual simulations. 
+        
+    '''
+
+    def __init__(self, simulations):
+        self.series = list(simulations)
+
+
[docs]    def run(self, prms_exec='prms', nproc=None):
+        """
+        Method to run multiple :class:`Simulation` objects in parrallel.
+
+        Keyword Arguments:
+            prms_exec (str): name of PRMS executable on $PATH or path to 
+                executable
+            nproc (int or None): number of logical or physical processors
+                for parrallel execution of PRMS simulations.  
+
+        Example:
+            see :class:`SimulationSeries`
+
+        Note:
+            If ``nproc`` is not assigned the deault action is to use half
+            of the available processecors on the machine using the Python
+            :mod:`multiprocessing` module. 
+
+        """
+        if not nproc:
+            nproc = mp.cpu_count() // 2 
+
+        pool = mp.Pool(processes=nproc)
+        pool.map(_simulation_runner, self.series)
+        pool.close() 
+
+        return self

+
+
[docs]    def outputs_iter(self):
+        '''
+        Return a :class:`generator` of directories with the path to the 
+        ``simulation_dir`` as well as paths to the *statvar.dat* output 
+        file, and *data* and *parameters* input files used in the simulation.
+
+        Yields:
+            :obj:`dict`: dictionary of paths to simulation directory,
+                input, and output files.
+
+        Example:
+            >>> ser = SimulationSeries(simulations)
+            >>> ser.run()
+            >>> g = ser.outputs_iter()
+
+            Would return something like
+
+            >>> print(g.next())
+                {
+                   'simulation_dir': 'path/to/sim/', 
+                   'statvar': 'path/to/statvar', 
+                   'data': 'path/to/data', 
+                   'parameters': 'path/to/parameters'
+                 }
+        '''
+        dirs = list(s.simulation_dir for s in self.series)
+        print(dirs)
+
+        return (
+            {
+                'simulation_dir': d,
+                'statvar': OPJ(d, 'outputs', 'statvar.dat'),
+                'data': OPJ(d, 'inputs', 'data'),
+                'parameters': OPJ(d, 'inputs', 'parameters')
+            }
+            for d in dirs
+        )        

+
+    def __len__(self):
+        return len(list(self.outputs_iter()))

+
+
+def _simulation_runner(sim):
+    sim.run(prms_exec='prms')
+
+
+
[docs]class Simulation(object):
+    """
+    Class that runs and manages file structure for a single PRMS simulation.
+    
+    The ``Simulation`` class provides low-level managment of a PRMS simulation
+    by copying model input files from ``input_dir`` argument to an output dir
+    ``simulation_dir``. The file stucture for an individual simulation after
+    calling the ``run`` method is simple, two subdirectories "inputs" and 
+    "outputs" are created under ``simulation_dir`` and the respective input
+    and output files from the current PRMS simulation are transfered there after
+    the ``Simulation.run()`` method is called which executes the PRMS model,
+    (see examples below in :func:`Simulation.run`).
+
+    A ``Simulation`` instance checks that all required PRMS inputs (control, 
+    parameters, data) exist in the expected locations. If simulation_dir is 
+    provided and does not exist, it will be created. If it does exist it will 
+    be overwritten.
+ 
+    Keyword Arguments:
+        input_dir (str): path to directory that contains control, parameter, 
+            and data files for the simulation
+        simulation_dir (str): directory path to bundle inputs and outputs
+       
+    Example:
+        see :func:`Simulation.run()`
+
+    Raises:
+        RuntimeError: if input directory does not contain a PRMS *data*,
+            *parameters*, and *control* file.
+
+    """
+    def __init__(self, input_dir=None, simulation_dir=None):
+        # check if model input paths exist
+        idir = input_dir
+        self.input_dir = idir
+        self.simulation_dir = simulation_dir
+        if idir is not None:
+            self.control_path = os.path.join(idir, 'control')
+            self.parameters_path = os.path.join(idir, 'parameters')
+            self.data_path = os.path.join(idir, 'data')
+
+            if not os.path.exists(self.control_path):
+                raise RuntimeError('Control file missing from ' + idir)
+
+            if not os.path.exists(self.parameters_path):
+                raise RuntimeError('Parameter file missing from ' + idir)
+
+            if not os.path.exists(self.data_path):
+                raise RuntimeError('Data file missing from ' + idir)
+            # build output (simulation_dir) and move input files there
+            if simulation_dir is not None:
+                self.simulation_dir = simulation_dir
+                if simulation_dir and simulation_dir != input_dir:
+                    
+                    if os.path.exists(simulation_dir):
+                        shutil.rmtree(simulation_dir)
+
+                    os.mkdir(simulation_dir)
+
+                    shutil.copy(self.control_path, simulation_dir)
+                    shutil.copy(self.data_path, simulation_dir)
+                    shutil.copy(self.parameters_path, simulation_dir)
+
+                    self.control_path = os.path.join(simulation_dir, 'control')
+                    self.parameters_path = os.path.join(simulation_dir,
+                                                        'parameters')
+                    self.data_path = os.path.join(simulation_dir, 'data')
+
+        else:
+            self.control_path = None
+            self.parameters_path = None
+            self.data_path = None
+            self.simulation_dir = None
+
+        self.has_run = False
+
+
[docs]    @classmethod
+    def from_data(cls, data, parameters, control_path, simulation_dir):
+        '''
+        Create a ``Simulation`` from a :class:`Data` and :class:`Parameter` object,
+        plus a path to the *control* file, and providing a ``simulation_dir`` 
+        where the simulation should be run.
+
+        Arguments:
+            data (:class:`Data`): ``Data`` object for simulation
+            parameters (:class:`Parameters`): ``Parameters`` object for simulation 
+            control_path (str): path to control file
+            simulation_dir (str): path to directory where simulations will be
+                run and where input and output will be stored. If it exists it will 
+                be overwritten.
+
+        Returns:
+            :class:`Simulation` ready to be run using ``simulation_dir`` for
+                inputs and outputs
+
+        Example:
+
+            >>> d = Data('path_to_data_file')
+            >>> p = Parameters('path_to_parameters_file')
+            >>> c = 'path_to_control_file'
+            >>> sim_dir = 'path_to_create_simulation'
+            >>> sim = Simulation.from_data(d, p, c, sim_dir)
+            >>> sim.run()
+
+        Raises:
+            TypeError: if ``data`` and ``parameters`` arguments are not of type
+                :class:`Data` and :class:`Parameters`
+        '''
+
+        if not isinstance(data, Data):
+            raise TypeError('data must be instance of Data')
+
+        if not isinstance(parameters, Parameters):
+            raise TypeError('parameters must be instance of Parameters, not '\
+                             + str(type(parameters)))
+
+        if os.path.exists(simulation_dir):
+            shutil.rmtree(simulation_dir)
+
+        os.makedirs(simulation_dir)
+
+        sim = cls()
+        sim.simulation_dir = simulation_dir
+
+        sd = simulation_dir
+
+        data_path = OPJ(sd, 'data')
+        data.write(data_path)
+        params_path = OPJ(sd, 'parameters')
+        parameters.write(params_path)
+        shutil.copy(control_path, OPJ(sd, 'control'))
+
+        return sim

+
+
[docs]    def run(self, prms_exec='prms'):
+        """
+        Run a ``Simulation`` instance using PRMS input files from ``input_dir`` 
+        and copy to the ``Simulation`` file structure under ``simulation_dir`` if
+        given, otherwise leave PRMS input output unstructured and in ``input_dir``
+
+        This method runs a single PRMS simulation from a ``Simulation`` instance,
+        waits until the process has completed and then transfers model input and 
+        output files to respective newly created directories. See example of the 
+        file structure that is created under different workflows of the ``run``
+        method below.
+
+        Keyword Arguments:
+            prms_exec (str): name of PRMS executable on $PATH or path to executable
+        
+        Examples:
+            If we create a :class:`Simulation` instance by only assigning the 
+            ``input_dir`` argument and call its ``run`` method the model will be
+            run in the ``input_dir`` and all model input and output files will
+            remain in ``input_dir``,
+    
+            >>> import os
+            >>> input_dir = os.path.join(
+                                          'PRMS-Python',
+                                          'prms_python',
+                                          'models',
+                                          'lbcd'
+                                        )
+            >>> os.listdir(input_dir)
+                ['data',
+                 'data_3deg_upshift',
+                 'parameters',
+                 'parameters_adjusted',
+                 'control']
+            >>> sim = Simulation(input_dir)
+            >>> sim.run()
+            >>> os.listdir(input_dir) # all input and outputs in input_dir
+                ['data',
+                 'data_3deg_upshift',
+                 'parameters',
+                 'parameters_adjusted',
+                 'control',
+                 'statvar.dat',
+                 'prms_ic.out',
+                 'prms.out' ]
+            
+            Instead if we assigned a path for ``simulation_dir`` keyword 
+            argument and then called ``run``, i.e. 
+            
+            >>> sim = Simulation(input_dir, 'path_simulation')
+            >>> sim.run()
+    
+        the files structure for the PRMS simulation created by ``Simulation.run()`` 
+        would be::
+             
+             path_simulation                             
+             ├── inputs
+             │   ├── control
+             │   ├── data
+             │   └── parameters
+             └── outputs
+                 ├── data_3deg_upshift
+                 ├── parameters_adjusted
+                 ├── prms_ic.out
+                 ├── prms.out
+                 └── statvar.dat
+
+        Note:
+            As shown in the last example, currently the ``Simulation.run`` routine only
+            recognizes the *data*, *parameters*. and *control* file as PRMS inputs,
+            all other files found in ``input_dir`` before *and* after normal completion 
+            of the PRMS simulation will be transferred to ``simulation_dir/outputs/``. 
+        """
+        cwd = os.getcwd()
+
+        if self.simulation_dir:
+            os.chdir(self.simulation_dir)
+
+        else:
+            os.chdir(self.input_dir)
+
+        p = subprocess.Popen(
+            prms_exec + ' control', shell=True, stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE
+        )
+
+        prms_finished = False
+        checked_once = False
+        while not prms_finished:
+
+            if not checked_once:
+                p.communicate()
+                checked_once = True
+
+            poll = p.poll()
+            prms_finished = poll >= 0
+
+        self.has_run = True
+        # avoid too many files open error
+        p.stdout.close()
+        p.stderr.close()
+
+        if self.simulation_dir:
+            os.mkdir('inputs')
+            os.mkdir('outputs')
+            shutil.move('data', 'inputs')
+            shutil.move('parameters', 'inputs')
+            shutil.move('control', 'inputs')
+
+            # all remaining files are outputs
+            for g in glob.glob('*'):
+                if not os.path.isdir(g):
+                    shutil.move(g, 'outputs')
+
+        os.chdir(cwd)

+
+ +
+
+
+ +
+
+
+ ©2016-2018, John Volk and Matthew Turner. + + | + Powered by Sphinx 1.7.5 + & Alabaster 0.7.10 + +
+ + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/changelog.rst.txt b/docs/build/html/_sources/changelog.rst.txt new file mode 100644 index 0000000..9d6b6b3 --- /dev/null +++ b/docs/build/html/_sources/changelog.rst.txt @@ -0,0 +1,2 @@ +.. _changes: +.. include:: ../../CHANGELOG.rst diff --git a/docs/build/html/changelog.html b/docs/build/html/changelog.html new file mode 100644 index 0000000..04288b6 --- /dev/null +++ b/docs/build/html/changelog.html @@ -0,0 +1,162 @@ + + + + + + + + Change log — PRMS-Python v1.0.0 + + + + + + + + + + + + + + + + +
+
+
+
+ +
+

Change log

+
+

Version 1.0.1

+

Substantial improvements and updates to online documentation. Renamed two util +module functions to align with PEP style conventions specifically: +Kolmogorov_Smirnov became kolmogorov_smirnov and calc_emp_CDF +became calc_emp_cdf, also renamed util.load_data_file to +util.load_data for consistency with util.load_statvar. Name changes +are backwards compatible.

+
+

Bug Fixes

+
    +
  • Fix prmspy script errors after prms-python installation with Python 2
    • +
+
+
+
+

Version 1.0.0

+

Stable version and first numbered release on PyPI. All functionality tested +and passed on multiple platforms using several PRMS models.

+
+

Major submodules, classes and functions

+
    +
  • data +- Data
    • +
  • parameters +- Parameters, modify_params
    • +
  • simulation +- Simulation, SimulationSeries
    • +
  • scenario +- Scenario, ScenarioSeries
    • +
  • optimizer +- Optimizer, OptimizationResult, resample_param
    • +
  • util +- load_statvar, nash_sutcliffe, and others
    • +
+

This version also includes the command line interface script “prmspy”. +Ongoing work on Docs website, includes mostly up-to-date Jupyter notebook +examples.

+
+
+
+

Version 0.1.0

+

First numbered version, was not setup for installation using PyPI. Many changes +occured for initial development under this version which were not released.

+
+
+ + +
+
+
+ +
+
+
+ ©2016-2018, John Volk and Matthew Turner. + + | + Powered by Sphinx 1.7.5 + & Alabaster 0.7.10 + + | + Page source +
+ + + + + + \ No newline at end of file From acd0b9016fa7b7bba0a41e49377902e452b35f90 Mon Sep 17 00:00:00 2001 From: John Volk Date: Mon, 24 Sep 2018 15:34:59 -0700 Subject: [PATCH 02/11] remove build directory --- build/lib/prms_python/__init__.py | 24 - build/lib/prms_python/data.py | 263 - build/lib/prms_python/models/lbcd/control | 362 - build/lib/prms_python/models/lbcd/data | 1571 - build/lib/prms_python/models/lbcd/parameters | 1079959 --------------- build/lib/prms_python/optimizer.py | 888 - build/lib/prms_python/parameters.py | 575 - build/lib/prms_python/scenario.py | 601 - build/lib/prms_python/scripts/__init__.py | 0 build/lib/prms_python/scripts/prmspy.py | 225 - build/lib/prms_python/simulation.py | 411 - build/lib/prms_python/util.py | 346 - build/lib/test/test_prms_python.py | 476 - 13 files changed, 1085701 deletions(-) delete mode 100644 build/lib/prms_python/__init__.py delete mode 100644 build/lib/prms_python/data.py delete mode 100644 build/lib/prms_python/models/lbcd/control delete mode 100644 build/lib/prms_python/models/lbcd/data delete mode 100644 build/lib/prms_python/models/lbcd/parameters delete mode 100644 build/lib/prms_python/optimizer.py delete mode 100644 build/lib/prms_python/parameters.py delete mode 100644 build/lib/prms_python/scenario.py delete mode 100644 build/lib/prms_python/scripts/__init__.py delete mode 100644 build/lib/prms_python/scripts/prmspy.py delete mode 100644 build/lib/prms_python/simulation.py delete mode 100644 build/lib/prms_python/util.py delete mode 100644 build/lib/test/test_prms_python.py diff --git a/build/lib/prms_python/__init__.py b/build/lib/prms_python/__init__.py deleted file mode 100644 index 300fd8c..0000000 --- a/build/lib/prms_python/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -""" -PRMS-Python provides a Python interface to PRMS data files and manages -PRMS simulations. This module aims to improve the efficiency of PRMS -workflows by giving access to PRMS data structures while providing -"pythonic" tools to do scenario-based PRMS simulations. By -"scenario-based" we mean testing model hypotheses associated with model -inputs, outputs, and model structure. For example, parameter sensitivity -analysis, where each "scenario" is an iterative perturbation of one or -many parameters. Another example "scenario-based" modeling exercise -would be climate scenario modeling: what will happen to modeled outputs -if the input meteorological data were to change? - -""" - -__name__ = 'prms_python' -__author__ = 'John Volk and Matthew Turner' -__version__ = '1.0.1' - -from prms_python.data import Data -from prms_python.optimizer import Optimizer, OptimizationResult -from prms_python.parameters import Parameters, modify_params -from prms_python.simulation import Simulation, SimulationSeries -from prms_python.scenario import Scenario, ScenarioSeries -from prms_python.util import load_statvar, load_data, nash_sutcliffe diff --git a/build/lib/prms_python/data.py b/build/lib/prms_python/data.py deleted file mode 100644 index 2e2f831..0000000 --- a/build/lib/prms_python/data.py +++ /dev/null @@ -1,263 +0,0 @@ -# -*- coding: utf-8 -*- -''' -data.py -- holds ``Data`` class for standard PRMS climate input data. -''' - -import pandas as pd -from shutil import copyfile - -class Data(object): - """ - Object to access or create a PRMS data file with ability to load/assign it to a - date-time indexed pandas.DataFrame for data management, analysis and visualization. - It can be used to build a new PRMS data file from user defined metadata and a - ``pandas.DataFrame`` of PRMS datetime-indexed climatic forcing and observation - variables. - - The class properties ``metadata`` and ``data_frame`` can be later assigned if no - ``base_file`` is given on initialization, allowing for the creation of PRMS climatic - forcing file in a Python environment. - - Keyword Arguments: - base_file (str, optional): path to standard PRMS data file - na_rep (int, optional): how to represent missing values default = -999 - - Attributes: - date_header (list): date and time header for PRMS data file - valid_input_variables (tuple): valid hydro-climate variables for PRMS data file - - Note: - If using the ``Data`` class to create a new data file, it is up to the user - to ensure that the metadata and :class:`pandas.DataFrame` assigned are correct - and compatible. - - """ - - ## data file constant attributes - date_header = ['year', - 'month', - 'day', - 'hh', - 'mm', - 'sec'] - - valid_input_variables = ('gate_ht', - 'humidity', - 'lake_elev', - 'pan_evap', - 'precip', - 'rain_day', - 'runoff', - 'snowdepth', - 'solrad', - 'tmax', - 'tmin', - 'wind_speed') - - def __init__(self, base_file=None, na_rep=-999): - self.base_file = base_file - self.na_rep = na_rep - self._metadata = None - self._data_frame = None - - @property - def metadata(self): - """ - :obj:`dict`:A property that gets and sets the header information from - a standard PRMS climate input data file held in a Python dictionary. As - a property it can be assigned directly to overwrite or create a new PRMS - data file. As such the user is in control and must supply the correct - syntax for PRMS standard data files, e.g. text lines before header should - begin with "//". Here is an example of the information gathered and held in - this attribute: - - Example: - >>> data.metadata - { - 'data_startline' : 6, - 'data_variables' : ['runoff 1', 'runoff 2', 'tmin', 'tmax', 'ppt'] - 'text_before_header' : "Title of data file \\n //some comments\\nrunoff 2 - \\ntmin 1\\ntmax 1\\nppt 1\\nrunoff 2\\ntmin 1 - \\ntmax 1\\nppt 1\\n - ########################################\\n" - } - - Note: - When assigning or creating a new data file, the ``Data.write`` method will - assign the appropriate date header that follows the line of number signs "#". - - Raises: - ValueError: if data in metadata is accessed before data is assigned, - e.g. if accessed to write a PRMS data file from a ``Data`` instance - that was initialized without a valid PRMS data file. - TypeError: if an object that is not a Python dictionary is assigned. - - """ - # to avoid overwriting pre-assigned data, check if already exists - if isinstance(self._metadata, dict): - return self._metadata - elif not self.base_file: - raise ValueError('No data file was given on initialization') - - ## starting list for variable names in data file - input_data_names = [] - text_before_header = str() - ## open data file and read header information - with open(self.base_file, 'r') as inf: - for idx,l in enumerate(inf): - text_before_header+=l - if idx == 0: ## first line always string identifier of the file- may use later - data_head = l.rstrip() - elif l.startswith('/'): ## comment lines - continue - if l.startswith(Data.valid_input_variables): - h = l.split() ## split, first name and second number of columns - if int(h[1]) > 1: ## more than one input time series of a particular variable - for el in range(int(h[1])): - tmp = '{var_name} {var_ind}'.format(var_name = h[0], var_ind = el+1) - input_data_names.append(tmp) - elif int(h[1]) == 1: - input_data_names.append(h[0]) - if l.startswith('#'): ## end of header info and begin time series input data - data_startline = idx+1 ## 0 indexed line of first data entry - break - - self._metadata = dict([('data_startline',data_startline), - ('data_variables',input_data_names), - ('text_before_header',text_before_header)]) - return self._metadata - - @metadata.setter - def metadata(self, dic): - if not isinstance(dic, dict): - raise TypeError('Must assign a Python dictionary for new Data object/file metadata') - self._metadata = dic - - @property - def data_frame(self): - """ - A property that gets and sets the climatic forcing data for a standard PRMS - climate input data file as a :class:`pandas.DataFrame`. - - Example: - d is a Data instance, calling - - >>> d.data_frame - input variables runoff 1 runoff 2 runoff 3 precip tmax tmin - date - 1996-12-27 0.54 1.6 NaN 0.0 46 32.0 - 1996-12-28 0.65 1.6 NaN 0.0 45 24.0 - 1996-12-29 0.80 1.6 NaN 0.0 44 28.0 - 1996-12-30 0.90 1.6 NaN 0.0 51 33.0 - 1996-12-31 1.00 1.7 NaN 0.0 47 32.0 - - shows the date-indexed ``pd.DataFrame`` of the input data that is created - when a ``Data`` object is initiated if given a valid ``base_file``, i.e. - file path to a PRMS climate data file. - - Raises: - ValueError: if attribute is accessed before either assigning a PRMS data - file on ``Data`` initialization or not assigning a compatabile - date-indexed ``pandas.DataFrame`` of hydro-climate variables. - TypeError: if a data type other than ``pandas.DataFrame`` is assigned. - """ - if not self._metadata: - self.metadata - elif not isinstance(self._data_frame, pd.DataFrame) and self.base_file == None: - raise ValueError('No data base_file given on initialization, '\ - 'therefore you must assign a DataFrame'\ - +' before accessing the .data_frame attribute!') - # to avoid overwriting pre-assigned data - elif isinstance(self._data_frame, pd.DataFrame): - return self._data_frame - - df = pd.read_csv(self.base_file, header = -1, skiprows = self.metadata['data_startline'], - delim_whitespace = True, na_values = [self.na_rep]) ## read data file - df.columns = Data.date_header + self.metadata['data_variables'] - date = pd.Series(pd.to_datetime(df.year * 10000 + df.month * 100 +\ - df.day, format = '%Y%m%d'), index = df.index) - df.index = pd.to_datetime(date) ## assign datetime index - df.drop(Data.date_header, axis = 1, inplace = True) ## unneeded columns - df.columns.name = 'input variables' ; df.index.name = 'date' - self._data_frame = df - return self._data_frame - - @data_frame.setter - def data_frame(self, df): - if not isinstance(df, pd.DataFrame): - raise TypeError("Must assign a Pandas.DataFrame object for PRMS data input") - self._data_frame = df - - def modify(self, func, vars_to_adjust): - """ - Apply a user defined function to one or more variable(s) in the data file. - - The ``modify`` method allows for inplace modification of one or more - time series inputs in the data file based on a user defined function. - - Arguments: - func (function): function to apply to each variable in vars_to_adjust - vars_to_adjust (list or tuple): collection of variable names to apply func to. - - Returns: - None - - Example: - Here is an example of loading a data file, modifying the temperature inputs - (*tmin* and *tmax*) by adding two degrees to each element, and rewritting the - modified data to disk, - - >>> d = Data('path_to_data_file') - >>> def f(x): - return x + 2 - >>> d.modify(f,['tmax','tmin']) - >>> d.write('data_temp_plus_2') - """ - - if not isinstance(self._data_frame, pd.DataFrame): - self.data_frame # will raise ValueError from data_frame property - for v in vars_to_adjust: - self._data_frame[v] = self._data_frame[v].apply(func) - - def write(self, out_path): - """ - Writes the current state of the ``Data`` to PRMS text format - utilizing the ``Data.metadata`` and ``Data.data_frame`` instance - properties. If ``Data.data_frame`` was never accessed or assigned - new values then this method simply copies the original PRMS - data file to ``out_path``. - - Arguments: - out_path (str): full path to save or copy the current PRMS data - in PRMS text format. - - Returns: - None - - Raises: - ValueError: if the ``write`` method is called without assigning either - an initial data (``base_file``) path or assigning correct ``metadata`` - and ``data_frame`` properties. - - """ - # if file data was never accessed- unchanged - if not isinstance(self._data_frame, pd.DataFrame): - if self.base_file: - copyfile(self.base_file, out_path) - else: # if data not from original file and dataframe never assigned - raise ValueError('No data base_file was given and'\ - +' no data was assigned!') - - ## reconstruct PRMS data file format, don't overwrite date-indexed - else: - df = self._data_frame[self.metadata['data_variables']] - df['year'] = self._data_frame.index.year - df['month'] = self._data_frame.index.month - df['day'] = self._data_frame.index.day - df['hh'] = df['mm'] = df['sec'] = 0 - df = df[Data.date_header + self._metadata['data_variables']] - with open(out_path,'w') as outf: # write comment header then data - outf.write(self._metadata['text_before_header']) - df.to_csv(outf, sep=' ', header=None,\ - index=False, na_rep=self.na_rep) - diff --git a/build/lib/prms_python/models/lbcd/control b/build/lib/prms_python/models/lbcd/control deleted file mode 100644 index 1033436..0000000 --- a/build/lib/prms_python/models/lbcd/control +++ /dev/null @@ -1,362 +0,0 @@ -GSFLOW Control File for Lehman and Baker Creek, John Volk 2014 all rights reserved -#### -aniOutON_OFF -1 -1 -0 -#### -print_debug -1 -1 --1 -#### -aniOutVar_names -5 -4 -upslope_interflow -upslope_dunnianflow -soil_moist_tot -upslope_hortonian -sroff -#### -ani_output_file -1 -4 -animation.out -#### -csv_output_file -1 -4 -gsflow.csv -#### -model_output_file -1 -4 -prms.out -#### -data_file -1 -4 -data -#### -dispGraphsBuffSize -1 -4 -15 -#### -dispVar_element -15 -4 -2 -2 -2 -2 -2 -1 -2 -1 -1 -1 -2 -1 -1 -1 -1 -#### -dispVar_names -15 -4 -runoff -sub_cfs -sub_gwflow -sub_interflow -sub_sroff -basin_pweqv -subinc_pkweqv -basin_dunnian -basin_prefflow -basin_slowflow -subinc_precip -basin_rain -basin_snow -basin_tmax -basin_tmin -#### -dispVar_plot -15 -1 -1 -1 -2 -2 -2 -3 -3 -4 -4 -4 -5 -5 -5 -6 -6 -#### -end_time -6 -1 -1996 -12 -31 -0 -0 -0 -#### -et_module -1 -4 -potet_jh -#### -executable_desc -1 -4 -GSFLOW model -#### -executable_model -1 -4 -c:\workspace\mows_software\bin\prms-iv -#### -gsflow_output_file -1 -4 -gsflow.out -#### -init_vars_from_file -1 -1 -0 -#### -initial_deltat -1 -2 -24.0 -#### -mms_user_dir -1 -4 -.\ -#### -mms_user_out_dir -1 -4 -.\ -#### -model_mode -1 -4 -PRMS -#### -modflow_name -1 -4 -lbcd.nam -#### -naniOutVars -1 -1 -5 -#### -ndispGraphs -1 -1 -6 -#### -nstatVars -1 -1 -41 -#### -param_file -1 -4 -parameters -#### -precip_module -1 -4 -precip_1sta -#### -param_print_file -1 -4 -lbcd.parprt -#### -save_vars_to_file -1 -1 -1 -#### -solrad_module -1 -4 -ddsolrad -#### -srunoff_module -1 -4 -srunoff_smidx -#### -start_time -6 -1 -1992 -10 -1 -0 -0 -0 -#### -statVar_element -41 -4 -1 -1 -1 -1 -1 -1 -1 -1 -2 -2 -2 -2 -3 -3 -3 -3 -1 -2 -3 -1 -2 -3 -1 -2 -3 -1 -2 -3 -1 -2 -3 -1 -2 -3 -1 -2490 -1 -1 -1 -1 -2 -#### -statVar_names -41 -4 -basin_gwflow_cfs -basin_sroff_cfs -basin_ssflow_cfs -basin_cfs -sub_cfs -sub_interflow -sub_sroff -sub_gwflow -sub_cfs -sub_interflow -sub_sroff -sub_gwflow -sub_cfs -sub_interflow -sub_sroff -sub_gwflow -subinc_actet -subinc_actet -subinc_actet -subinc_snowcov -subinc_snowcov -subinc_snowcov -sub_sroff -sub_sroff -sub_sroff -subinc_pkweqv -subinc_pkweqv -subinc_pkweqv -subinc_precip -subinc_precip -subinc_precip -subinc_snowmelt -subinc_snowmelt -subinc_snowmelt -basin_potet -swrad -basin_ppt -basin_hortonian -basin_dunnian -subinc_snowmelt -subinc_snowmelt -#### -stat_var_file -1 -4 -statvar.dat -#### -statsON_OFF -1 -1 -1 -#### -cascade_flag -1 -1 -1 -#### -cascadegw_flag -1 -1 -1 -#### -temp_module -1 -4 -temp_1sta -#### -var_save_file -1 -4 -prms_ic.out -#### -stats_output_file -1 -4 -stats1.out -#### -var_init_file -1 -4 -vars -#### -rpt_days -1 -1 -1 -#### -capillary_module -1 -4 -soilzone_prms -#### -strmflow_module -1 -4 -strmflow diff --git a/build/lib/prms_python/models/lbcd/data b/build/lib/prms_python/models/lbcd/data deleted file mode 100644 index 857f114..0000000 --- a/build/lib/prms_python/models/lbcd/data +++ /dev/null @@ -1,1571 +0,0 @@ -Data file created by John Volk 2014 all rights reserved -//////////////////////////////////////////////////////////// -// Station metadata (listed in the same order as the data): -// ID Type Latitude Longitude Elevation Stream -// 10243240 runoff 38.9908 -114.2067 6750 Baker -// 10243260 runoff 39.0116 -114.2144 6700 Lehman -// 10243265 runoff 39.0086 -114.2078 6580 Rowland -// 263340 precip 39.0056 -114.2206 6850 -// 263340 tmax 39.0056 -114.2206 6850 -// 263340 tmin 39.0056 -114.2206 6850 -//////////////////////////////////////////////////////////// -// Unit: runoff = ft3 per "sec " precip = "in " temperature = deg "F " elevation = feet -//////////////////////////////////////////////////////////// -runoff 3 -precip 1 -tmax 1 -tmin 1 -######################################## -1992 10 1 0 0 0 1.2 1.9 -999 0 77 52 -1992 10 2 0 0 0 1.3 2 -999 0 75 53 -1992 10 3 0 0 0 1.4 2.1 -999 0 62 44 -1992 10 4 0 0 0 1.5 2.2 -999 0 62 34 -1992 10 5 0 0 0 1.5 2.2 -999 0 62 35 -1992 10 6 0 0 0 1.5 2.2 -999 0 67 38 -1992 10 7 0 0 0 1.5 2.1 -999 0 64 30 -1992 10 8 0 0 0 1.5 2 -999 0 72 30 -1992 10 9 0 0 0 1.5 1.8 -999 0 68 46 -1992 10 10 0 0 0 1.5 1.6 -999 0 66 37 -1992 10 11 0 0 0 1.4 1.6 -999 0 70 37 -1992 10 12 0 0 0 1.4 1.5 -999 0 76 47 -1992 10 13 0 0 0 1.5 1.5 -999 0 77 54 -1992 10 14 0 0 0 1.5 1.5 -999 0 75 41 -1992 10 15 0 0 0 1.5 1.5 -999 0 73 34 -1992 10 16 0 0 0 1.5 1.6 -999 0 65 41 -1992 10 17 0 0 0 1.5 1.6 -999 0 64 40 -1992 10 18 0 0 0 1.5 1.6 -999 0 72 41 -1992 10 19 0 0 0 1.5 1.6 -999 0 69 45 -1992 10 20 0 0 0 1.4 1.6 -999 0 73 44 -1992 10 21 0 0 0 1.5 1.5 -999 0 71 50 -1992 10 22 0 0 0 1.5 1.6 -999 0 59 43 -1992 10 23 0 0 0 1.5 1.6 -999 0 60 37 -1992 10 24 0 0 0 1.5 1.6 -999 0.05 61 42 -1992 10 25 0 0 0 1.6 1.8 -999 0.21 59 40 -1992 10 26 0 0 0 1.5 1.7 -999 0 57 38 -1992 10 27 0 0 0 1.5 1.6 -999 0.02 59 43 -1992 10 28 0 0 0 1.4 1.6 -999 0 56 38 -1992 10 29 0 0 0 1.4 1.8 -999 0.01 52 39 -1992 10 30 0 0 0 1.9 2.2 -999 0.33 50 37 -1992 10 31 0 0 0 1.5 1.9 -999 0.02 50 30 -1992 11 1 0 0 0 1.4 1.8 -999 0 57 31 -1992 11 2 0 0 0 1.5 1.9 -999 0 56 41 -1992 11 3 0 0 0 1.4 1.8 -999 0 43 26 -1992 11 4 0 0 0 1.3 1.6 -999 0.02 33 20 -1992 11 5 0 0 0 1.4 1.8 -999 0.01 43 26 -1992 11 6 0 0 0 1.3 1.7 -999 0 53 24 -1992 11 7 0 0 0 1.3 1.7 -999 0 62 31 -1992 11 8 0 0 0 1.3 1.7 -999 0 55 24 -1992 11 9 0 0 0 1.4 1.6 -999 0 45 16 -1992 11 10 0 0 0 1.3 1.6 -999 0 31 19 -1992 11 11 0 0 0 1.2 1.6 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a/build/lib/prms_python/models/lbcd/parameters +++ /dev/null @@ -1,1079959 +0,0 @@ -PRMS parameter file for Lehman and Baker Creek, John Volk 2014 all rights reserved -Version: 1.7 -** Dimensions ** -#### -ngwcell -10791 -#### -ntemp -1 -#### -nrain -1 -#### -nreach -289 -#### -ndays -366 -#### -nsegment -15 -#### -ncascade -13884 -#### -nevap -0 -#### -nobs -3 -#### -nsub -3 -#### -ndeplval -22 -#### -ngw -10791 -#### -nhrucell -10791 -#### -nmonths -12 -#### -nsol -0 -#### -nssr -10791 -#### -one -1 -#### -ndepl -2 -#### -nform -0 -#### -ncascdgw -13884 -#### -nhru -10791 -** Parameters ** -#### -smidx_exp -1 -nhru -10791 -2 -0.29952 -0.3 -0.30147 -0.30156 -0.30048 -0.30108 -0.30147 -0.30156 -0.30363 -0.30678 -0.30738 -0.30363 -0.30138 -0.30009 -0.2997 -0.29922 -0.29952 -0.30267 -0.3072 -0.31062 -0.31515 -0.3192 -0.32274 -0.32352 -0.32244 -0.31929 -0.31605 -0.31161 -0.30768 -0.30444 -0.30117 -0.29724 -0.29418 -0.29007 -0.2874 -0.28584 -0.28455 -0.28347 -0.28239 -0.28092 -0.27894 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--1.643560093 --1.454449515 --1.270114195 -1.445482558 -1.259346161 -0.882469478 -0.654915077 -0.445133501 -0.47558539 -0.166063684 -0.35299743 -0.963579684 --1.541557192 --1.679811246 --1.573132 --1.799724382 --1.777720488 --1.789956907 --1.738019154 --1.766515825 --0.580375461 --0.336934093 -0.007566589 -0.3208429 -0.443938158 -1.595265019 -1.716061464 -1.734020754 -1.742205188 -1.716061464 -1.742205188 -1.54931558 -1.565112927 -1.280792454 -1.49909682 -1.463313667 -1.49909682 -1.280792454 -1.696000213 -1.565112927 -1.516302471 -1.623463111 -1.280792454 -0.258667945 --1.679367656 --1.453722432 --1.572532131 --1.795505101 --1.782216241 --1.799702373 --1.643560093 --1.701412705 --1.63046463 --1.711433482 --1.104742349 --0.812460038 --0.727053361 --0.812460038 --0.398601555 --1.270114195 --1.270114195 --0.022699234 --0.242460575 --1.454449515 --1.270114195 --0.895627886 --0.812460038 --0.727053361 --1.270114195 -1.041028559 -1.116650902 -1.280792454 --1.760198087 --1.786360104 --1.8 --1.782388793 -1.280792454 -1.565112927 -1.60961053 -1.280792454 -1.49909682 -1.015173104 -0.258667945 -0.007566589 --1.8 --0.022699234 -0.103243471 --1.782388793 --1.77250777 --1.103768195 --0.608942075 --0.867087517 --1.334467838 -0.351787748 -0.653765918 -1.799936385 -0.908722988 -1.116650902 -1.769584949 -1.706292108 -1.60961053 -1.40629253 -0.98900676 -1.280792454 -1.280792454 -1.280792454 -1.280792454 --1.711433482 --1.797519945 --1.270114195 --0.398601555 --1.128480526 -0.625475279 -0.47558539 -0.383830134 -0.383830134 --0.022699234 --0.022699234 -0.040296798 -0.040296798 -0.909787589 -1.259346161 --1.557583095 --1.679811246 --1.8 --1.746247185 --0.549295852 --1.782216241 --1.028646391 --0.520396831 --1.129441288 --0.727053361 --0.45978078 --0.055424626 -0.039063545 -0.164835353 -0.594679785 -1.280792454 -1.706292108 -1.141184609 -1.623463111 -1.366115405 -1.280792454 -1.516302471 -1.280792454 -1.280792454 -1.49909682 -1.425738137 -1.595265019 -1.189196182 -1.280792454 --1.711050905 --1.587618231 --1.690447504 --0.894557664 --1.572532131 --1.291380182 --1.797454791 --1.798897613 --1.313968477 --1.435669695 --1.270114195 --1.270114195 -0.007566589 --0.49016386 --0.727053361 --0.398601555 --0.367824146 --0.812460038 --1.291380182 --0.022699234 --0.022699234 --1.270114195 --1.270114195 --0.812460038 --0.755756605 --1.129441288 --0.812460038 --0.66898777 --1.270114195 --1.291380182 --1.291380182 --1.766515825 --1.790086613 --1.50809417 --1.270114195 --1.153794345 --0.66898777 -1.280792454 -1.54931558 -1.280792454 -0.007566589 --1.8 --1.8 --1.291380182 --0.335722265 --0.211212122 --1.799702373 --1.053337144 --1.078717844 --0.921753859 --1.434925286 -1.280792454 -0.797821948 -1.798250538 -1.280792454 -1.324264959 -1.280792454 -1.280792454 -1.673861574 -1.54931558 -1.280792454 --1.79311377 --1.746247185 --1.746247185 --1.711433482 --1.8 --0.895627886 -0.443938158 --1.270114195 --1.223799887 --0.783117898 --0.458588041 --0.549295852 -0.134674199 -0.197402313 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--1.777526588 --0.022699234 -0.291017189 -0.445133501 --1.291380182 --0.335722265 --0.366616537 -0.007566589 -0.007566589 -0.908722988 --1.701412705 --1.270114195 -1.481432082 -1.280792454 -1.280792454 -1.280792454 --1.746247185 --1.8 --1.8 -0.007566589 --1.8 --1.738339663 -0.564859007 -1.799936385 --0.784228403 -0.007566589 --1.027633868 --0.549295852 --0.783117898 --0.48897681 --0.458588041 --0.783117898 --1.200514248 --0.179898986 -1.40706217 -1.610162305 -1.799936385 -1.60961053 -1.60961053 -1.799936385 -1.799936385 -1.796662298 -1.742514854 -1.788437297 -1.444747123 -1.280792454 -1.189196182 --0.812460038 --1.435669695 --1.153794345 --1.247600223 --0.922813184 --0.610102752 -1.280792454 -1.280792454 -0.797821948 -1.280792454 -1.280792454 -1.280792454 -1.280792454 --1.8 -0.007566589 --1.766515825 --1.782216241 --1.789956907 --1.8 --1.789956907 --1.62994162 --1.667773515 --1.667773515 --1.556964456 --1.556964456 -0.007566589 -0.007566589 -0.351787748 -0.564859007 -0.007566589 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--1.270114195 --1.8 --1.8 --1.799724382 --1.573132 --1.786511256 --0.49016386 --1.270114195 --0.45978078 -0.007566589 -0.908722988 --0.118347954 --0.49016386 --1.729558483 --1.223799887 --0.811359101 -0.383830134 -0.882469478 -1.366918306 -1.42649079 -1.346215357 -1.042034634 -0.827030957 -1.067558638 -1.067558638 -0.653765918 -0.653765918 -1.685188932 -1.734020754 -1.769584949 -1.79658679 -1.794469893 -1.799936385 -1.763548062 -1.636818519 -1.595265019 -1.444747123 -1.066565207 -0.96253754 -0.443938158 -0.382624863 -0.258667945 --0.367824146 -0.007566589 -0.007566589 -0.98900676 -0.797821948 --1.270114195 -1.280792454 --1.760198087 --1.745947569 --1.745947569 --1.766515825 --1.790086613 --1.790086613 --1.701009665 --1.434925286 --1.291380182 --1.766515825 --1.772292582 -0.007566589 -0.007566589 -0.007566589 -0.007566589 -0.164835353 -0.039063545 -0.102011925 -0.504707684 -0.007566589 -0.007566589 -0.007566589 -0.007566589 --1.270114195 --1.291380182 --0.273634777 --0.148530757 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-1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.769 -0.231 -1.000 -1.000 -0.667 -0.333 -0.600 -0.400 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -#### -mapvars_freq -1 -one -1 -1 -1 -#### -mapvars_units -1 -one -1 -1 -0 -#### -ncol -1 -one -1 -1 -109 -#### -prms_warmup -1 -one -1 -1 -0 diff --git a/build/lib/prms_python/optimizer.py b/build/lib/prms_python/optimizer.py deleted file mode 100644 index 5ffde01..0000000 --- a/build/lib/prms_python/optimizer.py +++ /dev/null @@ -1,888 +0,0 @@ -# -*- coding: utf-8 -*- -''' -optimizer.py -- holds ``Optimizer`` and ``OptimizationResult`` classes for -optimization routines and management conducted on PRMS parameters. -''' -from __future__ import print_function -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -import os, sys, json, re, shutil -import multiprocessing as mp -from copy import copy -from copy import deepcopy -from numpy import log10 -from datetime import datetime -from .data import Data -from .parameters import Parameters -from .simulation import Simulation, SimulationSeries -from .util import load_statvar, nash_sutcliffe, percent_bias, rmse - -OPJ = os.path.join - - -class Optimizer: - ''' - Container for PRMS parameter optimization and related routines. - - Currently the ``monte_carlo`` method provides random parameter - resampling routines using uniform and normal random variables. - - Example: - >>> from prms_python import Data, Optimizer, Parameters - >>> params = Parameters('path/to/parameters') - >>> data = Data('path/to/data') - >>> control = 'path/to/control' - >>> work_directory = 'path/to/create/simulations' - >>> optr = Optimizer( - params, - data, - control, - work_directory, - title='the title', - description='desc') - >>> measured = 'path/to/measured/csv' - >>> statvar_name = 'basin_cfs' # or any other valid statvar - >>> params_to_resample = ['dday_intcp', 'dday_slope'] # list of params - >>> optr.monte_carlo(measured, params_to_resample, statvar_name) - - ''' - - #dic for min/max of parameter allowable ranges, add more when needed - param_ranges = {'dday_intcp': (-60.0, 10.0), - 'dday_slope': (0.2, 0.9), - 'jh_coef': (0.005, 0.06), - 'pt_alpha': (1.0, 2.0), - 'potet_coef_hru_mo': (1.0, 2.0), - 'tmax_index': (-10.0, 110.0), - 'tmin_lapse': (-10.0, 10.0), - 'soil_moist_max': (0.001, 10.0), - 'rain_adj': (0.5, 2.0), - 'ppt_rad_adj': (0.0, 0.5), - 'radadj_intcp': (0.0, 1.0), - 'radadj_slope': (0.0, 1.0), - 'radj_sppt': (0.0, 1.0), - 'radj_wppt': (0.0, 1.0), - 'radmax': (0.1, 1.0) - } - - def __init__(self, parameters, data, control_file, working_dir, - title, description=None): - - if isinstance(parameters, Parameters): - self.parameters = parameters - else: - raise TypeError('parameters must be instance of Parameters') - - if isinstance(data, Data): - self.data = data - - else: - raise TypeError('data must be instance of Data') - - input_dir = '{}'.format(os.sep).join(control_file.split(os.sep)[:-1]) - if not os.path.isfile(OPJ(input_dir, 'statvar.dat')): - print('You have no statvar.dat file in your model directory') - print('Running PRMS on original data in {} for later comparison'\ - .format(input_dir)) - sim = Simulation(input_dir) - sim.run() - - if not os.path.isdir(working_dir): - os.mkdir(working_dir) - - self.input_dir = input_dir - self.control_file = control_file - self.working_dir = working_dir - self.title = title - self.description = description - self.measured_arb = None # for arbitrary output methods - self.statvar_name = None - self.arb_outputs = [] - - def monte_carlo(self, reference_path, param_names, statvar_name, \ - stage, n_sims=10, method='uniform', mu_factor=1,\ - noise_factor=0.1, nproc=None): - ''' - The ``monte_carlo`` method of ``Optimizer`` performs parameter - random resampling techniques to a set of PRMS parameters and - executes and manages the corresponding simulations. - - Arguments: - reference_path (str): path to measured data for optimization - param_names (list): list of parameter names to resample - statvar_name (str): name of statisical variable output name for - optimization - stage (str): custom name of optimization stage e.g. 'ddsolrad' - - Keyword Arguments: - n_sims (int): number of simulations to conduct - parameter optimization/uncertaitnty analysis. - method (str): resampling method for parameters (normal or uniform) - mu_factor (float): coefficient to scale mean of the parameter(s) - to resample from when using the normal distribution to resample - i.e. a value of 1.5 will sample from a normal rv with mean - 50% higher than the original parameter mean - noise_factor (float): scales the variance of noise to add to - parameter values when using normal rv (method='normal') - nproc (int): number of processors available to run PRMS simulations - - Returns: - None - ''' - if '_' in stage: - raise ValueError('stage name cannot contain an underscore') - # assign the optimization object a copy of measured data for plots - self.measured_arb = pd.Series.from_csv(reference_path,\ - parse_dates=True) - # statistical variable output name - self.statvar_name = statvar_name - - start_time = datetime.now().isoformat() - - # resample params for all simulations- potential place to serialize - params = [] - for name in param_names: # create list of lists of resampled params - tmp = [] - for idx in range(n_sims): - tmp.append(resample_param(self.parameters, name, how=method,\ - mu_factor=mu_factor, noise_factor=noise_factor)) - params.append(list(tmp)) - - # SimulationSeries comprised of each resampled param set - series = SimulationSeries( - Simulation.from_data( - self.data, _mod_params(self.parameters,\ - [params[n][i] for n in range(len(params))],\ - param_names), - self.control_file, - OPJ( - self.working_dir, # name of sim: first param and mean value - '{0}_{1:.10f}'.format(param_names[0], np.mean(params[0][i])) - ) - ) - for i in range(n_sims) - ) - - if not nproc: - nproc = mp.cpu_count() // 2 - - # run - outputs = list(series.run(nproc=nproc).outputs_iter()) - self.arb_outputs.extend(outputs) # for current instance- add outputs - - end_time = datetime.now().isoformat() - - # json metadata for Monte Carlo run - meta = { 'params_adjusted' : param_names, - 'statvar_name' : self.statvar_name, - 'optimization_title' : self.title, - 'optimization_description' : self.description, - 'start_datetime' : start_time, - 'end_datetime' : end_time, - 'measured' : reference_path, - 'method' : 'Monte Carlo', - 'mu_factor' : mu_factor, - 'noise_factor' : noise_factor, - 'resample': method, - 'sim_dirs' : [], - 'stage': stage, - 'original_params' : self.parameters.base_file, - 'nproc': nproc, - 'n_sims' : n_sims - } - - for output in outputs: - meta['sim_dirs'].append(output['simulation_dir']) - - json_outfile = OPJ(self.working_dir, _create_metafile_name(\ - self.working_dir, self.title, stage)) - - with open(json_outfile, 'w') as outf: - json.dump(meta, outf, sort_keys=True, indent=4,\ - ensure_ascii=False) - - print('{0}\nOutput information sent to {1}\n'.\ - format('-' * 80, json_outfile)) - - def plot_optimization(self, freq='daily', method='time_series',\ - plot_vars='both', plot_1to1=True, return_fig=False,\ - n_plots=4): - """ - Basic plotting of current optimization results with limited options. - Plots measured, original simluated, and optimization simulated variabes - Not recommended for plotting results when n_sims is very large, instead - use options from an OptimizationResult object, or employ a user-defined - method using the result data. - - Keyword Arguments: - freq (str): frequency of time series plots, value can be 'daily' - or 'monthly' for solar radiation - method (str): 'time_series' for time series sub plot of each - simulation alongside measured radiation. Another choice is - 'correlation' which plots each measured daily solar radiation - value versus the corresponding simulated variable as subplots - one for each simulation in the optimization. With coefficients - of determiniationi i.e. square of pearson correlation coef. - plot_vars (str): what to plot alongside simulated srad: - 'meas': plot simulated along with measured swrad - 'orig': plot simulated along with the original simulated swrad - 'both': plot simulated, with original simulation and measured - plot_1to1 (bool): if True plot one to one line on correlation - scatter plot, otherwise exclude. - return_fig (bool): flag whether to return matplotlib figure - - Returns: - f (:obj:`matplotlib.figure.Figure`): If kwarg return_fig=True, then return - copy of the figure that is generated to the user. - """ - if not self.arb_outputs: - raise ValueError('You have not run any optimizations') - var_name = self.statvar_name - X = self.measured_arb - idx = X.index.intersection(load_statvar(self.arb_outputs[0]\ - ['statvar'])['{}'.format(var_name)].index) - X = X[idx] - orig = load_statvar(OPJ(self.input_dir, 'statvar.dat'))['{}'\ - .format(var_name)][idx] - meas = self.measured_arb[idx] - sims = [load_statvar(out['statvar'])['{}'.format(var_name)][idx] for \ - out in self.arb_outputs] - simdirs = [out['simulation_dir'].split(os.sep)[-1].\ - replace('_', ' ') for out in self.arb_outputs] - var_name = '{}'.format(self.statvar_name) - n = len(self.arb_outputs) # number of simulations to plot - # user defined number of subplots from first n_plots results - if (n > n_plots): n = n_plots - # styles for each plot - ms = 4 # markersize for all points - orig_sty = dict(linestyle='none',markersize=ms,\ - markerfacecolor='none', marker='s',\ - markeredgecolor='royalblue', color='royalblue') - meas_sty = dict(linestyle='none',markersize=ms+1,\ - markerfacecolor='none', marker='1',\ - markeredgecolor='k', color='k') - sim_sty = dict(linestyle='none',markersize=ms,\ - markerfacecolor='none', marker='o',\ - markeredgecolor='r', color='r') - ## number of subplots and rows (two plots per row) - nrow = n//2 # round down if odd n - ncol = 2 - odd_n = False - if n/2. - nrow == 0.5: - nrow+=1 # odd number need extra row - odd_n = True - ######## - ## Start plots depnding on key word arguments - ######## - if freq == 'daily' and method == 'time_series': - fig, ax = plt.subplots(n, sharex=True, sharey=True,\ - figsize=(12,n*3.5)) - axs = ax.ravel() - for i,sim in enumerate(sims[:n]): - if plot_vars in ('meas', 'both'): - axs[i].plot(meas, label='Measured', **meas_sty) - if plot_vars in ('orig', 'both'): - axs[i].plot(orig, label='Original sim.', **orig_sty) - axs[i].plot(sim, **sim_sty) - axs[i].set_ylabel('sim: {}'.format(simdirs[i]), fontsize=10) - if i == 0: axs[i].legend(markerscale=5, loc='best') - fig.subplots_adjust(hspace=0) - fig.autofmt_xdate() - #monthly means - elif freq == 'monthly' and method == 'time_series': - # compute monthly means - meas = meas.groupby(meas.index.month).mean() - orig = orig.groupby(orig.index.month).mean() - # change line styles for monthly plots to lines not points - for d in (orig_sty, meas_sty, sim_sty): - d['linestyle'] = '-' - d['marker'] = None - - fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=(12,n*3.5)) - axs = ax.ravel() - for i,sim in enumerate(sims[:n]): - if plot_vars in ('meas', 'both'): - axs[i].plot(meas, label='Measured', **meas_sty) - if plot_vars in ('orig', 'both'): - axs[i].plot(orig, label='Original sim.', **orig_sty) - sim = sim.groupby(sim.index.month).mean() - axs[i].plot(sim, **sim_sty) - axs[i].set_ylabel('sim: {}\nmean'.format(simdirs[i]),\ - fontsize=10) - axs[i].set_xlim(0.5,12.5) - if i == 0: axs[i].legend(markerscale=5, loc='best') - if odd_n: # empty subplot if odd number of simulations - fig.delaxes(axs[n]) - fig.text(0.5, 0.1, 'month') - #x-y scatter - elif method == 'correlation': - ## figure - fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=(12,n*3)) - axs = ax.ravel() - ## subplot dimensions - meas_min = min(X) - meas_max = max(X) - - for i, sim in enumerate(sims[:n]): - Y = sim - sim_max = max(Y) - sim_min = min(Y) - m = max(meas_max,sim_max) - if plot_1to1: - axs[i].plot([0, m], [0, m], 'k--', lw=2) ## one to one line - axs[i].set_xlim(meas_min,meas_max) - axs[i].set_ylim(sim_min, sim_max) - axs[i].plot(X, Y, **sim_sty) - axs[i].set_ylabel('sim: {}'.format(simdirs[i])) - axs[i].set_xlabel('Measured {}'.format(var_name)) - axs[i].text(0.05, 0.95,r'$R^2 = {0:.2f}$'.format(\ - X.corr(Y)**2), fontsize=16,\ - ha='left', va='center', transform=axs[i].transAxes) - if odd_n: # empty subplot if odd number of simulations - fig.delaxes(axs[n]) - - if return_fig: - return fig - -def _create_metafile_name(out_dir, opt_title, stage): - """ - Search through output directory where simulations are conducted - look for all metadata simulation json files and find out if the - current simulation is a replicate. Then use that information to - build the correct file name for the output json file. The series - are typically run in parallel that is why this step has to be - done after running multiple simulations from an optimization stage. - - Arguments: - out_dir (str): path to directory with model results, i.e. - location where simulation series outputs and optimization - json files are located, aka Optimizer.working_dir - opt_title (str): optimization instance title for file search - stage (str): stage of optimization, e.g. 'swrad', 'pet' - - Returns: - name (str): file name for the current optimization simulation series - metadata json file. E.g 'dry_creek_swrad_opt.json', or if - this is the second time you have run an optimization titled - 'dry_creek' the next json file will be returned as - 'dry_creek_swrad_opt1.json' and so on with integer increments - """ - meta_re = re.compile(r'^{}_{}_opt(\d*)\.json'.format(opt_title, stage)) - reps = [] - for f in os.listdir(out_dir): - if meta_re.match(f): - nrep = meta_re.match(f).group(1) - if nrep == '': - reps.append(0) - else: - reps.append(nrep) - - if not reps: - name = '{}_{}_opt.json'.format(opt_title, stage) - else: - # this is the nth optimization done under the same title - n = max(map(int, reps)) + 1 - name = '{}_{}_opt{}.json'.format(opt_title, stage, n) - return name - -def resample_param(params, param_name, how='uniform', mu_factor=1,\ - noise_factor=0.1): - """ - Resample PRMS parameter by shifting all values by a constant that is - taken from a uniform distribution, where the range of the uniform - values is equal to the difference between the min and max of the allowable - range. The parameter min and max are set in ``Optimizer.param_ranges``. - If the resampling method (``how`` argument) is set to 'normal', randomly - sample a normal distribution with mean = mean(parameter) X ``mu_factor`` and - sigma = param allowable range multiplied by ``noise_factor``. If parameters have - array length <= 366 then individual parameter values are resampled otherwise - resample all param values at once, e.g. by taking a single random value - from the uniform distribution. If they are taking all at once using the - normal method then the original values are scaled by mu_factor and a normal - random variable with mean=0 and std dev = parameter range X ``noise_factor``. - - Arguments: - params (:class:`prms_python.Parameters`): ``Parameters`` object - param_name (str): name of PRMS parameter to resample - - Keyword Arguments: - how (str): distribution to resample parameters from in the case - that each parameter element can be resampled (len <=366) - Currently works for uniform and normal distributions. - noise_factor (float): factor to multiply parameter range by, - use the result as the standard deviation for the normal rand. - variable used to add element wise noise. i.e. higher - noise_factor will result in higher variance. Must be > 0. - - Returns: - ret (:obj:`numpy.ndarry`): ndarray of param after resampling - - Raises: - KeyError: if ``param_name`` not a valid parameter name - ValueError: if the parameter range has not been set in - ``Optimizer.param_ranges`` - """ - p_min, p_max = Optimizer.param_ranges.get(param_name,(-1,-1)) - - # create dictionary of parameter basic info (not values) - param_dic = {param['name']: param for param in params.base_params} - if not param_dic.get(param_name): - raise KeyError('{} is not a valid parameter'.format(param_name)) - - if p_min == p_max == -1: - raise ValueError("""{} has not been added to the dictionary of - parameters to resample, add it's allowable min and max value - to the Optimizer.param_ranges attribute in - Optimizer.py""".format(param_name)) - - dim_case = None - nhru = params.dimensions['nhru'] - ndims = param_dic.get(param_name)['ndims'] - dimnames = param_dic.get(param_name)['dimnames'] - length = param_dic.get(param_name)['length'] - param = deepcopy(params[param_name]) - - # could expand list and check parameter name also e.g. cascade_flg - # is a parameter that should not be changed - dims_to_not_change = set(['ncascade','ncascdgw','nreach',\ - 'nsegment']) - if (len(set.intersection(dims_to_not_change, set(dimnames))) > 0): - raise ValueError("""{} should not be resampled as - it relates to the location of cascade flow - parameters.""".format(param_name)) - - # use param info to get dimension info- e.g. if multidimensional - if (ndims == 1 and length <= 366): - dim_case = 'resample_each_value' # for smaller param dimensions - elif (ndims == 1 and length > 366): - dim_case = 'resample_all_values_once' # covers nssr, ngw, etc. - elif (ndims == 2 and dimnames[1] == 'nmonths' and \ - nhru == params.dimensions[dimnames[0]]): - dim_case = 'nhru_nmonths' - elif not dim_case: - raise ValueError('The {} parameter is not set for resampling'.\ - format(param_name)) -# #testing purposes -# print('name: ', param_name) -# print('max_val: ', p_max) -# print('min_val: ', p_min) -# print('ndims: ', ndims) -# print('dimnames: ', dimnames) -# print('length: ', length) -# print('resample_method: ', dim_case) - - s = (p_max - p_min) * noise_factor # std_dev (s) default: param_range/10 - #do resampling based on param dimensions and sampling distribution - if dim_case == 'resample_all_values_once': - if how == 'uniform': - ret = np.random.uniform(low=p_min, high=p_max, size=param.shape) - elif how == 'normal': # scale parameter mean if mu_factor given - param *= mu_factor - tmp = np.random.normal(0, s, size=param.shape) - ret = tmp + param - - elif dim_case == 'resample_each_value': - ret = param - if how == 'uniform': - ret = np.random.uniform(low=p_min, high=p_max, size=param.shape) - elif how == 'normal': # the original value is considered the mean - if len(ret.shape) != 0: - for i, el in enumerate(param): - mu = el * mu_factor - ret[i] = np.random.normal(mu, s) - else: # single value parameter - mu = param * mu_factor - ret = np.random.normal(mu, s) - - # nhru by nmonth dimensional params - elif dim_case == 'nhru_nmonths': - ret = param - if how == 'uniform': - for month in range(12): - ret[month] = np.random.uniform(low=p_min, high=p_max,\ - size=param[0].shape) - elif how == 'normal': - for i, el in enumerate(param): - el *= mu_factor - tmp = np.random.normal(0, s, size=el.shape) - ret[i] = tmp + el - - return ret - -def _mod_params(parameters, params, param_names): - # loop through list of params and assign their values to Parameter instance - ret = copy(parameters) - for idx, param in enumerate(params): - ret[param_names[idx]] = np.array(param) - return ret - - -class OptimizationResult: - """ - The ``OptimizationResult`` object serves to collect and manage output - from an ``Optimizer`` method. Upon initialization and a given optimization - stage that was used when running the Optimizer method, e.g. ``monte_carlo``, - the class gathers all JSON metadata that was produced for the given stage. - The ``OptimizationResult`` has three main user methods: first ``result_table`` - which returns the top n simulations according to four model performance - metrics (Nash-Sutcliffe efficiency (NSE), root-mean squared-error (RMSE), - percent bias (PBIAS), and the coefficient of determination (COEF_DET) as - calculated against measured data. For example the table may look like: - - >>> ddsolrad_res = OptimizationResult(work_directory, stage=stage) - >>> top10 = ddsolrad_res.result_table(freq='monthly',top_n=10) - >>> top10 - ======================== ======== ======= ========= ======== - ddsolrad parameters NSE RMSE PBIAS COEF_DET - ======================== ======== ======= ========= ======== - orig_params 0.956267 39.4725 -0.885715 0.963116 - tmax_index_54.2224631748 0.921626 47.6092 -0.849256 0.94402 - tmax_index_44.8823940703 0.879965 58.9194 5.79603 0.922021 - tmax_index_47.6835387480 0.764133 82.5918 -4.78896 0.837582 - ======================== ======== ======= ========= ======== - - Second, the ``get_top_ranked_sims`` which returns a dictionary that map - key information about the top n ranked simulations, an example returned - dictionary may look like: - - >>> { - 'dir_name' : ['pathToSim1', 'pathToSim2'], - 'param_path' : ['pathToSim1/input/parameters', 'pathToSim2/input/parameters'], - 'statvar_path' : ['pathToSim1/output/statvar.dat', 'pathToSim2/output/statvar.dat'], - 'params_adjusted' : [[param_names_sim1], [param_names_sim2]] - } - - The third method of ``OptimizationResult`` is ``archive`` which essentially - opens all parameter and statvar files from each simulation of the given - stage and archives the parameters that were modified and their modified values - and the statistical variable (PRMS time series output) that is associated with - the optimization stage. Other ``Optimizer`` simulation metadata is also gathered - and new JSON metadata containing only this information is created and written - within a newly created "archived" subdirectory within the same directory that - the ``Optimizer`` routine managed simulations. The ``OptimizationResult.archive`` - method then recursively deletes the simulation data for each of the given stage. - """ - - def __init__(self, working_dir, stage): - """ - Create an ``OptimizationResult`` instance to manage output and analyse parameter- - output relationships as produced by the use of an ``Optimizer`` method of a user - defined optimization stage. - """ - self.working_dir = working_dir - self.stage = stage - self.metadata_json_paths = self._get_optr_jsons(working_dir, stage) - self.total_sims = self._count_total_sims() - self.statvar_name = self._get_statvar_name(stage) - self.measured = self._get_measured(stage) - self.input_dir = self._get_input_dir(stage) - self.input_params = self._get_input_params(stage) - - # if there are more than one input param for given stage - if len(self.input_params) > 1: - print( -"""Warning: there were more than one initial parameter sets used for the - optimization for stage: {}. Make sure to compare the the correct input - params with their corresponding output sims.""".format(stage)) - print('\nThis optimization stage used the\ - following input parameter files:\n{}'.format('\n'.join(self.input_params))) - - def _count_total_sims(self): - # total number of simulations of given stage in working directory - tracked_dirs = [] - for f in self.metadata_json_paths[self.stage]: - with open(f) as fh: - json_data = json.load(fh) - tracked_dirs.extend(json_data.get('sim_dirs')) - return len(tracked_dirs) - - def _get_optr_jsons(self, work_dir, stage): - """ - Retrieve locations of optimization output jsons which contain - metadata needed to understand optimization results. - Create dictionary of each optimization with stage as key and lists - of corresponding json file paths as values. - - Arguments: - work_dir (str): path to directory with model results, i.e. - location where simulation series outputs and optimization - json files are located, aka Optimizer.working_dir - stage (str): the stage ('ddsolrad', 'jhpet', 'flow', etc.) of - the optimization in which to gather the jsons - - Returns: - ret (dict): dictionary of stage (keys) and lists of - json file paths for that stage (values). - """ - - ret = {} - optr_metafile_re = re.compile(r'^.*_{}_opt(\d*)\.json'.\ - format(stage)) - ret[stage] = [OPJ(work_dir, f) for f in\ - os.listdir(work_dir) if\ - optr_metafile_re.match(f)] - return ret - - def _get_input_dir(self, stage): - # retrieves the input directory from the first json file of given stage - json_file = self.metadata_json_paths[stage][0] - with open(json_file) as jf: - meta_dic = json.load(jf) - return '{}'.format(os.sep).join(meta_dic['original_params'].\ - split(os.sep)[:-1]) - - def _get_input_params(self, stage): - json_files = self.metadata_json_paths[stage] - param_paths = [] - for json_file in json_files: - with open(json_file) as jf: - meta_dic = json.load(jf) - param_paths.append(meta_dic['original_params']) - return list(set(param_paths)) - - def _get_sim_dirs(self, stage): - jsons = self.metadata_json_paths[stage] - json_files = [] - sim_dirs = [] - for inf in jsons: - with open(inf) as json_file: - json_files.append(json.load(json_file)) - for json_file in json_files: - sim_dirs.extend(json_file['sim_dirs']) - # list of all simulation directory paths for stage - return sim_dirs - - def _get_measured(self, stage): - # only need to open one json file to get this information - if not self.metadata_json_paths.get(stage): - return # no optimization json files exist for given stage - first_json = self.metadata_json_paths[stage][0] - with open(first_json) as json_file: - json_data = json.load(json_file) - measured_series = pd.Series.from_csv(json_data.get('measured'),\ - parse_dates=True) - return measured_series - - def _get_statvar_name(self, stage): - # only need to open one json file to get this information - try: - first_json = self.metadata_json_paths[stage][0] - except: - raise ValueError("""No optimization has been run for - stage: {}""".format(stage)) - with open(first_json) as json_file: - json_data = json.load(json_file) - var_name = json_data.get('statvar_name') - - return var_name - - def result_table(self, freq='daily', top_n=5, latex=False): - ##TODO: add stats for freq options annual (means or sum) - - sim_dirs = self._get_sim_dirs(self.stage) - if top_n >= len(sim_dirs): - top_n = len(sim_dirs) + 1 # for returning inclusive last sim - sim_names = [path.split(os.sep)[-1] for path in sim_dirs] - meas_var = self._get_measured(self.stage) - statvar_name = self._get_statvar_name(self.stage) - orig_statvar = load_statvar(OPJ(self.input_dir,'statvar.dat'))\ - [statvar_name] - - result_df = pd.DataFrame(columns=\ - ['NSE','RMSE','PBIAS','COEF_DET','ABS(PBIAS)']) - orig_results = pd.DataFrame(index=['orig_params'],\ - columns=['NSE','RMSE','PBIAS','COEF_DET']) - # get datetime indices that overlap from measured and simulated - sim_out = load_statvar(OPJ(sim_dirs[0], 'outputs', 'statvar.dat'))\ - [statvar_name] - idx = meas_var.index.intersection(sim_out.index) - meas_var = copy(meas_var[idx]) - #sim_out = sim_out[idx] - orig_statvar = orig_statvar[idx] - - if freq == 'monthly': - meas_mo = meas_var.groupby(meas_var.index.month).mean() - orig_mo = orig_statvar.groupby(orig_statvar.index.month).mean() - - for i, sim in enumerate(sim_dirs): - try: - sim_out = load_statvar(OPJ(sim, 'outputs', 'statvar.dat'))\ - ['{}'.format(statvar_name)] - except: # simulation might have been removed or missing - pass - - sim_out = sim_out[idx] - if freq == 'daily': - result_df.loc[sim_names[i]] = [\ - nash_sutcliffe(meas_var, sim_out),\ - rmse(meas_var, sim_out),\ - percent_bias(meas_var,sim_out),\ - meas_var.corr(sim_out)**2,\ - np.abs(percent_bias(meas_var, sim_out)) ] - orig_results.loc['orig_params'] = [\ - nash_sutcliffe(orig_statvar,meas_var),\ - rmse(orig_statvar,meas_var),\ - percent_bias(orig_statvar,meas_var),\ - orig_statvar.corr(meas_var)**2] - - elif freq == 'monthly': - sim_out = sim_out.groupby(sim_out.index.month).mean() - result_df.loc[sim_names[i]] = [\ - nash_sutcliffe(meas_mo, sim_out),\ - rmse(meas_mo, sim_out),\ - percent_bias(meas_mo, sim_out),\ - meas_mo.corr(sim_out)**2,\ - np.abs(percent_bias(meas_mo, sim_out)) ] - orig_results.loc['orig_params'] = [\ - nash_sutcliffe(orig_mo,meas_mo),\ - rmse(orig_mo,meas_mo),\ - percent_bias(orig_mo,meas_mo),\ - orig_mo.corr(meas_mo)**2] - - sorted_result = result_df.sort_values(by=['NSE','RMSE','ABS(PBIAS)',\ - 'COEF_DET'], ascending=[False,True,True,False]) - sorted_result.columns.name = '{} parameters'.format(self.stage) - sorted_result = sorted_result[['NSE','RMSE','PBIAS','COEF_DET']] - sorted_result = pd.concat([orig_results,sorted_result]) - - if latex: return sorted_result[:top_n].to_latex(escape=False) - else: return sorted_result[:top_n] - - def get_top_ranked_sims(self, sorted_df): - # use result table to make dic with best param and statvar paths - # index of table are simulation directory names - ret = { - 'dir_name' : [], - 'param_path' : [], - 'statvar_path' : [], - 'params_adjusted' : [] - } - - json_paths = self.metadata_json_paths[self.stage] - - for el in sorted_df.drop('orig_params').index: - ret['dir_name'].append(el) - ret['param_path'].append(OPJ(self.working_dir,el,'inputs',\ - 'parameters')) - ret['statvar_path'].append(OPJ(self.working_dir,el,'outputs',\ - 'statvar.dat')) - for f in json_paths: - with open(f) as fh: - json_data = json.load(fh) - if OPJ(self.working_dir, el) in json_data.get('sim_dirs'): - ret['params_adjusted'].append(\ - json_data.get('params_adjusted')) - - return ret - - def archive(self, remove_sims=True, remove_meta=False, metric_freq='daily'): - """ - Create archive directory to hold json files that contain - information of adjusted parameters, model output, and performance - metrics for each Optimizer simulation of the - OptimizationResult.stage in the OptimizationResult.working_dir. - - Keyword Arguments: - remove_sims (bool): If True recursively delete all folders - and files associated with original simulations of the - OptimizationResult.stage in the - OptimizationResult.working_dir, if False do not delete - simulations. - remove_meta (bool): Whether to delete original Optimizer - JSON metadata files, default is False. - metric_freq (Str): Frequency of output metric computation - for recording of model performance. Can be 'daily' - (default) or 'monthly'. Note, other results can be computed - later with archived results. - - Returns: - None - """ - # create output archive directory - archive_dir = OPJ(self.working_dir,"{}_archived".format(self.stage)) - if not os.path.isdir(archive_dir): - os.mkdir(archive_dir) - - # create table and use to make mapping dic - table = self.result_table(freq=metric_freq,\ - top_n=self.total_sims, latex=False) - map_dic = self.get_top_ranked_sims(table) - - metadata_json_paths = self.metadata_json_paths[self.stage] - # get measured optimization variable path - first_json = metadata_json_paths[0] - - with open(first_json) as json_file: - json_data = json.load(json_file) - measured_path = json_data.get('measured') - - # record info for each simulation and archive to JSONs - # pandas series and numpy arrays are converted to Python lists - # for JSON serialization - for i, sim in enumerate(map_dic.get('dir_name')): - json_path = OPJ(archive_dir, '{sim}.json'.format(sim=sim)) - try: - output_series = load_statvar(OPJ(self.working_dir, sim,\ - 'outputs', 'statvar.dat'))\ - [self.statvar_name] - except: # simulation directory was already removed - continue - - # look for resampling method info for the particular simulation - for f in metadata_json_paths: - with open(f) as tmp_file: - tmp = json.load(tmp_file) - if OPJ(self.working_dir, sim) in tmp.get('sim_dirs'): - resample = tmp.get('resample') - noise_factor = tmp.get('noise_factor') - mu_factor = tmp.get('mu_factor') - - json_data = { - 'param_names' : [], - 'param_values' : [], - 'original_param_path' : self.input_params, - 'measured_path' : measured_path, - 'output_name' : self.statvar_name, - 'output_date_index' : output_series.\ - index.astype(str).tolist(), - 'output_values' : output_series.values.tolist(), - 'metric_freq' : metric_freq, - 'resample' : resample, - 'stage' : self.stage, - 'mu_factor' : mu_factor, - 'noise_factor' : noise_factor, - 'NSE' : table.loc[sim, 'NSE'], - 'RMSE' : table.loc[sim, 'RMSE'], - 'PBIAS' : table.loc[sim, 'PBIAS'], - 'COEF_DET' : table.loc[sim, 'COEF_DET'] - } - - for param in map_dic.get('params_adjusted')[i]: - json_data['param_names'].append(param) - json_data['param_values'].append(Parameters(\ - map_dic.get('param_path')[i])[param]\ - .tolist()) - # save JSON file into archive directory - with open(json_path, 'w') as outf: - json.dump(json_data, outf, sort_keys = True, indent = 4,\ - ensure_ascii = False) - - # recursively delete all simulation directories after archiving - if remove_sims: - path = OPJ(self.working_dir, sim) - for dirpath, dirnames, filenames in os.walk(path,\ - topdown=False): - shutil.rmtree(dirpath, ignore_errors=True) - else: - continue - - # optional delete the original JSON metadata - if remove_meta: - for meta_file in self.metadata_json_paths[self.stage]: - try: - os.remove(meta_file) - except: - continue - - diff --git a/build/lib/prms_python/parameters.py b/build/lib/prms_python/parameters.py deleted file mode 100644 index 77a8ad6..0000000 --- a/build/lib/prms_python/parameters.py +++ /dev/null @@ -1,575 +0,0 @@ -# -*- coding: utf-8 -*- -''' -parameters.py -- holds ``Parameter`` class with multiple functionality for -the standard PRMS parameters input file. -''' - -import datetime, calendar -import io, os -import itertools -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from matplotlib.backends.backend_pdf import PdfPages -from mpl_toolkits.axes_grid1 import make_axes_locatable -from collections import OrderedDict - -OPJ = os.path.join - -class Parameters(object): - ''' - Disk-based representation of a PRMS parameter file. - - For the sake of memory efficiency, we only load parameters from - ``base_file`` that get modified through item assignment or accessed directly. - Internally, a reference is kept to only previously accessed parameter data, - so when ``write`` is called most copying is from ``base_file`` directly. - When parameters are accessed or modified using the dictionary-like syntax, - a ``np.ndarray`` representation of the parameter is returned. As a result - ``numpy`` mathematical rules including efficient vectorization of math applied - to arrays can be applied to modify parameters directly. The ``Parameter`` - objects user methods allow for visualization of most PRMS parameters, function - based modification of parameters, and a write function that writes the data - back to PRMS text format. - - Arguments: - base_file (str): path to PRMS parameters file - - Attributes: - base_file (str): path to PRMS parameters file - base_file_reader (file): file handle of PRMS parameters file - dimensions (:obj:`collections.OrderedDict`): dictionary with - parameter dimensions as defined in parameters file loaded on - initialization - base_params (list of dicts): list of dictionaries of parameter - metadata loaded on initialization e.g. name, dimension(s), data - type, length of data array, and lines where data starts and ends - in file - param_arrays (dict): dictionary with parameteter names as keys and - ``numpy.array`` and ``numpy.ndarray`` representations of parameter - values as keys. Initially empty, uses getter and setter functions. - - Example: - >>> p = Parameters('path/to/a/parameter/file') - >>> p['jh_coef'] = p['jh_coef']*1.1 - >>> p.write('example_modified_params') - - will read parameter information from the params file to check that - *jh_coef* is present in the parameter file, read the lines corresponding - to *jh_coef* data and assign the new value as requested. Calling - the ``write`` method next will copy all parameters except *jh_coef* - to the new parameter file and append the newly modified *jh_coef* - to the end of the new file from the modified values stored in the - parameter instance ``p``. - ''' - - def __init__(self, base_file): - self.base_file = base_file - self.base_file_reader = open(base_file) - self.dimensions, self.base_params = self.__read_base(base_file) - self.param_arrays = dict() - - def write(self, out_name): - """ - Writes current state of ``Parameters`` to disk in PRMS text format - - To reduce memory usage the ``write`` method copies parameters - from the initial ``base_file`` parameter file for all parameters - that were never modified. - - Arguments: - out_name (str): path to write ``Parameters`` data to PRMS text - format. - - Returns: - None - """ - data_type_dic = {'1': 'int', - '2': 'float'} # retain PRMS data types - - with open(self.base_file, 'r') as base_file: - with open(out_name, 'w') as out_file: - # write metadata - out_file.write('File Auto-generated by PRMS-Python\n') - out_file.write(datetime.datetime.now().isoformat() + '\n') - - # # write dimensions - out_file.write('** Dimensions **\n') - - # write parameters; pre-sorted by data start line on read - name_is_next = False - params_start = False - write_params_lines = False - for l in base_file: - - if not params_start and l.strip() == '** Parameters **': - out_file.write('** Parameters **\n') - params_start = True - - elif l.strip() == '####': - name_is_next = True - - elif name_is_next: - name = l.strip().split()[0] - if name not in self.param_arrays: - out_file.write('####\n') - out_file.write(name + '\n') - name_is_next = False - write_params_lines = True - else: - write_params_lines = False - name_is_next = False - - elif write_params_lines: - out_file.write(l.strip() + '\n') - - # write all parameters that had been accessed and/or modified - for param, new_arr in self.param_arrays.items(): - - out_file.write('####\n') - - param_info = [el for el in self.base_params - if el['name'] == param].pop() - - out_file.write(str(param_info['name']) + '\n') - out_file.write(str(param_info['ndims']) + '\n') - for dimname in param_info['dimnames']: - out_file.write(dimname + '\n') - out_file.write(str(param_info['length']) + '\n') - out_file.write(str(param_info['vartype']) + '\n') - out_file.writelines([str(a) + '\n' - for a in new_arr.flatten().\ - astype(data_type_dic[param_info\ - ['vartype']])]) - - def plot(self, nrows, which='all', out_dir=None, xlabel=None,\ - ylabel=None, cbar_label=None, title=None, mpl_style=None): - """ - Versatile method that plots most parameters in a standard PRMS parameter - file assuming the PRMS model was built on a uniform spatial grid. - - Plots parameters as line plots for series or 2D spatial grid depending on - parameter dimension. The PRMS parameter file is assumed to hold parameters - for a model that was set up on a uniform rectangular grid with the spatial - index of HRUs starting in the upper left corner and moving left to - right across columns and down rows. Default function is to print four - files, each with plots of varying parameter dimensions as explained - under Kwargs ``which`` and more detailed explanation in the example - `Jupyter notebook `_. - - Arguments: - nrows (int): The number of rows in the PRMS model grid for plotting - spatial parameters. Will only work correctly for rectangular gridded models - with HRU indices starting in the upper left cell moving left to right - across columns and down across rows. - - Keyword Arguments: - which (str): name of PRMS parameter to plot or 'all'. If 'all' then - the function will print 3 multipage pdfs, one for nhru - dimensional parameters, one for nhru by monthly parameters, one - for other parameters of length > 1, and one html file containing - single valued parameters. - out_dir (str): path to an output dir, default current directory - xlabel (str): x label for plot(s) - ylabel (str): y label for plot(s) - cbar_label (str): label for colorbar on spatial plot(s) - title (str): plot title - mpl_style (str, list): name or list of names of matplotlib style sheets to - use for plot(s). - - Returns: - None - - Examples: - If the plot method is called with the keyword argument ``which`` set - to a parameter that has length one, i.e. single valued it will simply - print out the value e.g.: - - >>> p = Parameters('path/to/parameters') - >>> p.plot(nrows=10, which='radj_sppt') - radj_sppt is single valued with value: 0.4924942352224324 - - The default action is particularly useful which makes four multi-page - pdfs of most PRMS parameters where each file contains parameters - of different dimensions e.g.: - - >>> p.plot(nrows=10, which='all', mpl_style='ggplot') - - will produce the following four files named by parameters of certain - dimensions: - - >>> import os - >>> os.listdir(os.getcwd()) # list files in current directory - nhru_param_maps.pdf - nhru_by_nmonths_param_maps.pdf - non_spatial_param_plots.pdf - single_valued_params.html - - """ - params = self - - if not isinstance(params, Parameters): - raise TypeError('params must be instance of Parameters, not '\ - + str(type(params))) - if not out_dir: - out_dir = os.getcwd() - - if not os.path.isdir(out_dir): - os.mkdir(out_dir) - - nhru = params.dimensions['nhru'] - ncols = nhru // nrows - - if not mpl_style: - mpl_style = 'classic' - plt.style.use(mpl_style) - - # make pdfs and html of all parameters seperated in 4 files based on dimension - if which == 'all': - ## spatial parameters with dimension of length nhru - p_names = [param['name'] for param in params.base_params if\ - param['length'] == nhru and len(param['dimnames'])==1] - with PdfPages(OPJ(out_dir,'nhru_param_maps.pdf')) as pdf: - for p in p_names: - try: - plt.figure() - ax = plt.gca() - im = ax.imshow(params['{}'.format(p)].reshape(nrows,ncols), origin='upper') - # origin upper- assumes indices of parameters starts in upper left - divider = make_axes_locatable(ax) - cax = divider.append_axes("right", size="5%", pad=0.05) - plt.colorbar(im, cax=cax) - ax.set_title('{}'.format(p)) - ax.tick_params(left='off', bottom='off', labelleft='off',labelbottom='off') - pdf.savefig() - plt.close() - except: - print('{param} parameter failed to plot'.format(param=p)) - - ## monthly spatial parameters (on plot per month) - p_names = [param['name'] for param in params.base_params if\ - param['dimnames'][0] == 'nhru' and len(param['dimnames'])==2 ] - with PdfPages(OPJ(out_dir,'nhru_by_nmonths_param_maps.pdf')) as pdf: - for p in p_names: - try: - for i in range(12): #month - plt.figure() - ax = plt.gca() - im = ax.imshow(params['{}'.format(p)][i].reshape(nrows, ncols), origin='upper') - divider = make_axes_locatable(ax) - cax = divider.append_axes("right", size="5%", pad=0.05) - plt.colorbar(im, cax=cax) - ax.set_title('{} {}'.format(p, calendar.month_name[i+1])) - ax.tick_params(left='off', bottom='off', labelleft='off', labelbottom='off') - pdf.savefig() - plt.close() - except: - print('{param} for {month} failed to plot'.\ - format(param=p, month=calendar.month_name[i+1])) - - ## non spatial parameters with dimension length > 1 to be plotted as time series - p_names = [param['name'] for param in params.base_params if\ - ( 1 < param['length'] <= 366 )\ - and param['dimnames'][0] != 'nhru' ] - with PdfPages(OPJ(out_dir,'non_spatial_param_plots.pdf')) as pdf: - for p in p_names: - try: - param_dict = [param for param in params.base_params if param['name'] == p][0] - plt.plot(np.arange(1, param_dict['length']+1, 1), params[p]) - plt.xlabel(param_dict['dimnames'][0]) - plt.ylabel(p) - plt.xlim(0.5, param_dict['length']+0.5) - pdf.savefig() - plt.close() - except: - print('{param} parameter failed to plot'.format(param=p)) - - ## html table of parameters with dimension length = 1 - p_names = [param['name'] for param in params.base_params if param['length'] == 1] - df = pd.DataFrame() - df.index.name = 'parameter' - for p in p_names: - df.set_value(p, 'value', params[p]) - df.to_html(OPJ(out_dir,'single_valued_params.html')) - ################################################################ - # plot single parameter, in case of nhru by monthly param, - # save multi-page pdf - else: - param_name = which - try: - params[which] - except: - print('{param} is not a valid PRMS parameter'.format(param=param_name)) - return - - param_dict = [param for param in params.base_params if param['name'] == param_name][0] - - # labels for single plots - if not cbar_label: - cbar_label = param_name - if not title: - title = '' - - # if parameter is not spatial, one dimensional, with length greater than one, plot as line - if param_dict['ndims'] == 1 and ( 1 < param_dict['length'] <= 366 )\ - and param_dict['dimnames'][0] != 'nhru': - if not xlabel: - xlabel = param_dict['dimnames'][0] - if not ylabel: - ylabel = param_name - plt.plot(np.arange(1, param_dict['length']+1,1), params[param_name]) - plt.xlim(0.5, param_dict['length']+0.5) - plt.xlabel(xlabel) - plt.ylabel(ylabel) - plt.title(title) - # if spatial and one dimensional, plot - elif param_dict['ndims'] == 1 and param_dict['length'] == params.dimensions['nhru']: - if not xlabel: - xlabel = '' - if not ylabel: - ylabel = '' - plt.figure() - ax = plt.gca() - im = ax.imshow(params[param_name].reshape(nrows,ncols), origin='upper') - divider = make_axes_locatable(ax) - cax = divider.append_axes("right", size="5%", pad=0.05) - plt.colorbar(im, cax=cax,label=cbar_label) - ax.tick_params(left='off', bottom='off', labelleft='off', labelbottom='off') - ax.set_title(title) - ax.set_ylabel(ylabel) - ax.set_xlabel(xlabel) - # spatial monthly parameter - elif param_dict['dimnames'][0] == 'nhru' and param_dict['dimnames'][1] == 'nmonths'\ - and param_dict['ndims'] == 2: - if not xlabel: - xlabel = '' - if not ylabel: - ylabel = '' - file_name = '{}.pdf'.format(param_name) - with PdfPages(OPJ(out_dir, file_name)) as pdf: - for i in range(12): #month - plt.figure() - ax = plt.gca() - im = ax.imshow(params['{}'.format(param_name)][i].reshape(nrows,ncols), origin='upper') - divider = make_axes_locatable(ax) - cax = divider.append_axes("right", size="5%", pad=0.05) - plt.colorbar(im, cax=cax) - ax.set_title('{} {}'.format(param_name, calendar.month_name[i+1])) - ax.set_ylabel(ylabel) - ax.set_xlabel(xlabel) - ax.tick_params(left='off', bottom='off', labelleft='off', labelbottom='off') - pdf.savefig() - plt.close() - else: - val = params[param_name] - print('{param} is single valued with value: {v}'.format(param=param_name, v=val)) - - def __read_base(self, base_file): - "Read base file returning 2-tuple of dimension and params dict" - - params_startline, dimensions = self.__make_dimensions_dict(base_file) - base_params = self.__make_parameter_dict(base_file, params_startline) - - return (dimensions, base_params) - - def __make_dimensions_dict(self, base_file): - """ - Extract dimensions and each dimension length. Runs before - __make_parameter_dict. - """ - ret = OrderedDict() - - dim_name = '' - dim_len = 0 - # finished = False - found_dim_start = False - # while not finished: - for idx, l in enumerate(self.base_file_reader): - - if l.strip() == '** Dimensions **': # start of dimensions - found_dim_start = True - - elif '#' in l: # comments - pass - - elif l.strip() == '** Parameters **': # start of parameters - dimlines = idx - # finished = True - break - - elif found_dim_start: - - if dim_name == '': - dim_name = l.strip() - else: - dim_len = int(l) - ret.update({dim_name: dim_len}) - dim_name = '' - - return (dimlines, ret) - - def __make_parameter_dict(self, base_file, params_startline=0): - ret = [] - - name = '' - ndims = 0 - dimnames = [] - length = 0 - vartype = '' - - dimnames_read = 0 - data_startline = 0 - - for idx, l in enumerate(self.base_file_reader): - - if '#' in l: - # we have a comment; the next lines will be new - # parameter metadata. No data for the first time through, so - # we don't want to append an metadata blob with empty values - if name: - ret.append( - dict( - name=name, - ndims=ndims, - dimnames=dimnames, - length=length, - vartype=vartype, - data_startline=data_startline - ) - ) - - name = '' - ndims = 0 - dimnames = [] - length = 0 - vartype = '' - dimnames_read = 0 - - elif not name: - name = l.strip().split()[0] # in case old format with integer after name - - elif not ndims: - ndims = int(l.strip()) - - elif not (dimnames_read == ndims): - dimnames.append(l.strip()) - dimnames_read += 1 - - elif not length: - length = int(l.strip()) - - elif not vartype: - vartype = l.strip() - # advance one from current position and account for starting - # to count from zero - data_startline = params_startline + idx + 2 - - # need to append one more time since iteration will have stopped after - # last line - ret.append( - dict( - name=name, - ndims=ndims, - dimnames=dimnames, - length=length, - vartype=vartype, - data_startline=data_startline - ) - ) - - return ret - - def __getitem__(self, key): - """ - Look up a parameter by its name. - - Raises: - KeyError if parameter name is not valid - """ - def load_parameter_array(param_metadata): - - startline = param_metadata['data_startline'] - endline = startline + param_metadata['length'] + 1 - - param_slice = itertools.islice( - io.open(self.base_file, 'rb'), startline, endline - ) - - arr = np.genfromtxt(param_slice) - - if param_metadata['ndims'] > 1: - dimsizes = [ - self.dimensions[d] for d in param_metadata['dimnames'] - ] - dimsizes.reverse() - arr = arr.reshape(dimsizes) - - return arr - - if key in self.param_arrays: - return self.param_arrays[key] - - else: - try: - param_metadata = [ - el for el in self.base_params if el['name'] == key - ].pop() - - except IndexError: - raise KeyError(key) - - arr = load_parameter_array(param_metadata) - - # cache the value for future access (but maybe shouldn't?) - self.param_arrays.update({key: arr}) - - return arr - - def __setitem__(self, key, value): - - if key in self.param_arrays: - cur_arr = self.param_arrays[key] - if not value.shape == cur_arr.shape: - raise ValueError('New array does not match existing') - - self.param_arrays[key] = value - -def modify_params(params_in, params_out, param_mods=None): - ''' - Given a parameter file in and a dictionary of param_mods, write modified - parameters to params_out. - - - Arguments: - params_in (str): location on disk of the base parameter file - params_out (str): location on disk where the modified parameters will - be written - - Keyword Arguments: - param_mods (dict): param name-keyed, param modification function-valued - - Returns: - None - - Example: - Below we modify the monthly *jh_coef* parameter by increasing it 10% - for every month, - - >>> params_in = 'models/lbcd/parameters' - >>> params_out = 'scenarios/jh_coef_1.1/params' - >>> scale_10pct = lambda x: x * 1.1 - >>> modify_params(params_in, params_out, {'jh_coef': scale_10pct}) - - So param_mods is a dictionary of with keys being parameter names and - values a function that operates on a single value. Currently we only - accept functions that operate without reference to any other - parameters. The function will be applied to every cell, month, or - cascade routing rule for which the parameter is defined. - ''' - p_in = Parameters(params_in) - - for k in param_mods: - p_in[k] = param_mods[k](p_in[k]) - - p_in.write(params_out) diff --git a/build/lib/prms_python/scenario.py b/build/lib/prms_python/scenario.py deleted file mode 100644 index 7dea799..0000000 --- a/build/lib/prms_python/scenario.py +++ /dev/null @@ -1,601 +0,0 @@ -# -*- coding: utf-8 -*- -''' -scenario.py -- holds ``Scenario`` and ``ScenarioSeries`` classes for PRMS -managing parameter-based model scenarios that may be used for hypotheses -testing. -''' - -import inspect -import json -import multiprocessing as mp -import os -import shutil -import uuid - -from datetime import datetime -from .parameters import modify_params, Parameters -from .data import Data -from .util import load_statvar -from .simulation import Simulation - - -class ScenarioSeries(object): - """ - Create and manage a series of model runs where parameters are modified. - - First initialize the series with an optional title and description. - Then to build the series the user provides a list of dictionaries with - parameter-function key-value pairs, and optionally a title and - description for each dictionary defining the individual scenario. - - The ScenarioSeries' ``build`` method creates a file structure under - the series directory (``scenarios_dir``) where each subdirectory is - named with a :mod:`uuid` which can be later matched to its title using - the metadata in ``scenario_dir/series_metadata.json`` (see :mod:`json`). - In the future we may add a way for the user to access the results of - the scenario simulations directly through the ``ScenarioSeries`` - instance, but for now the results are written to disk. Therefore each - scenario's title metadata can be used to refer to which parmaters were - modified and how for post-processing and analysis. One could also use - the description metadata for this purpose. - - Arguments: - base_dir (str): path to base inputs; 'control', 'parameters', - and 'data' must be present there - scenarios_dir (str): directory where scenario data will be written - to; will be overwritten or created if it does not exist - - Keyword Arguments: - title (str, optional): title of the ScenarioSeries instance - description (str, optional): description of the ScenarioSeries - instance - - Attributes: - metadata (dict): dictionary with title, description, and UUID map - dictionary for individiual ``Scenario`` output directories, - the UUID dictionary (``uuid_title_map``)is left empty until - calling :meth:`ScenarioSeries.build`. - scenarios (list): empty list that will be filled with ``Scenario``s - after defining them by calling :meth:`ScenarioSeries.build`. - - Example: - There are three steps to both ``Scenario`` and ScenarioSeries, - first we initialize the object - - >>> series = ScenarioSeries( - base_dir = 'dir_with_input_files', - scenarios_dir = 'dir_to_run_series', - title = 'title_for_group_of_scenarios', - description = 'description_for_scenarios' - ) - - The next step is to "build" the ``ScenarioSeries`` by calling the - :meth:`ScenarioSeries.build` method which defines which parameters - to modify, how to modify them, and then performs the modification - which readies the series to be "run" (the last step). See the - :meth:`ScenarioSeries.build` method for the next step example. - - Also see :ref:`scenario_and_scenarioseries_tutorial` for full example - """ - - def __init__(self, base_dir, scenarios_dir, title=None, description=None): - """ - """ - self.base_dir = base_dir - self.scenarios_dir = scenarios_dir - if os.path.exists(scenarios_dir): - shutil.rmtree(scenarios_dir) - - os.mkdir(scenarios_dir) - - shutil.copytree(base_dir, os.path.join(scenarios_dir, 'base_inputs')) - - self.metadata = dict(title=title, - description=description, - uuid_title_map={}) - self.scenarios = [] - - self.outputs = None - - @classmethod - def from_parameters_iter(cls, base_directory, parameters_iter, - title=None, description=None): - ''' - Alternative way to initialize and build a ``ScenarioSeries`` in one - step. - - Create and build a ``ScenarioSeries`` by including the param-keyed- - function-valued dictionary (``parameters_iter``) that is otherwise - passed in :meth:`ScenarioSeries.build`. - - Arguments: - base_directory (str): directory that contains model input files - parameters_iter (list of dicts): list of dictionaries for each - ``Scenario`` as described in :class:`Scenario` and - :meth:`ScenarioSeries.build`. - title (str): title for group of scenarios - description (str): description for group of scenarios - - Returns: - None - ''' - series = cls(base_directory, base_directory, - title=title, description=description) - - for parameters in parameters_iter: - - title = parameters['title'] if 'title' in parameters else None - - uu = str(uuid.uuid4()) - - series.metadata['uuid_title_map'].update({uu: title}) - - scenario_dir = os.path.join(series.scenarios_dir, uu) - - scenario = Scenario(series.base_dir, scenario_dir, title=title) - - scenario.build() - - series.scenarios.append(scenario) - - with open( - os.path.join(series.scenarios_dir, 'series_metadata.json'), 'w' - ) as f: - f.write(json.dumps(series.metadata, indent=2)) - - def __len__(self): - return len(self.scenarios) - - def build(self, scenarios_list): - """ - Build the scenarios from a list of scenario definitions in dicitonary - form. - - Each element of ``scenarios_list`` can have any number of parameters - as keys with a function for each value. The other two acceptable keys - are ``title`` and ``description`` which will be passed on to each - individual Scenario's metadata in ``series_metadata.json`` for future - lookups. The ``build`` method also creates a file structure that uses - UUID values as individiual ``Scenario`` subdirectories as shown below. - - Arguments: - scenarios_list (list): list of dictionaries with key-value - pairs being parameter-function definition pairs or - title-title string or description-description string. - Returns: - None - - Examples: - Following the initialization of a ``ScenarioSeries`` instance as - shown the example docstring there, we "build" the series by - defining a list of param named-keyed function-valued - dictionaries. This example uses arbitrary functions on two - PRMS parameters *snowinfil_max* and *snow_adj*, - - >>> def _function1(x): #Note, function must start with underscore - return x * 0.5 - >>> def _function2(x): - return x + 5 - >>> dic1 = {'snowinfil_max': _function1, 'title': 'scenario1'} - >>> dic2 = {'snowinfil_max': _function2, - 'snow_adj': function1, - 'title': 'scenario2', - 'description': 'we adjusted two snow parameters' - } - >>> example_scenario_list = [dic1, dic2] - >>> # now we can build the series - >>> series.build(example_scenario_list) - - In this example that follows from :class:`ScenarioSeries` example the - file structure that is created by the ``build`` method is as follows:: - - dir_to_run_series - ├── 670d6352-2852-400a-997e-7b12ba34f0b0 - │   ├── control - │   ├── data - │   └── parameters - ├── base_inputs - │   ├── control - │   ├── data - │   └── parameters - ├── ee9526a9-8fe6-4e88-b357-7dfd7111208a - │   ├── control - │   ├── data - │   └── parameters - └── series_metadata.json - - As shown the build method has copied the original inputs from the - ``base_dir`` given on initialization of ``ScenarioSeries`` to a new - subdirectory of the ``scenarios_dir``, it also applied the - modifications to the parameters for both scenarios above and move the - input files to their respective directories. At this stage the - ``metadata`` will not have updated the UUID map dictionary to each - scenarios subdirectory because they have not yet been run. See the - :meth:`ScenarioSeries.run` method for further explanation including - the final file structure and metadata file contents. - """ - - title = None - description = None - for s in scenarios_list: - - if 'title' in s: - title = s['title'] - del s['title'] - - if 'description' in s: - description = s['description'] - del s['description'] - - uu = str(uuid.uuid4()) - - self.metadata['uuid_title_map'].update({uu: title}) - - scenario_path = os.path.join(self.scenarios_dir, uu) - - # create Scenario - scenario = Scenario( - self.base_dir, scenario_path, title=title, - description=description - ) - - # s now only contains parameter keys and function references vals - scenario.build(s) - - self.scenarios.append(scenario) - - with open( - os.path.join(self.scenarios_dir, 'series_metadata.json'), 'w' - ) as f: - - f.write(json.dumps(self.metadata, indent=2)) - - def run(self, prms_exec='prms', nproc=None): - """ - Run a "built" ``ScenarioSeries`` and make final updates - to file structure and metadata. - - Keyword Arguments: - prms_exec (str): name of PRMS executable on $PATH or path to - executable. Default = 'prms' - nproc (int or None): number of processceors available to - parallelize PRMS simulations, if None (default) then use - half of what the :mod:`multiprocessing` detects on the - machine. - - Returns: - None - - Examples: - This example starts where the example ends in - :meth:`ScenarioSeries.build`, calling ``run`` will run the - models for all scenarios and then update the file structure - as well as create individual ``Scenario`` metadata files as such:: - - dir_to_run_series - ├── 5498c21d-d064-45f4-9912-044734fd230e - │   ├── inputs - │   │   ├── control - │   │   ├── data - │   │   └── parameters - │   ├── metadata.json - │   └── outputs - │   ├── prms_ic.out - │   ├── prms.out - │   └── statvar.dat - ├── 9d28ec5a-b570-4abb-8000-8dac113cbed3 - │   ├── inputs - │   │   ├── control - │   │   ├── data - │   │   └── parameters - │   ├── metadata.json - │   └── outputs - │   ├── prms_ic.out - │   ├── prms.out - │   └── statvar.dat - ├── base_inputs - │   ├── control - │   ├── data - │   └── parameters - └── series_metadata.json - - As we can see the file structure follows the combined structures - as defined by :class:`Simulation` and :class:`Scenario`. The content - of the top-level metadata file ``series_metadata.json`` is as such:: - - { - "title": "title_for_group_of_scenarios", - "description": "description_for_scenarios", - "uuid_title_map": { - "5498c21d-d064-45f4-9912-044734fd230e": "scenario1", - "9d28ec5a-b570-4abb-8000-8dac113cbed3": "scenario2" - } - } - - Therefore one can use the :mod:`json` file to track between UUID's and - individual scenario titles. The json files are read as a Python - dictionary which makes them particularly convenient. The contents of - an individual scenarios ``metadata.json`` file included a string - representation of the function(s) that were applied to the - paramter(s):: - - { - "description": null, - "end_datetime": "2018-09-03T00:00:40.793817", - "mod_funs_dict": { - "snowinfil_max": "def _function1(x): - return x * 0.5" - }, - "start_datetime": "2018-09-03T00:00:30.421353", - "title": "scenario1" - } - - Note: - As shown, it is important to give appropriate scenario titles when - building a ``ScenarioSeries`` dictionary in order to later - understand how parameters were modified in each scenario. If not - one would have to rely on the individual ``metadata.json`` files - in each scenario directory which may be more cumbersome. - """ - if not nproc: - nproc = mp.cpu_count()//2 - - pool = mp.Pool(processes=nproc) - pool.map(_scenario_runner, self.scenarios) - - -# multiprocessing req the function be def'd at root scope so it's picklable -def _scenario_runner(scenario, prms_exec='prms'): - scenario.run(prms_exec=prms_exec) - - -class Scenario: - """ - Container for the process in which one modifies input parameters then - runs a simulation while tracking metadata. - - Metadata includes a title and description, if provided, plus start/end - datetime, and parameter names of parameters that were modified including - string representations of the Python modification functions that were - applied to each parameter. The metadata file is in :mod:`json` format - making it conveniently read as a Python dictionary. - - Arguments: - base_dir (str): path to directory that contains initial *control*, - *parameter*, and *data* files to be used for ``Scenario``. - The *parameters* file in ``base_dir`` will not be modifed - instead will be copied to ``scenario_dir`` and them modified. - scenario_dir (str): directory path to bundle inputs and outputs - title (str, optional): title of ``Scenario``, if given will be - added to ``Scenario.metadata`` attribute as well as the - ``metadata.json`` file in ``scenario_dir`` written after - calling the :func:`Scenario.build()` and :func:`Scenario.run()` - methods. - description (str, optional): description of ``Scenario``, also - is added to ``Scenario.metadata`` as ``title``. - - Attributes: - metadata (:class:`scenario.ScenarioMetadata`): a dictionary-like - class in :mod:`prms_python.scenario` that tracks ``Scenario`` and - ``ScenarioSeries`` imformation including user-defined parameter - modifications and descriptions, and file structure. - - Examples: - This example is kept simple for clarity, here we adjust a - single PRMS parameter *tmin_lapse* by using a single arbitrary - mathematical function. We use the example PRMS model included - with PRMS-Python for this example, - - >>> input_dir = 'PRMS-Python/test/data/models/lbcd' - >>> scenario_directory = 'scenario_testing' - >>> title = 'Scenario example' - >>> desc = 'adjust tmin_lapse using sine wave function' - >>> # create Scenario instance - >>> scenario_obj = Scenario - ( - base_dir=input_dir, - scenario_dir=scenario_directory, - title=title, - description=desc - ) - - Next we need to build a dictionary to modify, in this case - *tmin_lapse*, here we use a vectorized sine function - - >>> # build the modification function and dictionary - >>> def a_func(arr): - return 4 + np.sin(np.linspace(0,2*np.pi,num=len(arr))) - >>> # make dictionary with parameter names as keys and modification - >>> # function as values - >>> param_mod_dic = dict(tmin_lapse=a_func) - >>> scenario_obj.build(param_mod_funs=param_mod_dic) - - After building a ``Scenario`` instance the input files are - copied to ``scenario_dir`` which was assigned 'scenario_testing':: - - scenario_testing - ├── control - ├── data - └── parameters - - - After calling ``build`` the input files from ``input_dir`` were - first copied to ``scenario_dir`` and then the functions in - ``param_mod_dic`` are applied the the parameters names (key) - in ``param_mod_dic``. To run the ``Scenario`` use the the ``run`` - method - - >>> scenario_obj.run() - - Now the simulation is run and the ``metadata.json`` file is created, - the final file structure will be similar to this:: - - scenario_testing - ├── inputs - │   ├── control - │   ├── data - │   └── parameters - ├── metadata.json - └── outputs - ├── prms_ic.out - ├── prms.out - └── statvar.dat - - Finally, here is what is contained in ``metadata.json`` for this example - which is also updates in the :attr:`Scenario.metadata` - - >>> scenario_obj.metadata - { - 'title': 'Scenario example', - 'description': 'adjust tmin_lapse using sine wave function', - 'start_datetime': '2018-09-01T19:20:21.723003', - 'end_datetime': '2018-09-01T19:20:31.117004', - 'mod_funs_dict': { - 'tmin_lapse': 'def parab(arr): - return 4 + np.sin(np.linspace(0,2*np.pi,num=len(arr)))' - } - } - - As shown the metadata retirieved the parameter modification function - as a string representation of the exact Python function(s) used for - modifying the user-defined parameter(s). - - Note: - The main differentiator between :class:`Scenario` and - :class:`ScenarioSeries` is that ``Scenario`` is designed for modifying - one or more parameters of a **single** *parameters* file whereas - ``ScenarioSeries`` is designed for modifying and tracking the - modification of one or more parameters in **multiple** PRMS - *parameters* files, therefore resulting in multiple PRMS simulations. - - """ - - def __init__(self, base_dir, scenario_dir, - title=None, description=None): - - self.title = title - self.description = description - - self.base_dir = base_dir - self.scenario_dir = scenario_dir - - self.metadata = ScenarioMetadata(title=title, description=description) - - self.__simulation_ready = False - - def build(self, param_mod_funs=None): - """ - Take a user-defined dictionary with param names as keys and Python - functions as values, copy the original input files as given when - initializing a :class:`Scenario` instance to the ``simulation_dir`` - then apply the functions in the user-defined dictionary to the - parameters there. The ``build`` method must be called before running - the ``Scenario`` (calling :func:`Scenario.run()` ). - - Keyword Arguments: - param_mod_funs (dict): dictionary with parameter names as keys - and Python functions as values to apply to the names (key) - - Returns: - None - - Example: - see :class:`Scenario` for a full example. - - Note: - If the ``scenario_dir`` that was assigned for the current - instance already exists, it will be overwritten when ``build`` - is invoked. - """ - - if isinstance(param_mod_funs, dict): - - # create scenario_dir that will be used as Simulation input dir - if os.path.isdir(self.scenario_dir): - shutil.rmtree(self.scenario_dir) - - os.makedirs(self.scenario_dir) - shutil.copy( - os.path.join(self.base_dir, 'control'), self.scenario_dir - ) - shutil.copy( - os.path.join(self.base_dir, 'data'), self.scenario_dir - ) - - old_params_path = os.path.join(self.base_dir, 'parameters') - new_params_path = os.path.join(self.scenario_dir, 'parameters') - if not param_mod_funs: - shutil.copy(old_params_path, self.scenario_dir) - else: - modify_params(old_params_path, new_params_path, param_mod_funs) - - param_mod_funs_metadata = { - param_name: inspect.getsource(param_mod_fun) - for param_name, param_mod_fun in param_mod_funs.items() - } - - self.metadata['mod_funs_dict'] = param_mod_funs_metadata - - self.simulation = Simulation(self.scenario_dir, self.scenario_dir) - - else: - self.simulation = Simulation(self.scenario_dir) - - self.__simulation_ready = True - - def run(self, prms_exec='prms'): - """ - Run the PRMS simulation for a *built* ``Scenario`` instance. - - Keyword Arguments: - prms_exec (str): name of PRMS executable on $PATH or path to - executable - - Returns: - None - - Examples: - see :class:`Scenario` for full example - - Raises: - RuntimeError: if the :func:`Scenario.build` method has not yet - been called. - """ - if not self.__simulation_ready: - raise RuntimeError( - 'Scenario has not yet been prepared: run build_scenario first' - ) - - self.metadata['start_datetime'] = datetime.now().isoformat() - self.simulation.run(prms_exec=prms_exec) - self.metadata['end_datetime'] = datetime.now().isoformat() - - self.metadata.write(os.path.join(self.scenario_dir, 'metadata.json')) - - -class ScenarioMetadata: - - def __init__(self, title=None, description=None, start_datetime=None, - end_datetime=None, mod_funs_dict=None): - - self.metadata_dict = dict(title=title, - description=description, - start_datetime=start_datetime, - end_datetime=end_datetime, - mod_funs_dict=mod_funs_dict) - - def __getitem__(self, key): - return self.metadata_dict[key] - - def __setitem__(self, key, value): - self.metadata_dict[key] = value - - def __repr__(self): - return self.metadata_dict.__repr__() - - def write(self, output_path): - with open(output_path, 'w') as f: - f.write(json.dumps(self.metadata_dict, ensure_ascii=False,\ - indent=4, sort_keys=True)) - - diff --git a/build/lib/prms_python/scripts/__init__.py b/build/lib/prms_python/scripts/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/build/lib/prms_python/scripts/prmspy.py b/build/lib/prms_python/scripts/prmspy.py deleted file mode 100644 index 7749473..0000000 --- a/build/lib/prms_python/scripts/prmspy.py +++ /dev/null @@ -1,225 +0,0 @@ -import click -import io -import itertools -import json -import matplotlib.pyplot as plt -import numpy as np -import os -import re - -from datetime import datetime -from matplotlib.backends.backend_pdf import PdfPages - -from ..scenario import ScenarioSeries -from ..util import load_data, load_statvar, nash_sutcliffe - - -@click.group() -def prmspy(): - "access PRMS-Python functionality from the command line" - click.echo('\n*** Welcome to PRMS-Python! ***\n') - - -@prmspy.command() -@click.argument('base_data_dir', nargs=1) -@click.option('--params', '-p', nargs=1, type=str, multiple=True, - help='list of parameters to edit') -@click.option('--scale_vals', '-s', nargs=1, type=str, multiple=True, - help='list of scaling values; must be divisible by len(params), ' - 'e.g. "[0.8, 0.9, 1.0, 1.1, 1.2]"' - ) -@click.option('--output-dir', '-o', nargs=1, type=str, - help='directory where scenario data should be written') -@click.option('--title', '-t', nargs=1, type=str, - help='title for this parameter scaling experiment') -@click.option('--description', '-d', nargs=1, type=str, - help='description for this parameter scaling experiment') -@click.option('--run-prms', is_flag=True, - help='run PRMS after creating scenario input data') -@click.option('--prms-exec', '-e', default='prms', - help='PRMS executable to be used, eg prmsV4') -@click.option('--nproc', '-n', default=None, type=int, - help='number of processors to use') -@click.option('--analyze-output', is_flag=True, - help='create analysis of model output in the form of a ' - '"Nash-Sutcliffe Matrix"') -def param_scale_sim(base_data_dir, - params, - scale_vals, - output_dir, - title, - description, - run_prms, - prms_exec, - nproc, - analyze_output): - 'Provide params and scaling values; run PRMS scenarios' - - # scale_vals must be divisible by the number of params - # valid_input = len(scale_vals) % len(params) == 0 - print(scale_vals) - scale_vals = [eval(el) for el in scale_vals] - - # if not valid_input: - # raise IOError('The length of scale_vals list is not divisible ' - # 'by the length of the parameter list') - - if not output_dir: - output_dir =\ - re.sub(r'\/|\\\\', '-', base_data_dir) + \ - '-modified-params-' + datetime.now().isoformat() - - os.mkdir(output_dir) - - s = ScenarioSeries( - base_data_dir, output_dir, title=title, description=description - ) - - def _scale_fun(val): - def scale(x): - return x * val - - return scale - - # assign scaling values to parameter names - # dimensionality correctness guaranteed from valid_input check - pval_len = len(scale_vals)/len(params) - - def _slice(idx): - return slice(pval_len*idx, pval_len*(idx+1)) - - param_val_dict = { - param: zip(itertools.repeat(param), scale_vals[idx]) - for idx, param in enumerate(params) - } - # param_val_dict = { - # param: zip(itertools.repeat(param, pval_len), scale_vals[_slice(idx)]) - # for idx, param in enumerate(params) - # } - # for every combination we need to iterate over every value of - # each param we have - combinations = itertools.product(*param_val_dict.values()) - - # now build the scenario_list with re-callable titles - scenario_list = [] - for combo in combinations: - - scenario_def = { - 'title': _build_title(combo) - } - scenario_def.update( - { - param: _scale_fun(val) - for param, val in combo - } - ) - - scenario_list.append(scenario_def) - - s.build(scenario_list) - - if run_prms: - s.run(prms_exec=prms_exec, nproc=nproc) - - if analyze_output: - print('\n** Sorry, analyze_output has not yet been implemented! **\n') - - -@prmspy.command() -@click.argument('data_dir', nargs=1) -@click.argument('output_pdf_path', nargs=1) -def nash_sutcliffe_matrix(data_dir, output_pdf_path): - 'Save a PDF of the Nash-Sutcliffe created from to ' - - observed = load_data( - os.path.join(data_dir, 'base_inputs', 'data') - ).runoff_1 - - series_metadata = json.loads( - io.open(os.path.join(data_dir, 'series_metadata.json')).read() - ) - modeled_flows = { - - title: load_statvar( - os.path.join(data_dir, uu, 'outputs', 'statvar.dat') - ).basin_cfs_1 - - for uu, title in series_metadata['uuid_title_map'].items() - } - - titles = list(modeled_flows.keys()) - - params = [pair.split(':')[0] for pair in titles[0].split('|')] - - if len(params) > 2: - print('This command is only supported for two covarying parameters!') - exit(1) - - p1_vals = sorted({ - float(pair[0].split(':')[1]) - for pair in map(lambda t: t.split('|'), titles) - }) - - p2_vals = sorted({ - float(pair[1].split(':')[1]) - for pair in map(lambda t: t.split('|'), titles) - }) - - print(p1_vals) - - nash_sutcliffe_mat = np.zeros((len(p1_vals), len(p2_vals))) - - for p1_idx, p1_val in enumerate(p1_vals): - for p2_idx, p2_val in enumerate(p2_vals): - - coord = (p1_idx, p2_idx) - title = '{p1}:{p1val:.3f}|{p2}:{p2val:.3f}'.format( - p1=params[0], p2=params[1], p1val=p1_val, p2val=p2_val - ) - modeled = modeled_flows[title] - - nash_sutcliffe_mat[coord] = nash_sutcliffe(observed, modeled) - - with PdfPages(output_pdf_path) as pdf: - - fig, ax = plt.subplots() - - cax = ax.matshow(nash_sutcliffe_mat, cmap='viridis') - - ytix = p1_vals - xtix = p2_vals - plt.yticks(range(len(ytix)), ytix) - plt.xticks(range(len(xtix)), xtix) - - ax.xaxis.set_ticks_position('bottom') - - plt.ylabel(params[0].replace('"', '')) - plt.xlabel(params[1].replace('"', '')) - - for i, j in itertools.product(range(len(ytix)), range(len(xtix))): - plt.text(j, i, '%.2f' % nash_sutcliffe_mat[i, j], - horizontalalignment='center', - color='w' - if nash_sutcliffe_mat[i, j] < - np.mean(nash_sutcliffe_mat.flatten()) - else 'k') - - plt.title('Nash-Sutcliffe Matrix') - - plt.grid(b=False) - fig.colorbar(cax) - - pdf.savefig() - plt.close() - - -def _build_title(combo): - ''' - Given a list of tuples with parameter/scale_val pairs, build the title - for the parameterization. For example, - >>> combo = [('snow_adj', 0.8), ('rad_trncf', 0.9), ('jh_coef', 1.1)] - >>> assert _build_title(combo) ==\ - '"snow_adj":0.800|"rad_trncf":0.900|"jh_coef":1.100' - ''' - - return '|'.join('"{0}":{1:.3f}'.format(param, val) for param, val in combo) diff --git a/build/lib/prms_python/simulation.py b/build/lib/prms_python/simulation.py deleted file mode 100644 index 7776198..0000000 --- a/build/lib/prms_python/simulation.py +++ /dev/null @@ -1,411 +0,0 @@ -# -*- coding: utf-8 -*- -""" -simulation.py -- Contains ``Simulation`` and ``SimulationSeries`` classes and -associated functions for managing PRMS simulations at a low level. -""" - -from __future__ import print_function -import glob -import multiprocessing as mp -import os -import shutil -import subprocess -import time - -from .data import Data -from .parameters import Parameters -from .util import load_statvar - -OPJ = os.path.join - -class SimulationSeries(object): - ''' - Series of simulations all to be run through a common interface. - - Utilizes :class:`multiprocessing.Pool` class to parallelize the - execution of series of PRMS simulations. SimulationSeries also - allows the user to define the PRMS executable command which is - set to "prms" as default. It is best to add the prms executable to - your $PATH environment variable. Each simulation that is run through - ``SimulationSeries`` will follow the strict file structure as defined - by :func:`Simulation.run()`. This class is useful particularly for - creating new programatic workflows not provided yet by PRMS-Python. - - Arguments: - simulations (list or tuple): list of :class:`Simulation` objects - to be run. - - Example: - Lets say you have already created a series of PRMS models by modifying - the input climatic forcing data, e.g. you have 100 *data* files and - you want to run each using the same *control* and *parameters* file. - For simplicity lets say there is a directory that contains all 100 - *data* files e.g. data1, data2, ... or whatever they are named and - nothing else. This example also assumes that you want each simulation - to be run and stored in directories named after the *data* files as - shown. - - >>> data_dir = 'dir_that_contains_all_data_files' - >>> params = Parameters('path_to_parameter_file') - >>> control_path = 'path_to_control' - >>> # a list comprehension to make multiple simulations with - >>> # different data files, alternatively you could use a for loop - >>> sims = [ - Simulation.from_data - ( - Data(data_file), - params, - control_path, - simulation_dir='sim_{}'.format(data_file) - ) - for data_file in os.listdir(data_dir) - ] - - Next we can use ``SimulationSeries`` to run all of these - simulations in parrallel. For example we may use 8 logical cores - on a common desktop computer. - - >>> sim_series = SimulationSeries(sims) - >>> sim_series.run(nprocs=8) - - The ``SimulationSeries.run()`` method will run all 100 simulations - where chunks of 8 at a time will be run in parrallel. Inputs and - outputs of each simulation will be sent to each simulation's - ``simulation_dir`` following the file structure of - :func:`Simulation.run()`. - - Note: - The :class:`Simulation` and :class:`SimulationSeries` classes - are low-level in that they alone do not create metadata for - PRMS simulation scenarios. In other words they do not produce - any additional files that may help the user know what differs - between individual simulations. - - ''' - - def __init__(self, simulations): - self.series = list(simulations) - - def run(self, prms_exec='prms', nproc=None): - """ - Method to run multiple :class:`Simulation` objects in parrallel. - - Keyword Arguments: - prms_exec (str): name of PRMS executable on $PATH or path to - executable - nproc (int or None): number of logical or physical processors - for parrallel execution of PRMS simulations. - - Example: - see :class:`SimulationSeries` - - Note: - If ``nproc`` is not assigned the deault action is to use half - of the available processecors on the machine using the Python - :mod:`multiprocessing` module. - - """ - if not nproc: - nproc = mp.cpu_count() // 2 - - pool = mp.Pool(processes=nproc) - pool.map(_simulation_runner, self.series) - pool.close() - - return self - - def outputs_iter(self): - ''' - Return a :class:`generator` of directories with the path to the - ``simulation_dir`` as well as paths to the *statvar.dat* output - file, and *data* and *parameters* input files used in the simulation. - - Yields: - :obj:`dict`: dictionary of paths to simulation directory, - input, and output files. - - Example: - >>> ser = SimulationSeries(simulations) - >>> ser.run() - >>> g = ser.outputs_iter() - - Would return something like - - >>> print(g.next()) - { - 'simulation_dir': 'path/to/sim/', - 'statvar': 'path/to/statvar', - 'data': 'path/to/data', - 'parameters': 'path/to/parameters' - } - ''' - dirs = list(s.simulation_dir for s in self.series) - print(dirs) - - return ( - { - 'simulation_dir': d, - 'statvar': OPJ(d, 'outputs', 'statvar.dat'), - 'data': OPJ(d, 'inputs', 'data'), - 'parameters': OPJ(d, 'inputs', 'parameters') - } - for d in dirs - ) - - def __len__(self): - return len(list(self.outputs_iter())) - - -def _simulation_runner(sim): - sim.run(prms_exec='prms') - - -class Simulation(object): - """ - Class that runs and manages file structure for a single PRMS simulation. - - The ``Simulation`` class provides low-level managment of a PRMS simulation - by copying model input files from ``input_dir`` argument to an output dir - ``simulation_dir``. The file stucture for an individual simulation after - calling the ``run`` method is simple, two subdirectories "inputs" and - "outputs" are created under ``simulation_dir`` and the respective input - and output files from the current PRMS simulation are transfered there after - the ``Simulation.run()`` method is called which executes the PRMS model, - (see examples below in :func:`Simulation.run`). - - A ``Simulation`` instance checks that all required PRMS inputs (control, - parameters, data) exist in the expected locations. If simulation_dir is - provided and does not exist, it will be created. If it does exist it will - be overwritten. - - Keyword Arguments: - input_dir (str): path to directory that contains control, parameter, - and data files for the simulation - simulation_dir (str): directory path to bundle inputs and outputs - - Example: - see :func:`Simulation.run()` - - Raises: - RuntimeError: if input directory does not contain a PRMS *data*, - *parameters*, and *control* file. - - """ - def __init__(self, input_dir=None, simulation_dir=None): - # check if model input paths exist - idir = input_dir - self.input_dir = idir - self.simulation_dir = simulation_dir - if idir is not None: - self.control_path = os.path.join(idir, 'control') - self.parameters_path = os.path.join(idir, 'parameters') - self.data_path = os.path.join(idir, 'data') - - if not os.path.exists(self.control_path): - raise RuntimeError('Control file missing from ' + idir) - - if not os.path.exists(self.parameters_path): - raise RuntimeError('Parameter file missing from ' + idir) - - if not os.path.exists(self.data_path): - raise RuntimeError('Data file missing from ' + idir) - # build output (simulation_dir) and move input files there - if simulation_dir is not None: - self.simulation_dir = simulation_dir - if simulation_dir and simulation_dir != input_dir: - - if os.path.exists(simulation_dir): - shutil.rmtree(simulation_dir) - - os.mkdir(simulation_dir) - - shutil.copy(self.control_path, simulation_dir) - shutil.copy(self.data_path, simulation_dir) - shutil.copy(self.parameters_path, simulation_dir) - - self.control_path = os.path.join(simulation_dir, 'control') - self.parameters_path = os.path.join(simulation_dir, - 'parameters') - self.data_path = os.path.join(simulation_dir, 'data') - - else: - self.control_path = None - self.parameters_path = None - self.data_path = None - self.simulation_dir = None - - self.has_run = False - - @classmethod - def from_data(cls, data, parameters, control_path, simulation_dir): - ''' - Create a ``Simulation`` from a :class:`Data` and :class:`Parameter` object, - plus a path to the *control* file, and providing a ``simulation_dir`` - where the simulation should be run. - - Arguments: - data (:class:`Data`): ``Data`` object for simulation - parameters (:class:`Parameters`): ``Parameters`` object for simulation - control_path (str): path to control file - simulation_dir (str): path to directory where simulations will be - run and where input and output will be stored. If it exists it will - be overwritten. - - Returns: - :class:`Simulation` ready to be run using ``simulation_dir`` for - inputs and outputs - - Example: - - >>> d = Data('path_to_data_file') - >>> p = Parameters('path_to_parameters_file') - >>> c = 'path_to_control_file' - >>> sim_dir = 'path_to_create_simulation' - >>> sim = Simulation.from_data(d, p, c, sim_dir) - >>> sim.run() - - Raises: - TypeError: if ``data`` and ``parameters`` arguments are not of type - :class:`Data` and :class:`Parameters` - ''' - - if not isinstance(data, Data): - raise TypeError('data must be instance of Data') - - if not isinstance(parameters, Parameters): - raise TypeError('parameters must be instance of Parameters, not '\ - + str(type(parameters))) - - if os.path.exists(simulation_dir): - shutil.rmtree(simulation_dir) - - os.makedirs(simulation_dir) - - sim = cls() - sim.simulation_dir = simulation_dir - - sd = simulation_dir - - data_path = OPJ(sd, 'data') - data.write(data_path) - params_path = OPJ(sd, 'parameters') - parameters.write(params_path) - shutil.copy(control_path, OPJ(sd, 'control')) - - return sim - - def run(self, prms_exec='prms'): - """ - Run a ``Simulation`` instance using PRMS input files from ``input_dir`` - and copy to the ``Simulation`` file structure under ``simulation_dir`` if - given, otherwise leave PRMS input output unstructured and in ``input_dir`` - - This method runs a single PRMS simulation from a ``Simulation`` instance, - waits until the process has completed and then transfers model input and - output files to respective newly created directories. See example of the - file structure that is created under different workflows of the ``run`` - method below. - - Keyword Arguments: - prms_exec (str): name of PRMS executable on $PATH or path to executable - - Examples: - If we create a :class:`Simulation` instance by only assigning the - ``input_dir`` argument and call its ``run`` method the model will be - run in the ``input_dir`` and all model input and output files will - remain in ``input_dir``, - - >>> import os - >>> input_dir = os.path.join( - 'PRMS-Python', - 'prms_python', - 'models', - 'lbcd' - ) - >>> os.listdir(input_dir) - ['data', - 'data_3deg_upshift', - 'parameters', - 'parameters_adjusted', - 'control'] - >>> sim = Simulation(input_dir) - >>> sim.run() - >>> os.listdir(input_dir) # all input and outputs in input_dir - ['data', - 'data_3deg_upshift', - 'parameters', - 'parameters_adjusted', - 'control', - 'statvar.dat', - 'prms_ic.out', - 'prms.out' ] - - Instead if we assigned a path for ``simulation_dir`` keyword - argument and then called ``run``, i.e. - - >>> sim = Simulation(input_dir, 'path_simulation') - >>> sim.run() - - the files structure for the PRMS simulation created by ``Simulation.run()`` - would be:: - - path_simulation - ├── inputs - │   ├── control - │   ├── data - │   └── parameters - └── outputs - ├── data_3deg_upshift - ├── parameters_adjusted - ├── prms_ic.out - ├── prms.out - └── statvar.dat - - Note: - As shown in the last example, currently the ``Simulation.run`` routine only - recognizes the *data*, *parameters*. and *control* file as PRMS inputs, - all other files found in ``input_dir`` before *and* after normal completion - of the PRMS simulation will be transferred to ``simulation_dir/outputs/``. - """ - cwd = os.getcwd() - - if self.simulation_dir: - os.chdir(self.simulation_dir) - - else: - os.chdir(self.input_dir) - - p = subprocess.Popen( - prms_exec + ' control', shell=True, stdout=subprocess.PIPE, - stderr=subprocess.PIPE - ) - - prms_finished = False - checked_once = False - while not prms_finished: - - if not checked_once: - p.communicate() - checked_once = True - - poll = p.poll() - prms_finished = poll >= 0 - - self.has_run = True - # avoid too many files open error - p.stdout.close() - p.stderr.close() - - if self.simulation_dir: - os.mkdir('inputs') - os.mkdir('outputs') - shutil.move('data', 'inputs') - shutil.move('parameters', 'inputs') - shutil.move('control', 'inputs') - - # all remaining files are outputs - for g in glob.glob('*'): - if not os.path.isdir(g): - shutil.move(g, 'outputs') - - os.chdir(cwd) diff --git a/build/lib/prms_python/util.py b/build/lib/prms_python/util.py deleted file mode 100644 index c7fa14b..0000000 --- a/build/lib/prms_python/util.py +++ /dev/null @@ -1,346 +0,0 @@ -# -*- coding: utf-8 -*- -""" -util.py -- Utilities for working with PRMS data or other functionality that aren't -appropriate to put elsewhere at this time. -""" -import warnings -import os, shutil, json -import numpy as np -import pandas as pd - -def calc_emp_CDF(data): - # changed function name for PEP 8 style - warnings.warn("calc_emp_CDF is deprecated, please use "+\ - "util.calc_emp_cdf instead", DeprecationWarning) - return calc_emp_cdf(data) - -def calc_emp_cdf(data): - """ - Create empirical CDF of arbitrary data - - Arguments: - data (array_like) : array to calculate CDF on - - Returns: - X (numpy.ndarray) : array of x values of CDF (sorted data) - - F (numpy.ndarray) : array of CDF values for each X value or cumulative - exceedence probability, in [0,1]. - """ - n_bins = len(data) - X = np.sort(data) - F = np.array(range(n_bins))/float(n_bins) - return X,F - -def Kolmogorov_Smirnov(uncond, cond, n_bins=10000): - # changed function name for PEP 8 style - warnings.warn("Kolmogorov_Smirnov is deprecated, please use "+\ - "util.komogorov_smirnov instead", DeprecationWarning) - return kolmogorov_smirnov(uncond, cond, n_bins=10000) - -def kolmogorov_smirnov(uncond, cond, n_bins=10000): - """ - Calculate the Kolmogorov-Smirnov statistic between two datasets by first - computing their empirical CDFs - - Arguments: - uncond (array_like) : data for creating the unconditional CDF. - cond (array_like) : data for creating the conditional CDF - n_bins (int) : number of bins for both CDFs, note if n_bins > length - of either dataset then CDF values are interpolated by numpy - - Returns: - KS (float) : Kolmogorov-Smirnov statistic, i.e. absolute max distance - between uncond and cond CDFs - """ - # create unconditional CDF (F_Uc) - H,X = np.histogram(uncond, bins=n_bins, normed=True) - dx = X[1] - X[0] - F_Uc = np.cumsum(H)*dx - # create conditional CDF (F_C) - H,X = np.histogram(cond, bins=n_bins, normed=True) - dx = X[1] - X[0] - F_C = np.cumsum(H)*dx - # Calc max absolulte divergence - KS = np.max(np.abs(F_Uc - F_C)) - return KS - -def remove_all_optimization_sims_of_other_stage(work_directory, stage): - """ - Track number of simulation directories not tracked by a specific stage - and recursively delete them and their contents. This was created to avoid - having nutracked simulations in an optimizer working directory for example - when an optimization method was interupted before data was saved to a meta - data file. - - Arguments: - work_directory (str) : Directory to look for Optimization metadata - json files and simulation directories to keep or remove. - stage (str) : Optimization stage that will not have its simulation - data deleted. All other stages if any are found in metadata files - will have their associated simulation directories deleted. - Returns: - None - """ - from .optimizer import OptimizationResult # avoid circular import - - try: - result = OptimizationResult(work_directory,stage=stage) - tracked_dirs = [] - for f in result.metadata_json_paths[stage]: - with open(f) as fh: - json_data = json.load(fh) - tracked_dirs.extend(json_data.get('sim_dirs')) - count = 0 - for d in os.listdir(result.working_dir): - path = os.path.join(result.working_dir, d) - if path in tracked_dirs: - continue - elif os.path.isdir(path) and '_archived' not in path: - count+=1 - for dirpath, dirnames, filenames in os.walk(path,\ - topdown=False): - shutil.rmtree(dirpath, ignore_errors=True) - - # if no json file in working dir for given stage, delete any other sim dirs - except: - count = 0 - for d in os.listdir(work_directory): - path = os.path.join(work_directory, d) - if os.path.isdir(path) and '_archived' not in path: - count+=1 - for dirpath, dirnames, filenames in os.walk(path,\ - topdown=False): - shutil.rmtree(dirpath, ignore_errors=True) - - print('deleted {} simulations that were either not tracked by a JSON file'\ - .format(count) + ' or were not part of {} optimization stage'\ - .format(stage)) - - -def delete_files(work_directory, file_name=''): - """ - Recursively delete all files of a certain name from multiple PRMS - simulations that are within a given directory. Can be useful to removw - large files that are no longer needed. For example initial condition - output files are often large and not always used, similarly animation, - data, control, ... files may no longer be needed. - - Arguments: - work_directory (str) : path to directory with simulations. - file_name (str) : Name of the PRMS input or output file(s) to be - removed, default = '' empty string- nothing will be deleted. - - Example: - e.g. if you have several simulation directories: - - >>> "test/results/intcp:-26.50_slope:0.49", - "test/results/intcp:-11.68_slope:0.54", - "test/results/intcp:-4.70_slope:0.51", - "test/results/intcp:-35.39_slope:0.39", - "test/results/intcp:-20.91_slope:0.41" - - each of these contains an '/inputs' folder with a duplicate data - file that you would like to delete. In this case, delete all - data files like so: - - >>> work_dir = 'test/results/' - >>> delete_files(work_dir, file_name='data') - - Returns: - None - """ - for dirpath, dirnames, filenames in os.walk(work_directory, topdown=False): - paths = (os.path.join(dirpath, filename) for filename in filenames\ - if filename == file_name) - for path in paths: - os.remove(path) - -def load_statvar(statvar_file): - """ - Read the statvar file and load into a datetime indexed - Pandas dataframe object - - Arguments: - statvar_file (str): statvar file path - Returns: - (pandas.DataFrame) Pandas DataFrame of PRMS variables date indexed - from statvar file - """ - # make list of statistical output variables for df header - column_list = ['index', - 'year', - 'month', - 'day', - 'hh', - 'mm', - 'sec'] - - # append to header list the variables present in the file - with open(statvar_file, 'r') as inf: - for idx, l in enumerate(inf): - # first line is always number of stat variables - if idx == 0: - n_statvars = int(l) - elif idx <= n_statvars and idx != 0: - column_list.append(l.rstrip().replace(' ', '_')) - else: - break - - # arguments for read_csv function - missing_value = -999 - skiprows = n_statvars+1 - df = pd.read_csv( - statvar_file, delim_whitespace=True, skiprows=skiprows, - header=-1, na_values=[missing_value] - ) - - # apply correct header names using metadata retrieved from file - df.columns = column_list - date = pd.Series( - pd.to_datetime(df.year*10000+df.month*100+df.day, format='%Y%m%d'), - index=df.index - ) - - # make the df index the datetime for the time series data - df.index = pd.to_datetime(date) - - # drop unneeded columns - df.drop(['index', 'year', 'month', 'day', 'hh', 'mm', 'sec'], - axis=1, inplace=True) - - # name dataframe axes (index,columns) - df.columns.name = 'statistical_variables' - df.index.name = 'date' - - return df - - -def load_data_file(data_file): - # changed function name for PEP 8 style - warnings.warn("load_data_file is deprecated, please use "+\ - "util.load_data instead", DeprecationWarning) - return load_data(data_file) - -def load_data(data_file): - """ - Read the data file and load into a datetime indexed Pandas dataframe object. - - Arguments: - data_file (str): data file path - Returns: - df (pandas.DataFrame): Pandas dataframe of input time series data - from data file with datetime index - """ - # valid input time series that can be put into a data file - valid_input_variables = ('gate_ht', - 'humidity', - 'lake_elev', - 'pan_evap', - 'precip', - 'rain_day', - 'runoff', - 'snowdepth', - 'solrad', - 'tmax', - 'tmin', - 'wind_speed') - # starting list of names for header in dataframe - column_list = ['year', - 'month', - 'day', - 'hh', - 'mm', - 'sec'] - # append to header list the variables present in the file - with open(data_file, 'r') as inf: - for idx, l in enumerate(inf): - - # first line always string identifier of the file- may use later - if idx == 0: - data_head = l.rstrip() - - elif l.startswith('/'): # comment lines - continue - - # header lines with name and number of input variables - if l.startswith(valid_input_variables): - # split line into list, first element name and - # second number of columns - h = l.split() - - # more than one input time series of a particular variable - if int(h[1]) > 1: - for el in range(int(h[1])): - tmp = '{var_name}_{var_ind}'.format(var_name=h[0], - var_ind=el+1) - column_list.append(tmp) - elif int(h[1]) == 1: - column_list.append(h[0]) - # end of header info and begin time series input data - if l.startswith('#'): - skip_line = idx+1 - break - - # read data file into pandas dataframe object with correct header names - missing_value = -999 # missing data representation - df = pd.read_csv(data_file, header=-1, skiprows=skip_line, - delim_whitespace=True, na_values=[missing_value]) - - # apply correct header names using metadata retrieved from file - df.columns = column_list - - # create date column - date = pd.Series( - pd.to_datetime(df.year*10000+df.month*100+df.day, format='%Y%m%d'), - index=df.index - ) - - df.index = pd.to_datetime(date) # make the df index the datetime - - # drop unneeded columns - df.drop(['year', 'month', 'day', 'hh', 'mm', 'sec'], axis=1, inplace=True) - df.columns.name = 'input variables' - df.index.name = 'date' # name dataframe axes (index,columns) - return df - - -def nash_sutcliffe(observed, modeled): - """ - Calculates the Nash-Sutcliffe Goodness-of-fit - - Arguments: - observed (numpy.ndarray): historic observational data - - modeled (numpy.ndarray): model output with matching time index - """ - numerator = sum((observed - modeled)**2) - denominator = sum((observed - np.mean(observed))**2) - - return 1 - (numerator/denominator) - -def percent_bias(observed, modeled): - """ - Calculates percent bias - - Arguments: - observed (numpy.ndarray): historic observational data - - modeled (numpy.ndarray): model output with matching time index - """ - return 100 * ( sum( modeled - observed ) / sum( observed ) ) - - -def rmse(observed, modeled): - """ - Calculates root mean squared error - - Arguments: - observed (numpy.ndarray): historic observational data - - modeled (numpy.ndarray): model output with matching time index - """ - return np.sqrt( sum((observed - modeled)**2) / len(observed) ) - - - diff --git a/build/lib/test/test_prms_python.py b/build/lib/test/test_prms_python.py deleted file mode 100644 index 0cce97b..0000000 --- a/build/lib/test/test_prms_python.py +++ /dev/null @@ -1,476 +0,0 @@ -from copy import copy -import json -import glob -import numpy as np -import os -import re -import shutil -import unittest - -from difflib import Differ -from numpy.testing import assert_array_almost_equal - -from prms_python import ( - modify_params, Parameters, Scenario, ScenarioSeries, Simulation, - SimulationSeries, Data -) - - -OPJ = os.path.join - - -class TestSimulationSeries(unittest.TestCase): - ''' - Expect that a SimulationSeries - ''' - def setUp(self): - self.test_model_data_dir = os.path.join( - 'test', 'data', 'models', 'lbcd' - ) - - self.simulation_dir = os.path.join(self.test_model_data_dir, 'tmp_sim') - - def tearDown(self): - for g in glob.glob(OPJ(self.simulation_dir, '*')): - shutil.rmtree(g) - - def test_simulation_series(self): - tdd = self.test_model_data_dir - data = Data(OPJ(tdd, 'data')) - base_parameters = Parameters(OPJ(tdd, 'parameters')) - - def _copy_mod(base_parameters, val): - ret = copy(base_parameters) - ret['dday_intcp'][:] = val - - return ret - - parameters_gen = ( - (val, _copy_mod(base_parameters, val)) - for val in (-50, -40, -30) - ) - control_path = OPJ(tdd, 'control') - - series = SimulationSeries( - Simulation.from_data( - data, parameters, control_path, - self.simulation_dir + str(val) - ) - for val, parameters in parameters_gen - ) - - outputs = list(series.run().outputs_iter()) - - self.assertEqual(len(series), 3) - self.assertEqual(len(outputs), 3) - - for out in outputs: - sdir = out['simulation_dir'] - assert os.path.isdir(out['simulation_dir']) - assert os.path.exists(OPJ(sdir, 'outputs', 'statvar.dat')) - assert os.path.exists(OPJ(sdir, 'inputs', 'data')) - assert os.path.exists(OPJ(sdir, 'inputs', 'parameters')) - - shutil.rmtree(sdir) - - -class TestSimulation(unittest.TestCase): - """ - Simulations should take a base directory and return a simulation directory - """ - - def setUp(self): - - self.test_data_dir = os.path.join('test', 'data') - - self.test_model_data_dir = os.path.join( - 'test', 'data', 'models', 'lbcd' - ) - - self.simulation_dir = os.path.join(self.test_data_dir, 'tmp_sim') - - def tearDown(self): - - if os.path.exists(self.simulation_dir): - shutil.rmtree(self.simulation_dir) - - def test_simulation_no_simdir(self): - "Simulation should run and write outputs to input directory when simulation_dir is not specified" - s = Simulation(self.test_model_data_dir) - s.run() - - g = [ - os.path.basename(f) - for f in glob.glob(os.path.join(self.test_model_data_dir, '*')) - ] - - self.assertIn('prms_ic.out', g) - self.assertIn('prms.out', g) - self.assertIn('statvar.dat', g) - - def test_simulation_w_simdir(self): - "Simulation should create sim dir with inputs and outputs directory when simulation_dir is specified" - s = Simulation(self.test_model_data_dir, self.simulation_dir) - s.run() - - gs = [ - os.path.basename(f) - for f in glob.glob(os.path.join(self.simulation_dir, '*')) - ] - self.assertIn('inputs', gs) - self.assertIn('outputs', gs) - - assert_valid_input_dir( - self, os.path.join(self.simulation_dir, 'inputs') - ) - assert_valid_output_dir( - self, os.path.join(self.simulation_dir, 'outputs') - ) - - def test_simulation_from_data(self): - """ - Use @classmethod from_data to build a simulation from Parameters and Data instances - """ - tdd = self.test_model_data_dir - - data = Data(OPJ(tdd, 'data')) - parameters = Parameters(OPJ(tdd, 'parameters')) - ctrl = OPJ(tdd, 'control') - - test_dir = OPJ(tdd, 'test-sim-dir') - if os.path.isdir(test_dir): - shutil.rmtree(test_dir) - - s = Simulation.from_data(data, parameters, ctrl, test_dir) - - s.run() - - g = [ - os.path.basename(f) - for f in glob.glob(OPJ(test_dir, 'outputs', '*')) - ] - - self.assertIn('prms_ic.out', g) - self.assertIn('prms.out', g) - self.assertIn('statvar.dat', g) - - # clean up - shutil.rmtree(test_dir) - - -class TestScenario(unittest.TestCase): - - def setUp(self): - - self.test_data_dir = os.path.join('test', 'data') - - self.test_model_data_dir = os.path.join( - 'test', 'data', 'models', 'lbcd' - ) - - self.scenario_dir = os.path.join(self.test_data_dir, 'tmp_scenario') - - def tearDown(self): - - if os.path.exists(self.scenario_dir): - shutil.rmtree(self.scenario_dir) - - def test_create_scenario(self): - """ - a simulation setup should create a simulation directory with correct scenario data - """ - - s = Scenario( - self.test_model_data_dir, self.scenario_dir, - title='Scenario Uno', description='test scenario for prms_python' - ) - - param_mods = { - 'snow_adj': lambda x: 1.1*x, - 'rad_trncf': lambda x: 0.9*x - } - s.build(param_mod_funs=param_mods) - - assert_valid_input_dir(self, self.scenario_dir) # os.path.join(self.scenario_dir, 'inputs')) - - s.run() - - assert_valid_input_dir( - self, os.path.join(self.scenario_dir, 'inputs') - ) - assert_valid_output_dir( - self, os.path.join(self.scenario_dir, 'outputs') - ) - - md_json_path = os.path.join(self.scenario_dir, 'metadata.json') - assert os.path.isfile(md_json_path) - - md_json = json.loads(open(md_json_path).read()) - assert md_json['title'] == 'Scenario Uno' - assert md_json['description'] == 'test scenario for prms_python' - assert 'start_datetime' in md_json - assert 'end_datetime' in md_json - assert 'mod_funs_dict' in md_json - - p_base = Parameters( - os.path.join(self.test_model_data_dir, 'parameters') - ) - p_scen = Parameters( - os.path.join(self.scenario_dir, 'inputs', 'parameters') - ) - - assert_array_almost_equal(p_base['snow_adj']*1.1, p_scen['snow_adj']) - assert_array_almost_equal(p_base['rad_trncf']*0.9, p_scen['rad_trncf']) - - -class TestScenarios(unittest.TestCase): - - def setUp(self): - - self.test_data_dir = os.path.join('test', 'data') - - self.test_model_data_dir = os.path.join( - self.test_data_dir, 'models', 'lbcd' - ) - - self.scenarios_dir = os.path.join(self.test_data_dir, 'tmp_scenarios') - - def tearDown(self): - - if os.path.exists(self.scenarios_dir): - shutil.rmtree(self.scenarios_dir) - - def test_scenario_series(self): - "create_many_simulations should create many simulation directories and correct data" - - s = ScenarioSeries( - self.test_model_data_dir, self.scenarios_dir, - title='scenario series uno', - description=''' -Each scenario is given a title with the schema -'"":|"":', which can be -easily parsed into a Python dictionary later. We use the pipe instead of -comma for easier visual inspection, though this is entirely up to the -user/developer. The scenario consists of parameters scaled for each of the -specified parameters. -''' - ) - - def _scale_param(val): - def scale_by_val(x): - return x*val - return scale_by_val - - scale_arange = np.arange(0.7, 0.9, 0.1) - series_funs = [ - { - 'title': '"rad_trncf":{0:.1f}|"snow_adj":{0:.1f}'.format(val), - 'rad_trncf': _scale_param(val), - 'snow_adj': _scale_param(val) - } - for val in scale_arange - ] - - s.build(series_funs) - - g_series = glob.glob(os.path.join(self.scenarios_dir, '*')) - - # this should be 5 because of series_metadata.json and inputs_dir - assert len(g_series) == 5, g_series - - uuid_pattern = re.compile( - r'^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$' - ) - - uuid_dir_count = 0 - non_uuid_dir_count = 0 - series_dirs = [] - found_metadata = False - for d in g_series: - # XXX TODO this is in flux. at this point maybe all will be dirs - if os.path.isdir(d): - # at this point the files are in the base scenario dir; - # they will be moved later when the scenario is run - assert_valid_input_dir(self, os.path.join(d)) - if re.match(uuid_pattern, os.path.basename(d)): - uuid_dir_count += 1 - assert re.match(uuid_pattern, os.path.basename(d)), os.path.basename(d) - else: - non_uuid_dir_count += 1 - - else: - found_metadata = True - - series_dirs.append(d) - - assert uuid_dir_count == 3 - assert non_uuid_dir_count == 1 - assert found_metadata - - s.run() - - g_series = glob.glob(os.path.join(self.scenarios_dir, '*')) - - series_md_path = os.path.join( - self.scenarios_dir, 'series_metadata.json' - ) - self.assertIn(series_md_path, g_series) - - series_md = json.loads(open(series_md_path).read()) - dir_titles = series_md['uuid_title_map'] - titles = dir_titles.values() - - assert '"rad_trncf":0.7|"snow_adj":0.7' in titles - assert '"rad_trncf":0.8|"snow_adj":0.8' in titles - assert '"rad_trncf":0.9|"snow_adj":0.9' in titles - - p_base = Parameters( - os.path.join(self.test_model_data_dir, 'parameters') - ) - for g in g_series: - if os.path.isdir(g) and 'input' not in g: - assert_valid_output_dir(self, os.path.join(g, 'outputs')) - - md = json.loads(open(os.path.join(g, 'metadata.json')).read()) - - title = md['title'] - scale_vals = eval('{' + title.replace('|', ',') + '}') - - p_scen = Parameters(os.path.join(g, 'inputs', 'parameters')) - - rad_base = p_base['rad_trncf'] - rad_scen = p_scen['rad_trncf'] - - snow_base = p_base['snow_adj'] - snow_scen = p_scen['snow_adj'] - - assert_array_almost_equal( - rad_base*scale_vals['rad_trncf'], rad_scen) - assert_array_almost_equal( - snow_base*scale_vals['snow_adj'], snow_scen) - - -def assert_valid_input_dir(test_case, d): - - gs = [ - os.path.basename(f) - for f in glob.glob(os.path.join(d, '*')) - ] - - test_case.assertIn('control', gs, d) - test_case.assertIn('parameters', gs, d) - test_case.assertIn('data', gs, d) - - -def assert_valid_output_dir(test_case, d): - - go = [ - os.path.basename(f) - for f in - glob.glob(os.path.join(d, '*')) - ] - test_case.assertIn('prms_ic.out', go) - test_case.assertIn('prms.out', go) - test_case.assertIn('statvar.dat', go) - - -class TestParameters(unittest.TestCase): - - def setUp(self): - - self.test_data_dir = os.path.join('test', 'data') - self.test_param = os.path.join(self.test_data_dir, 'parameters') - self.temp_dir = os.path.join('test', 'data', 'tmp') - if os.path.isdir(self.temp_dir): - shutil.rmtree(self.temp_dir) - - os.mkdir(self.temp_dir) - - def tearDown(self): - - if os.path.isdir(self.temp_dir): - shutil.rmtree(self.temp_dir) - - def test_faithful_copy(self): - """ - Parameters class should load and write identical file - """ - test_copy = os.path.join(self.temp_dir, 'param_copy_test') - p = Parameters(self.test_param) - p.write(test_copy) - - expected = open(self.test_param) - generated = open(test_copy) - - # ignore first two lines ignore now date written on Parameters.write - expected = expected.readlines()[2:] - generated = generated.readlines()[2:] - - assert expected == generated - - def test_modify_params(self): - "parameter and data file modification access should work as expected" - - # test parameters mod with 1d rad_trncf - params_out = os.path.join(self.temp_dir, 'modify_params_out') - - mod_d = {'rad_trncf': lambda x: 1.1*x} - modify_params(self.test_param, params_out, mod_d) - - generated = open(params_out).readlines()[2:] - - expected = open( - os.path.join( - self.test_data_dir, 'expected_rad_trncf_1.1' - ) - ).readlines()[2:] - - assert expected == generated - - # test parameters mod with 2d snow_adj - params_out = os.path.join(self.temp_dir, 'modify_params_out') - - mod_d = {'snow_adj': lambda x: 1.1*x} - modify_params(self.test_param, params_out, mod_d) - - generated = open(params_out).readlines()[2:] - - expected = open( - os.path.join( - self.test_data_dir, 'expected_snow_adj_1.1' - ) - ).readlines()[2:] - - for a, b in zip(expected, generated): - try: - a = float(a) - b = float(b) - self.assertAlmostEqual(a, b, places=5) - except ValueError: - assert a == b - - -def colored_string_diff(s1, s2): - """ Writes differences between strings s1 and s2 """ - d = Differ() - diff = d.compare(s1.splitlines(), s2.splitlines()) - diffList = [el for el in diff - if el[0] != ' ' and el[0] != '?'] - - for l in diffList: - - if l[0] == '+': - print(bcolors.GREEN + '+' + l[1:] + bcolors.ENDC) - elif l[0] == '-': - print(bcolors.RED + '-' + l[1:] + bcolors.ENDC) - else: - assert False, 'Error, diffList entry must start with + or -' - - -class bcolors: - HEADER = '\033[95m' - BLUE = '\033[94m' - GREEN = '\033[92m' - YELLOW = '\033[93m' - RED = '\033[91m' - ENDC = '\033[0m' From 88f12cc75c3e6682f81d3724fb5681e747481b1e Mon Sep 17 00:00:00 2001 From: John Volk Date: Wed, 13 Mar 2019 20:23:54 -0700 Subject: [PATCH 03/11] Add manuscript citation and link --- README.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index b236d6f..d694d51 100644 --- a/README.md +++ b/README.md @@ -120,10 +120,8 @@ nosetests -v We welcome anyone seriously interested in contributing to PRMS-Python to do so in anyway they see fit. If you are not sure where to begin you can look for current issues or submit a new issue [here](https://github.com/PRMS-Python/PRMS-Python/issues). You may also [fork](https://help.github.com/articles/fork-a-repo/) PRMS-Python and submit a pull request if you would like to offer your direct changes to the package. - +Volk, J. M., & Turner, M. A. (2019). PRMS-Python: A Python framework for programmatic PRMS modeling and access to its data structures. Environmental Modelling & Software, 114, 152–165. https://doi.org/10.1016/J.ENVSOFT.2019.01.006 From 77c43c4633ffe0809d63574b7302ac8eecc9f799 Mon Sep 17 00:00:00 2001 From: John Volk Date: Sun, 13 Oct 2019 12:17:05 -0700 Subject: [PATCH 04/11] update docs, add manifest.in, conda env --- MANIFEST.in | 3 + docs/build/doctrees/api.doctree | Bin 315624 -> 325175 bytes docs/build/doctrees/cli.doctree | Bin 17666 -> 18241 bytes docs/build/doctrees/environment.pickle | Bin 3609162 -> 5776634 bytes docs/build/doctrees/index.doctree | Bin 12093 -> 12360 bytes docs/build/doctrees/tutorial.doctree | Bin 80030 -> 81081 bytes docs/build/html/.buildinfo | 2 +- docs/build/html/_modules/index.html | 42 +- .../build/html/_modules/prms_python/data.html | 42 +- .../html/_modules/prms_python/optimizer.html | 42 +- .../html/_modules/prms_python/parameters.html | 42 +- .../html/_modules/prms_python/scenario.html | 52 +- .../build/html/_modules/prms_python/util.html | 46 +- docs/build/html/_sources/api.rst.txt | 4 + docs/build/html/_sources/index.rst.txt | 37 +- docs/build/html/_sources/tutorial.rst.txt | 11 +- docs/build/html/_static/ajax-loader.gif | Bin 673 -> 0 bytes docs/build/html/_static/alabaster.css | 13 + docs/build/html/_static/basic.css | 103 +- docs/build/html/_static/comment-bright.png | Bin 756 -> 0 bytes docs/build/html/_static/comment-close.png | Bin 829 -> 0 bytes docs/build/html/_static/comment.png | Bin 641 -> 0 bytes docs/build/html/_static/custom.css | 22 +- docs/build/html/_static/doctools.js | 13 +- .../html/_static/documentation_options.js | 5 +- docs/build/html/_static/down-pressed.png | Bin 222 -> 0 bytes docs/build/html/_static/down.png | Bin 202 -> 0 bytes docs/build/html/_static/jquery-3.1.0.js | 10074 --------------- docs/build/html/_static/jquery-3.2.1.js | 10253 ---------------- docs/build/html/_static/jquery.js | 6 +- docs/build/html/_static/searchtools.js | 401 +- docs/build/html/_static/up-pressed.png | Bin 214 -> 0 bytes docs/build/html/_static/up.png | Bin 203 -> 0 bytes docs/build/html/_static/websupport.js | 808 -- docs/build/html/api.html | 1136 +- docs/build/html/cli.html | 44 +- docs/build/html/genindex.html | 54 +- docs/build/html/index.html | 71 +- docs/build/html/objects.inv | Bin 1345 -> 1387 bytes docs/build/html/search.html | 45 +- docs/build/html/searchindex.js | 2 +- docs/build/html/tutorial.html | 62 +- docs/source/_static/copybutton.js | 61 + docs/source/_static/custom.css | 21 + docs/source/_static/custom.js | 26 + docs/source/api.rst | 4 + docs/source/citation.rst | 12 + docs/source/conf.py | 39 +- docs/source/index.rst | 37 +- docs/source/tutorial.rst | 11 +- environment.yml | 9 + license | 2 +- prms_python/__init__.py | 2 +- prms_python/scenario.py | 10 +- setup.py | 1 - 55 files changed, 1372 insertions(+), 22298 deletions(-) create mode 100644 MANIFEST.in delete mode 100644 docs/build/html/_static/ajax-loader.gif delete mode 100644 docs/build/html/_static/comment-bright.png delete mode 100644 docs/build/html/_static/comment-close.png delete mode 100644 docs/build/html/_static/comment.png delete mode 100644 docs/build/html/_static/down-pressed.png delete mode 100644 docs/build/html/_static/down.png delete mode 100644 docs/build/html/_static/jquery-3.1.0.js delete mode 100644 docs/build/html/_static/jquery-3.2.1.js delete mode 100644 docs/build/html/_static/up-pressed.png delete mode 100644 docs/build/html/_static/up.png delete mode 100644 docs/build/html/_static/websupport.js create mode 100644 docs/source/_static/copybutton.js create mode 100644 docs/source/_static/custom.css create mode 100644 docs/source/_static/custom.js create mode 100644 docs/source/citation.rst create mode 100644 environment.yml diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000..649fa82 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,3 @@ +include license +include environment.yml +graft notebooks diff --git a/docs/build/doctrees/api.doctree 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zOZ4ds{o(sj_)3(0^vYiPa~u#WUKV&^Yp`K=?}|l zB%bw>y|j{evxB`M(9l{IcpVG8hO<2X1)lo?&wGLAe6~sRUEsN%<#{gf9M3jsehWOe zh2|GA1fFo8G^4Xkn#}^w1~fcrs$BLnUy zcH$EQ?rj7|1{{B){cPT*0mq*haCF&?033fJz;Q*e=9dYWe?))oq-SrYKMX|5 zhjpO+TgJVQBt~k19L7o}CFQTM6JsP<)LuE%{K}a37%{wLrxsdv5XC^KeD0xV7M@sm zjD!az^?@&wScimm=#T#k9ix9$cuorWb>xhD$+h}uRZt^XmYe?@ZItw%1)ltVFnRKE zOrT3*oPyn96~@Uq`Ra=Z%A8U8`Ut~7q(5q8=(DJvh1qonioxufG4E+;U6Dv=?RaA0 z(Rb&8>I(`~(0vN6{%s>xKfzLG$1H&actVfqG4Bm5wK;_rIXLkrlP>hw@P$skvrmj! zhFb2UW8N#e+~eD?m~_1}gX@)wT>;FkjhVMv-_n@(3a{@26R&S>aDCdm7OWOn<4a@a luGV;F%zK5`c<@TIhkkOE=2 + - - + Overview: module code — PRMS-Python v1.0.0 + - + + + + + @@ -57,7 +60,7 @@

All modules for which code is available

-

@@ -72,6 +75,7 @@

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    ©2016, John Volk and Matthew Turner. | - Powered by Sphinx 1.7.6 - & Alabaster 0.7.11 + Powered by Sphinx 2.2.0 + & Alabaster 0.7.12 diff --git a/docs/build/html/_modules/prms_python/data.html b/docs/build/html/_modules/prms_python/data.html index 912e2aa..2e0e985 100644 --- a/docs/build/html/_modules/prms_python/data.html +++ b/docs/build/html/_modules/prms_python/data.html @@ -1,19 +1,22 @@ - + - - + prms_python.data — PRMS-Python v1.0.0 + - + + + + + @@ -314,7 +317,7 @@

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    Source code for prms_python.optimizer

     
 
 
    
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    ©2016, John Volk and Matthew Turner. | - Powered by Sphinx 1.7.6 - & Alabaster 0.7.11 + Powered by Sphinx 2.2.0 + & Alabaster 0.7.12 diff --git a/docs/build/html/_modules/prms_python/parameters.html b/docs/build/html/_modules/prms_python/parameters.html index e1f7c4d..2f5e093 100644 --- a/docs/build/html/_modules/prms_python/parameters.html +++ b/docs/build/html/_modules/prms_python/parameters.html @@ -1,19 +1,22 @@ - + - - + prms_python.parameters — PRMS-Python v1.0.0 + - + + + + + @@ -626,7 +629,7 @@

    Source code for prms_python.parameters

     
 
 
    
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    ©2016, John Volk and Matthew Turner. | - Powered by Sphinx 1.7.6 - & Alabaster 0.7.11 + Powered by Sphinx 2.2.0 + & Alabaster 0.7.12 diff --git a/docs/build/html/_modules/prms_python/scenario.html b/docs/build/html/_modules/prms_python/scenario.html index 3634e57..9d4521f 100644 --- a/docs/build/html/_modules/prms_python/scenario.html +++ b/docs/build/html/_modules/prms_python/scenario.html @@ -1,19 +1,22 @@ - + - - + prms_python.scenario — PRMS-Python v1.0.0 + - + + + + + @@ -95,11 +98,11 @@

    Source code for prms_python.scenario

             first we initialize the object
 
         >>> series = ScenarioSeries(
-                       base_dir = 'dir_with_input_files',
-                       scenarios_dir = 'dir_to_run_series',
-                       title = 'title_for_group_of_scenarios',
-                       description = 'description_for_scenarios'
-                     )
+        >>>      base_dir = 'dir_with_input_files',
+        >>>      scenarios_dir = 'dir_to_run_series',
+        >>>      title = 'title_for_group_of_scenarios',
+        >>>      description = 'description_for_scenarios'
+        >>> )
         
         The next step is to "build" the ``ScenarioSeries`` by calling the 
         :meth:`ScenarioSeries.build` method which defines which parameters
@@ -652,7 +655,7 @@ 
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    ©2016, John Volk and Matthew Turner. | - Powered by Sphinx 1.7.6 - & Alabaster 0.7.11 + Powered by Sphinx 2.2.0 + & Alabaster 0.7.12 diff --git a/docs/build/html/_modules/prms_python/util.html b/docs/build/html/_modules/prms_python/util.html index 99c8eeb..6bcf206 100644 --- a/docs/build/html/_modules/prms_python/util.html +++ b/docs/build/html/_modules/prms_python/util.html @@ -1,19 +1,22 @@ - + - - + prms_python.util — PRMS-Python v1.0.0 + - + + + + + @@ -235,7 +238,7 @@

    Source code for prms_python.util

         return df
    -def load_data(data_file): +
    [docs]def load_data(data_file): """ Read the data file and load into a datetime indexed Pandas dataframe object. @@ -315,7 +318,7 @@

    Source code for prms_python.util

         df.drop(['year', 'month', 'day', 'hh', 'mm', 'sec'], axis=1, inplace=True)
     df.columns.name = 'input variables'
     df.index.name = 'date'  # name dataframe axes (index,columns)
-    return df
+    return df
    [docs]def nash_sutcliffe(observed, modeled): @@ -378,7 +381,7 @@

    Source code for prms_python.util

     
 
 
    
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    diff --git a/docs/build/html/_sources/api.rst.txt b/docs/build/html/_sources/api.rst.txt index 9d72296..b00829f 100644 --- a/docs/build/html/_sources/api.rst.txt +++ b/docs/build/html/_sources/api.rst.txt @@ -65,6 +65,10 @@ OptimizationResult Select helper functions ----------------------- +load_data +^^^^^^^^^^^^ +.. autofunction:: prms_python.load_data + load_statvar ^^^^^^^^^^^^ diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt index 72fefe4..3014734 100644 --- a/docs/build/html/_sources/index.rst.txt +++ b/docs/build/html/_sources/index.rst.txt @@ -7,18 +7,23 @@ PRMS-Python =========== This module provides intuitive structures and functions for implementing and -managing common modeling workflows with `PRMS `_, the United States Geological -Survery's hydrologic computer model. PRMS-Python gives direct access to PRMS input -parameters, climate forcing data, and output variables-- interfacing PRMS -data structures with powerful scientific Python data structures particularly -the ``numpy.ndarray`` and ``pandas.DataFrame`` objects. The module contains -routines to systematically modify input parameters using user defined methods -or a Monte Carlo parameter resampler; such routines then run a series of PRMS -simulations in parralel. Metadata is also created and tracked along with each -PRMS simulations inputs and outputs when running an automated routine. -Post-processing of PRMS-Python managed simulations is also enhanced by tools for -retrieving and archiving model inputs and outpts, calculate goodness-of-fit metrics, -and produce visualizations of model input and output. +managing common modeling workflows with `PRMS +`_, the United States +Geological Survery's hydrologic computer model. + +`View on GitHub `__ + +PRMS-Python gives direct access to PRMS input parameters, climate forcing data, +and output variables-- interfacing PRMS data structures with powerful +scientific Python data structures particularly the :obj:`numpy.ndarray` and +:obj:`pandas.DataFrame` objects. The module contains routines to systematically +modify input parameters using user defined methods or a Monte Carlo parameter +resampler; such routines then run a series of PRMS simulations in parralel. +Metadata is also created and tracked along with each PRMS simulation's inputs +and outputs when running an automated routine. Post-processing of PRMS-Python +managed simulations is also enhanced by tools for retrieving and archiving +model inputs and outputs, calculate goodness-of-fit metrics, and built in +visualizations routines are provided for model input and output. Install @@ -46,12 +51,7 @@ Then install dependencies and executable Usage ----- -.. - If you want to dive right in, modify some parameters and run some scenarios, - go on to the :doc:`cli` page. There you'll learn how to run `prmspy`, the - command-line interface to the PRMS-Python tools. - -We recommend getting acquainted with PRMS-Python by reading and applying some examples and recipes found in the :doc:`tutorial` page, and consult the :doc:`api` for more details. More examples including basic and advanced workflows, and additional documentation can be found in the Jupyter notebooks found in the `notebooks directory `_ in the PRMS-Python package. +We recommend getting acquainted with PRMS-Python by reading and applying some examples and recipes found in the :doc:`tutorial` page, and consult the :doc:`api` for more details. More examples including basic and advanced workflows with additional documentation can be found in the Jupyter notebooks on `GitHub `_. .. toctree:: @@ -61,6 +61,7 @@ We recommend getting acquainted with PRMS-Python by reading and applying some ex tutorial cli api + citation Questions or comments? Contact `John Volk `_ diff --git a/docs/build/html/_sources/tutorial.rst.txt b/docs/build/html/_sources/tutorial.rst.txt index 928865e..a98457e 100644 --- a/docs/build/html/_sources/tutorial.rst.txt +++ b/docs/build/html/_sources/tutorial.rst.txt @@ -1,14 +1,9 @@ -.. PRMS-Python documentation master file, created by - sphinx-quickstart on Tue Jun 28 10:24:04 2016. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - Tutorial and Recipes ==================== -In this tutorial we walk through each important class and function, and -explain each one's purpose and use with an example. At the end of the tutorial -in :ref:`example` we show a more advanced workflow involving parameter sensitivity +In this tutorial we walk through each important class and function, and explain +each one's purpose and use with an example. At the end of the tutorial in +:ref:`example` we show a more advanced workflow involving parameter sensitivity over dual parameter space that uses several of the previously described classes and functions. diff --git a/docs/build/html/_static/ajax-loader.gif b/docs/build/html/_static/ajax-loader.gif deleted file mode 100644 index 61faf8cab23993bd3e1560bff0668bd628642330..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 673 zcmZ?wbhEHb6krfw_{6~Q|Nno%(3)e{?)x>&1u}A`t?OF7Z|1gRivOgXi&7IyQd1Pl zGfOfQ60;I3a`F>X^fL3(@);C=vM_KlFfb_o=k{|A33hf2a5d61U}gjg=>Rd%XaNQW zW@Cw{|b%Y*pl8F?4B9 zlo4Fz*0kZGJabY|>}Okf0}CCg{u4`zEPY^pV?j2@h+|igy0+Kz6p;@SpM4s6)XEMg z#3Y4GX>Hjlml5ftdH$4x0JGdn8~MX(U~_^d!Hi)=HU{V%g+mi8#UGbE-*ao8f#h+S z2a0-5+vc7MU$e-NhmBjLIC1v|)9+Im8x1yacJ7{^tLX(ZhYi^rpmXm0`@ku9b53aN zEXH@Y3JaztblgpxbJt{AtE1ad1Ca>{v$rwwvK(>{m~Gf_=-Ro7Fk{#;i~+{{>QtvI yb2P8Zac~?~=sRA>$6{!(^3;ZP0TPFR(G_-UDU(8Jl0?(IXu$~#4A!880|o%~Al1tN diff --git a/docs/build/html/_static/alabaster.css b/docs/build/html/_static/alabaster.css index f10b805..f22dd9e 100644 --- a/docs/build/html/_static/alabaster.css +++ b/docs/build/html/_static/alabaster.css @@ -166,6 +166,19 @@ div.sphinxsidebar hr { width: 50%; } +div.sphinxsidebar .badge { + border-bottom: none; +} + +div.sphinxsidebar .badge:hover { + border-bottom: none; +} + +/* To address an issue with donation coming after search */ +div.sphinxsidebar h3.donation { + margin-top: 10px; +} + /* -- body styles ----------------------------------------------------------- */ a { diff --git a/docs/build/html/_static/basic.css b/docs/build/html/_static/basic.css index 19ced10..ea6972d 100644 --- a/docs/build/html/_static/basic.css +++ b/docs/build/html/_static/basic.css @@ -4,7 +4,7 @@ * * Sphinx stylesheet -- basic theme. * - * :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -81,6 +81,10 @@ div.sphinxsidebar input { font-size: 1em; } +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + div.sphinxsidebar #searchbox input[type="text"] { float: left; width: 80%; @@ -227,6 +231,16 @@ a.headerlink { visibility: hidden; } +a.brackets:before, +span.brackets > a:before{ + content: "["; +} + +a.brackets:after, +span.brackets > a:after { + content: "]"; +} + h1:hover > a.headerlink, h2:hover > a.headerlink, h3:hover > a.headerlink, @@ -275,6 +289,12 @@ img.align-center, .figure.align-center, object.align-center { margin-right: auto; } +img.align-default, .figure.align-default { + display: block; + margin-left: auto; + margin-right: auto; +} + .align-left { text-align: left; } @@ -283,6 +303,10 @@ img.align-center, .figure.align-center, object.align-center { text-align: center; } +.align-default { + text-align: center; +} + .align-right { text-align: right; } @@ -354,6 +378,11 @@ table.align-center { margin-right: auto; } +table.align-default { + margin-left: auto; + margin-right: auto; +} + table caption span.caption-number { font-style: italic; } @@ -387,6 +416,16 @@ table.citation td { border-bottom: none; } +th > p:first-child, +td > p:first-child { + margin-top: 0px; +} + +th > p:last-child, +td > p:last-child { + margin-bottom: 0px; +} + /* -- figures --------------------------------------------------------------- */ div.figure { @@ -427,6 +466,13 @@ table.field-list td, table.field-list th { hyphens: manual; } +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist td { + vertical-align: top; +} + + /* -- other body styles ----------------------------------------------------- */ ol.arabic { @@ -449,11 +495,58 @@ ol.upperroman { list-style: upper-roman; } +li > p:first-child { + margin-top: 0px; +} + +li > p:last-child { + margin-bottom: 0px; +} + +dl.footnote > dt, +dl.citation > dt { + float: left; +} + +dl.footnote > dd, +dl.citation > dd { + margin-bottom: 0em; +} + +dl.footnote > dd:after, +dl.citation > dd:after { + content: ""; + clear: both; +} + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dt:after { + content: ":"; 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+ -o-transition: opacity .3s ease-in-out; + transition: opacity .3s ease-in-out; + opacity: 0; + padding: 2px 6px; + position: absolute; + right: 4px; + top: 4px; +} +div.highlight:hover .copybtn, div.highlight .copybtn:focus { + opacity: .3; +} +div.highlight .copybtn:hover { + opacity: 1; +} +div.highlight { + position: relative; +} + diff --git a/docs/build/html/_static/doctools.js b/docs/build/html/_static/doctools.js index d892892..b33f87f 100644 --- a/docs/build/html/_static/doctools.js +++ b/docs/build/html/_static/doctools.js @@ -4,7 +4,7 @@ * * Sphinx JavaScript utilities for all documentation. * - * :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -87,14 +87,13 @@ jQuery.fn.highlightText = function(text, className) { node.nextSibling)); node.nodeValue = val.substr(0, pos); if (isInSVG) { - var bbox = span.getBBox(); var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - rect.x.baseVal.value = bbox.x; + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; rect.y.baseVal.value = bbox.y; rect.width.baseVal.value = bbox.width; rect.height.baseVal.value = bbox.height; rect.setAttribute('class', className); - var parentOfText = node.parentNode.parentNode; addItems.push({ "parent": node.parentNode, "target": rect}); @@ -150,7 +149,9 @@ var Documentation = { this.fixFirefoxAnchorBug(); this.highlightSearchWords(); this.initIndexTable(); - + if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { + this.initOnKeyListeners(); + } }, /** @@ -310,4 +311,4 @@ _ = Documentation.gettext; $(document).ready(function() { Documentation.init(); -}); \ No newline at end of file +}); diff --git a/docs/build/html/_static/documentation_options.js b/docs/build/html/_static/documentation_options.js index 14b6a79..6d86510 100644 --- a/docs/build/html/_static/documentation_options.js +++ b/docs/build/html/_static/documentation_options.js @@ -1,9 +1,10 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '0.1', + VERSION: '', LANGUAGE: 'None', COLLAPSE_INDEX: false, FILE_SUFFIX: '.html', HAS_SOURCE: true, - SOURCELINK_SUFFIX: '.txt' + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false }; 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- - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var document = window.document; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var concat = arr.concat; - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - - - - function DOMEval( code, doc ) { - doc = doc || document; - - var script = doc.createElement( "script" ); - - script.text = code; - doc.head.appendChild( script ).parentNode.removeChild( script ); - } -/* global Symbol */ -// Defining this global in .eslintrc would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.1.0", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }, - - // Support: Android <=4.0 only - // Make sure we trim BOM and NBSP - rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g, - - // Matches dashed string for camelizing - rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g, - - // Used by jQuery.camelCase as callback to replace() - fcamelCase = function( all, letter ) { - return letter.toUpperCase(); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - return num != null ? - - // Return just the one element from the set - ( num < 0 ? this[ num + this.length ] : this[ num ] ) : - - // Return all the elements in a clean array - slice.call( this ); - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !jQuery.isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - src = target[ name ]; - copy = options[ name ]; - - // Prevent never-ending loop - if ( target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = jQuery.isArray( copy ) ) ) ) { - - if ( copyIsArray ) { - copyIsArray = false; - clone = src && jQuery.isArray( src ) ? src : []; - - } else { - clone = src && jQuery.isPlainObject( src ) ? src : {}; - } - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isFunction: function( obj ) { - return jQuery.type( obj ) === "function"; - }, - - isArray: Array.isArray, - - isWindow: function( obj ) { - return obj != null && obj === obj.window; - }, - - isNumeric: function( obj ) { - - // As of jQuery 3.0, isNumeric is limited to - // strings and numbers (primitives or objects) - // that can be coerced to finite numbers (gh-2662) - var type = jQuery.type( obj ); - return ( type === "number" || type === "string" ) && - - // parseFloat NaNs numeric-cast false positives ("") - // ...but misinterprets leading-number strings, particularly hex literals ("0x...") - // subtraction forces infinities to NaN - !isNaN( obj - parseFloat( obj ) ); - }, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - - /* eslint-disable no-unused-vars */ - // See https://github.com/eslint/eslint/issues/6125 - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - type: function( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; - }, - - // Evaluates a script in a global context - globalEval: function( code ) { - DOMEval( code ); - }, - - // Convert dashed to camelCase; used by the css and data modules - // Support: IE <=9 - 11, Edge 12 - 13 - // Microsoft forgot to hump their vendor prefix (#9572) - camelCase: function( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); - }, - - nodeName: function( elem, name ) { - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // Support: Android <=4.0 only - trim: function( text ) { - return text == null ? - "" : - ( text + "" ).replace( rtrim, "" ); - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return concat.apply( [], ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // Bind a function to a context, optionally partially applying any - // arguments. - proxy: function( fn, context ) { - var tmp, args, proxy; - - if ( typeof context === "string" ) { - tmp = fn[ context ]; - context = fn; - fn = tmp; - } - - // Quick check to determine if target is callable, in the spec - // this throws a TypeError, but we will just return undefined. - if ( !jQuery.isFunction( fn ) ) { - return undefined; - } - - // Simulated bind - args = slice.call( arguments, 2 ); - proxy = function() { - return fn.apply( context || this, args.concat( slice.call( arguments ) ) ); - }; - - // Set the guid of unique handler to the same of original handler, so it can be removed - proxy.guid = fn.guid = fn.guid || jQuery.guid++; - - return proxy; - }, - - now: Date.now, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = jQuery.type( obj ); - - if ( type === "function" || jQuery.isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.0 - * https://sizzlejs.com/ - * - * Copyright jQuery Foundation and other contributors - * Released under the MIT license - * http://jquery.org/license - * - * Date: 2016-01-04 - */ -(function( window ) { - -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ({}).hasOwnProperty, - arr = [], - pop = arr.pop, - push_native = arr.push, - push = arr.push, - slice = arr.slice, - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[i] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier - identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - // "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace + - "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ), - - rattributeQuotes = new RegExp( "=" + whitespace + "*([^\\]'\"]*?)" + whitespace + "*\\]", "g" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace + - "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace + - "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + - whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ), - funescape = function( _, escaped, escapedWhitespace ) { - var high = "0x" + escaped - 0x10000; - // NaN means non-codepoint - // Support: Firefox<24 - // Workaround erroneous numeric interpretation of +"0x" - return high !== high || escapedWhitespace ? - escaped : - high < 0 ? - // BMP codepoint - String.fromCharCode( high + 0x10000 ) : - // Supplemental Plane codepoint (surrogate pair) - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\x80-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - disabledAncestor = addCombinator( - function( elem ) { - return elem.disabled === true; - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - (arr = slice.call( preferredDoc.childNodes )), - preferredDoc.childNodes - ); - // Support: Android<4.0 - // Detect silently failing push.apply - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - push_native.apply( target, slice.call(els) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - // Can't trust NodeList.length - while ( (target[j++] = els[i++]) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - - if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) { - setDocument( context ); - } - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) { - - // ID selector - if ( (m = match[1]) ) { - - // Document context - if ( nodeType === 9 ) { - if ( (elem = context.getElementById( m )) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && (elem = newContext.getElementById( m )) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[2] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( (m = match[3]) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !compilerCache[ selector + " " ] && - (!rbuggyQSA || !rbuggyQSA.test( selector )) ) { - - if ( nodeType !== 1 ) { - newContext = context; - newSelector = selector; - - // qSA looks outside Element context, which is not what we want - // Thanks to Andrew Dupont for this workaround technique - // Support: IE <=8 - // Exclude object elements - } else if ( context.nodeName.toLowerCase() !== "object" ) { - - // Capture the context ID, setting it first if necessary - if ( (nid = context.getAttribute( "id" )) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", (nid = expando) ); - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[i] = "#" + nid + " " + toSelector( groups[i] ); - } - newSelector = groups.join( "," ); - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - } - - if ( newSelector ) { - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return (cache[ key + " " ] = value); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement("fieldset"); - - try { - return !!fn( el ); - } catch (e) { - return false; - } finally { - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split("|"), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[i] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( (cur = cur.nextSibling) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return (name === "input" || name === "button") && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - // Known :disabled false positives: - // IE: *[disabled]:not(button, input, select, textarea, optgroup, option, menuitem, fieldset) - // not IE: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Check form elements and option elements for explicit disabling - return "label" in elem && elem.disabled === disabled || - "form" in elem && elem.disabled === disabled || - - // Check non-disabled form elements for fieldset[disabled] ancestors - "form" in elem && elem.disabled === false && ( - // Support: IE6-11+ - // Ancestry is covered for us - elem.isDisabled === disabled || - - // Otherwise, assume any non-
    -
    +
    -metadata
    -

    dict – A property that gets and sets the header information from +property metadata +

    A property that gets and sets the header information from a standard PRMS climate input data file held in a Python dictionary. As a property it can be assigned directly to overwrite or create a new PRMS data file. As such the user is in control and must supply the correct @@ -159,47 +167,42 @@

    Data -

    Note

    -

    When assigning or creating a new data file, the Data.write method will +

    Note

    +

    When assigning or creating a new data file, the Data.write method will assign the appropriate date header that follows the line of number signs “#”.

    - --- - - - -
    Raises:
      -
    • ValueError – if data in metadata is accessed before data is assigned, -e.g. if accessed to write a PRMS data file from a Data instance -that was initialized without a valid PRMS data file.
      • -
    • TypeError – if an object that is not a Python dictionary is assigned.
      • +
      +
      Raises
      +
        +

      • ValueError – if data in metadata is accessed before data is assigned, + e.g. if accessed to write a PRMS data file from a Data instance + that was initialized without a valid PRMS data file.

        • +

      • TypeError – if an object that is not a Python dictionary is assigned.

      -
    +

    +
    Type
    +

    dict

    +
    +
    -modify(func, vars_to_adjust)[source]
    +modify(func, vars_to_adjust)[source]

    Apply a user defined function to one or more variable(s) in the data file.

    The modify method allows for inplace modification of one or more time series inputs in the data file based on a user defined function.

    - --- - - - - - -
    Parameters:
      -
    • func (function) – function to apply to each variable in vars_to_adjust
      • -
    • vars_to_adjust (list or tuple) – collection of variable names to apply func to.
      • +
      +
      Parameters
      +
        +

      • func (function) – function to apply to each variable in vars_to_adjust

        • +

      • vars_to_adjust (list or tuple) – collection of variable names to apply func to.

      -
    Returns:

    None

    -
    +
    +
    Returns
    +

    None

    +
    +

    Example

    Here is an example of loading a data file, modifying the temperature inputs (tmin and tmax) by adding two degrees to each element, and rewritting the @@ -215,27 +218,26 @@

    Data
    -write(out_path)[source]
    +write(out_path)[source]

    Writes the current state of the Data to PRMS text format utilizing the Data.metadata and Data.data_frame instance properties. If Data.data_frame was never accessed or assigned new values then this method simply copies the original PRMS data file to out_path.

    - --- - - - - - - - -
    Parameters:out_path (str) – full path to save or copy the current PRMS data -in PRMS text format.
    Returns:None
    Raises:ValueError – if the write method is called without assigning either -an initial data (base_file) path or assigning correct metadata -and data_frame properties.
    +
    +
    Parameters
    +

    out_path (str) – full path to save or copy the current PRMS data +in PRMS text format.

    +
    +
    Returns
    +

    None

    +
    +
    Raises
    +

    ValueError – if the write method is called without assigning either + an initial data (base_file) path or assigning correct metadata + and data_frame properties.

    +
    +
    @@ -245,7 +247,7 @@

    DataParameters

    -class prms_python.Parameters(base_file)[source]
    +class prms_python.Parameters(base_file)[source]

    Disk-based representation of a PRMS parameter file.

    For the sake of memory efficiency, we only load parameters from base_file that get modified through item assignment or accessed directly. @@ -258,49 +260,71 @@

    Parameters - --- - - - -
    Parameters:base_file (str) – path to PRMS parameters file
    +
    +
    Parameters
    +

    base_file (str) – path to PRMS parameters file

    +
    +
    -base_file
    -

    str – path to PRMS parameters file

    +base_file +

    path to PRMS parameters file

    +
    +
    Type
    +

    str

    +
    +
    -base_file_reader
    -

    file – file handle of PRMS parameters file

    +base_file_reader +

    file handle of PRMS parameters file

    +
    +
    Type
    +

    file

    +
    +
    -dimensions
    -

    collections.OrderedDict – dictionary with +dimensions +

    dictionary with parameter dimensions as defined in parameters file loaded on initialization

    +
    +
    Type
    +

    collections.OrderedDict

    +
    +
    -base_params
    -

    list of dicts – list of dictionaries of parameter +base_params +

    list of dictionaries of parameter metadata loaded on initialization e.g. name, dimension(s), data type, length of data array, and lines where data starts and ends in file

    +
    +
    Type
    +

    list of dicts

    +
    +
    -param_arrays
    -

    dict – dictionary with parameteter names as keys and +param_arrays +

    dictionary with parameteter names as keys and numpy.array and numpy.ndarray representations of parameter values as keys. Initially empty, uses getter and setter functions.

    +
    +
    Type
    +

    dict

    +
    +

    Example

    @@ -318,7 +342,7 @@

    Parametersp.

    -plot(nrows, which='all', out_dir=None, xlabel=None, ylabel=None, cbar_label=None, title=None, mpl_style=None)[source]
    +plot(nrows, which='all', out_dir=None, xlabel=None, ylabel=None, cbar_label=None, title=None, mpl_style=None)[source]

    Versatile method that plots most parameters in a standard PRMS parameter file assuming the PRMS model was built on a uniform spatial grid.

    Plots parameters as line plots for series or 2D spatial grid depending on @@ -329,38 +353,33 @@

    Parameterswhich and more detailed explanation in the example Jupyter notebook.

    - --- - - - - - - - - -
    Parameters:

    nrows (int) – The number of rows in the PRMS model grid for plotting +

    +
    Parameters
    +

    nrows (int) – The number of rows in the PRMS model grid for plotting spatial parameters. Will only work correctly for rectangular gridded models with HRU indices starting in the upper left cell moving left to right across columns and down across rows.

    -
    Keyword Arguments:
     
      -
    • which (str) – name of PRMS parameter to plot or ‘all’. If ‘all’ then + +
      Keyword Arguments
      +
        +

      • which (str) – name of PRMS parameter to plot or ‘all’. If ‘all’ then the function will print 3 multipage pdfs, one for nhru dimensional parameters, one for nhru by monthly parameters, one for other parameters of length > 1, and one html file containing -single valued parameters.

        • -
      • out_dir (str) – path to an output dir, default current directory
        • -
      • xlabel (str) – x label for plot(s)
        • -
      • ylabel (str) – y label for plot(s)
        • -
      • cbar_label (str) – label for colorbar on spatial plot(s)
        • -
      • title (str) – plot title
        • -
      • mpl_style (str, list) – name or list of names of matplotlib style sheets to -use for plot(s).
        • +single valued parameters.

        +

      • out_dir (str) – path to an output dir, default current directory

        • +

      • xlabel (str) – x label for plot(s)

        • +

      • ylabel (str) – y label for plot(s)

        • +

      • cbar_label (str) – label for colorbar on spatial plot(s)

        • +

      • title (str) – plot title

        • +

      • mpl_style (str, list) – name or list of names of matplotlib style sheets to +use for plot(s).

      -
    Returns:

    None

    -
    +

    +
    Returns
    +

    None

    +
    +

    Examples

    If the plot method is called with the keyword argument which set to a parameter that has length one, i.e. single valued it will simply @@ -390,22 +409,20 @@

    Parameters
    -write(out_name)[source]
    +write(out_name)[source]

    Writes current state of Parameters to disk in PRMS text format

    To reduce memory usage the write method copies parameters from the initial base_file parameter file for all parameters that were never modified.

    - --- - - - - - -
    Parameters:out_name (str) – path to write Parameters data to PRMS text -format.
    Returns:None
    +
    +
    Parameters
    +

    out_name (str) – path to write Parameters data to PRMS text +format.

    +
    +
    Returns
    +

    None

    +
    +

    @@ -415,7 +432,7 @@

    ParametersSimulation

    -class prms_python.Simulation(input_dir=None, simulation_dir=None)[source]
    +class prms_python.Simulation(input_dir=None, simulation_dir=None)[source]

    Class that runs and manages file structure for a single PRMS simulation.

    The Simulation class provides low-level managment of a PRMS simulation by copying model input files from input_dir argument to an output dir @@ -429,61 +446,48 @@

    Simulation - --- - - - - -
    Keyword Arguments:
     
      -
    • input_dir (str) – path to directory that contains control, parameter, -and data files for the simulation
      • -
    • simulation_dir (str) – directory path to bundle inputs and outputs
      • +
      +
      Keyword Arguments
      +
        +

      • input_dir (str) – path to directory that contains control, parameter, +and data files for the simulation

        • +

      • simulation_dir (str) – directory path to bundle inputs and outputs

      -
    +

    +

    Example

    see Simulation.run()

    - --- - - - -
    Raises:RuntimeError – if input directory does not contain a PRMS data, -parameters, and control file.
    -
    +
    +
    Raises
    +

    RuntimeError – if input directory does not contain a PRMS data, + parameters, and control file.

    +
    +
    +
    -classmethod from_data(data, parameters, control_path, simulation_dir)[source]
    +classmethod from_data(data, parameters, control_path, simulation_dir)[source]

    Create a Simulation from a Data and Parameter object, plus a path to the control file, and providing a simulation_dir where the simulation should be run.

    - --- - - - - - -
    Parameters:
      -
    • data (Data) – Data object for simulation
      • -
    • parameters (Parameters) – Parameters object for simulation
      • -
    • control_path (str) – path to control file
      • -
    • simulation_dir (str) – path to directory where simulations will be +
      +
      Parameters
      +
        +

      • data (Data) – Data object for simulation

        • +

      • parameters (Parameters) – Parameters object for simulation

        • +

      • control_path (str) – path to control file

        • +

      • simulation_dir (str) – path to directory where simulations will be run and where input and output will be stored. If it exists it will -be overwritten.

        • +be overwritten.

      -
    Returns:

    -
    Simulation ready to be run using simulation_dir for
    -

    inputs and outputs

    +
    +
    Returns
    +

    +
    Simulation ready to be run using simulation_dir for

    inputs and outputs

    -
    +
    +

    Example

    >>> d = Data('path_to_data_file')
 >>> p = Parameters('path_to_parameters_file')
@@ -493,20 +497,17 @@ 
    Simulation>>> sim.run()
    - --- - - - -
    Raises:TypeError – if data and parameters arguments are not of type -Data and Parameters
    +
    +
    Raises
    +

    TypeError – if data and parameters arguments are not of type + Data and Parameters

    +
    +
    -run(prms_exec='prms')[source]
    +run(prms_exec='prms')[source]

    Run a Simulation instance using PRMS input files from input_dir and copy to the Simulation file structure under simulation_dir if given, otherwise leave PRMS input output unstructured and in input_dir

    @@ -515,15 +516,11 @@

    Simulationrun method below.

    - --- - - - - -
    Keyword Arguments:
     prms_exec (str) – name of PRMS executable on $PATH or path to executable
    +
    +
    Keyword Arguments
    +

    prms_exec (str) – name of PRMS executable on $PATH or path to executable

    +
    +

    Examples

    If we create a Simulation instance by only assigning the input_dir argument and call its run method the model will be @@ -577,8 +574,8 @@

    Simulation
    -

    Note

    -

    As shown in the last example, currently the Simulation.run routine only +

    Note

    +

    As shown in the last example, currently the Simulation.run routine only recognizes the data, parameters. and control file as PRMS inputs, all other files found in input_dir before and after normal completion of the PRMS simulation will be transferred to simulation_dir/outputs/.

    @@ -592,7 +589,7 @@

    SimulationSimulationSeries

    -class prms_python.SimulationSeries(simulations)[source]
    +class prms_python.SimulationSeries(simulations)[source]

    Series of simulations all to be run through a common interface.

    Utilizes multiprocessing.Pool class to parallelize the execution of series of PRMS simulations. SimulationSeries also @@ -602,15 +599,12 @@

    SimulationSeriesSimulationSeries will follow the strict file structure as defined by Simulation.run(). This class is useful particularly for creating new programatic workflows not provided yet by PRMS-Python.

    - --- - - - -
    Parameters:simulations (list or tuple) – list of Simulation objects -to be run.
    +
    +
    Parameters
    +

    simulations (list or tuple) – list of Simulation objects +to be run.

    +
    +

    Example

    Lets say you have already created a series of PRMS models by modifying the input climatic forcing data, e.g. you have 100 data files and @@ -650,8 +644,8 @@

    SimulationSeriessimulation_dir following the file structure of Simulation.run().

    -

    Note

    -

    The Simulation and SimulationSeries classes +

    Note

    +

    The Simulation and SimulationSeries classes are low-level in that they alone do not create metadata for PRMS simulation scenarios. In other words they do not produce any additional files that may help the user know what differs @@ -659,24 +653,19 @@

    SimulationSeries
    -outputs_iter()[source]
    +outputs_iter()[source]

    Return a generator of directories with the path to the simulation_dir as well as paths to the statvar.dat output file, and data and parameters input files used in the simulation.

    - --- - - - -
    Yields:

    dict –

    -
    -
    dictionary of paths to simulation directory,
    -

    input, and output files.

    +
    +
    Yields
    +

    dict –

    +
    +
    dictionary of paths to simulation directory,

    input, and output files.

    +
    +
    -

    Example

    >>> ser = SimulationSeries(simulations)
 >>> ser.run()
@@ -697,28 +686,23 @@ 
    SimulationSeries
 
    
-run(prms_exec='prms', nproc=None)[source]
    
+run(prms_exec='prms', nproc=None)[source]
 
    Method to run multiple Simulation objects in parrallel.
    
-
---
-
-
-
-
-
    


    Keyword Arguments:
     
      
-
    • prms_exec (str) – name of PRMS executable on $PATH or path to
-executable
      • 
-
    • nproc (int or None) – number of logical or physical processors
-for parrallel execution of PRMS simulations.
      • 
+
      
+
      Keyword Arguments
      
+
        
+
      • prms_exec (str) – name of PRMS executable on $PATH or path to
+executable
        • 
+
      • nproc (int or None) – number of logical or physical processors
+for parrallel execution of PRMS simulations.
        • 
 
      
-
    
+
    
+

    Example

    see SimulationSeries

    -

    Note

    -

    If nproc is not assigned the deault action is to use half +

    Note

    +

    If nproc is not assigned the deault action is to use half of the available processecors on the machine using the Python multiprocessing module.

    @@ -731,7 +715,7 @@

    SimulationSeriesScenario

    -class prms_python.Scenario(base_dir, scenario_dir, title=None, description=None)[source]
    +class prms_python.Scenario(base_dir, scenario_dir, title=None, description=None)[source]

    Container for the process in which one modifies input parameters then runs a simulation while tracking metadata.

    Metadata includes a title and description, if provided, plus start/end @@ -739,35 +723,36 @@

    Scenariojson format making it conveniently read as a Python dictionary.

    - --- - - - -
    Parameters:
      -
    • base_dir (str) – path to directory that contains initial control, +
      +
      Parameters
      +
        +

      • base_dir (str) – path to directory that contains initial control, parameter, and data files to be used for Scenario. The parameters file in base_dir will not be modifed -instead will be copied to scenario_dir and them modified.

        • -
      • scenario_dir (str) – directory path to bundle inputs and outputs
        • -
      • title (str, optional) – title of Scenario, if given will be +instead will be copied to scenario_dir and them modified.

        • +

      • scenario_dir (str) – directory path to bundle inputs and outputs

        • +

      • title (str, optional) – title of Scenario, if given will be added to Scenario.metadata attribute as well as the metadata.json file in scenario_dir written after calling the Scenario.build() and Scenario.run() -methods.

        • -
      • description (str, optional) – description of Scenario, also -is added to Scenario.metadata as title.
        • +methods.

        +

      • description (str, optional) – description of Scenario, also +is added to Scenario.metadata as title.

      -
    +

    +
    -metadata
    -

    scenario.ScenarioMetadata – a dictionary-like +metadata +

    a dictionary-like class in prms_python.scenario that tracks Scenario and ScenarioSeries imformation including user-defined parameter modifications and descriptions, and file structure.

    +
    +
    Type
    +

    scenario.ScenarioMetadata

    +
    +

    Examples

    @@ -849,8 +834,8 @@

    Scenario -

    Note

    -

    The main differentiator between Scenario and +

    Note

    +

    The main differentiator between Scenario and ScenarioSeries is that Scenario is designed for modifying one or more parameters of a single parameters file whereas ScenarioSeries is designed for modifying and tracking the @@ -859,30 +844,27 @@

    Scenario
    -build(param_mod_funs=None)[source]
    +build(param_mod_funs=None)[source]

    Take a user-defined dictionary with param names as keys and Python functions as values, copy the original input files as given when initializing a Scenario instance to the simulation_dir then apply the functions in the user-defined dictionary to the parameters there. The build method must be called before running the Scenario (calling Scenario.run() ).

    - --- - - - - - - -
    Keyword Arguments:
     param_mod_funs (dict) – dictionary with parameter names as keys -and Python functions as values to apply to the names (key)
    Returns:None
    +
    +
    Keyword Arguments
    +

    param_mod_funs (dict) – dictionary with parameter names as keys +and Python functions as values to apply to the names (key)

    +
    +
    Returns
    +

    None

    +
    +

    Example

    see Scenario for a full example.

    -

    Note

    -

    If the scenario_dir that was assigned for the current +

    Note

    +

    If the scenario_dir that was assigned for the current instance already exists, it will be overwritten when build is invoked.

    @@ -890,31 +872,25 @@

    Scenario
    -run(prms_exec='prms')[source]
    +run(prms_exec='prms')[source]

    Run the PRMS simulation for a built Scenario instance.

    - --- - - - - - - -
    Keyword Arguments:
     prms_exec (str) – name of PRMS executable on $PATH or path to -executable
    Returns:None
    +
    +
    Keyword Arguments
    +

    prms_exec (str) – name of PRMS executable on $PATH or path to +executable

    +
    +
    Returns
    +

    None

    +
    +

    Examples

    see Scenario for full example

    - --- - - - -
    Raises:RuntimeError – if the Scenario.build() method has not yet -been called.
    +
    +
    Raises
    +

    RuntimeError – if the Scenario.build() method has not yet + been called.

    +
    +

    @@ -924,7 +900,7 @@

    ScenarioScenarioSeries

    -class prms_python.ScenarioSeries(base_dir, scenarios_dir, title=None, description=None)[source]
    +class prms_python.ScenarioSeries(base_dir, scenarios_dir, title=None, description=None)[source]

    Create and manage a series of model runs where parameters are modified.

    First initialize the series with an optional title and description. Then to build the series the user provides a list of dictionaries with @@ -940,53 +916,58 @@

    ScenarioSeries - --- - - - - - - -
    Parameters:
      -
    • base_dir (str) – path to base inputs; ‘control’, ‘parameters’, -and ‘data’ must be present there
      • -
    • scenarios_dir (str) – directory where scenario data will be written -to; will be overwritten or created if it does not exist
      • +
      +
      Parameters
      +
        +

      • base_dir (str) – path to base inputs; ‘control’, ‘parameters’, +and ‘data’ must be present there

        • +

      • scenarios_dir (str) – directory where scenario data will be written +to; will be overwritten or created if it does not exist

      -
    Keyword Arguments:
     
      -
    • title (str, optional) – title of the ScenarioSeries instance
      • -
    • description (str, optional) – description of the ScenarioSeries -instance
      • + +
      Keyword Arguments
      +
        +

      • title (str, optional) – title of the ScenarioSeries instance

        • +

      • description (str, optional) – description of the ScenarioSeries +instance

      -
    +

    +
    -metadata
    -

    dict – dictionary with title, description, and UUID map +metadata +

    dictionary with title, description, and UUID map dictionary for individiual Scenario output directories, the UUID dictionary (uuid_title_map)is left empty until calling ScenarioSeries.build().

    +
    +
    Type
    +

    dict

    +
    +
    -scenarios
    -

    list – empty list that will be filled with ``Scenario``s +scenarios +

    empty list that will be filled with ``Scenario``s after defining them by calling ScenarioSeries.build().

    +
    +
    Type
    +

    list

    +
    +

    Example

    There are three steps to both Scenario and ScenarioSeries, first we initialize the object

    >>> series = ScenarioSeries(
-               base_dir = 'dir_with_input_files',
-               scenarios_dir = 'dir_to_run_series',
-               title = 'title_for_group_of_scenarios',
-               description = 'description_for_scenarios'
-             )
+>>>      base_dir = 'dir_with_input_files',
+>>>      scenarios_dir = 'dir_to_run_series',
+>>>      title = 'title_for_group_of_scenarios',
+>>>      description = 'description_for_scenarios'
+>>> )

    The next step is to “build” the ScenarioSeries by calling the @@ -997,7 +978,7 @@

    ScenarioSeriesAlso see Scenario & ScenarioSeries for full example

    -build(scenarios_list)[source]
    +build(scenarios_list)[source]

    Build the scenarios from a list of scenario definitions in dicitonary form.

    Each element of scenarios_list can have any number of parameters @@ -1006,18 +987,16 @@

    ScenarioSeriesseries_metadata.json for future lookups. The build method also creates a file structure that uses UUID values as individiual Scenario subdirectories as shown below.

    - --- - - - - - -
    Parameters:scenarios_list (list) – list of dictionaries with key-value +
    +
    Parameters
    +

    scenarios_list (list) – list of dictionaries with key-value pairs being parameter-function definition pairs or -title-title string or description-description string.

    Returns:None
    +title-title string or description-description string.

    +

    +
    Returns
    +

    None

    +
    +

    Examples

    Following the initialization of a ScenarioSeries instance as shown the example docstring there, we “build” the series by @@ -1068,60 +1047,51 @@

    ScenarioSeries -
    +
    -classmethod from_parameters_iter(base_directory, parameters_iter, title=None, description=None)[source]
    +classmethod from_parameters_iter(base_directory, parameters_iter, title=None, description=None)[source]

    Alternative way to initialize and build a ScenarioSeries in one step.

    Create and build a ScenarioSeries by including the param-keyed- function-valued dictionary (parameters_iter) that is otherwise passed in ScenarioSeries.build().

    - --- - - - - - -
    Parameters:
      -
    • base_directory (str) – directory that contains model input files
      • -
    • parameters_iter (list of dicts) – list of dictionaries for each +
      +
      Parameters
      +
        +

      • base_directory (str) – directory that contains model input files

        • +

      • parameters_iter (list of dicts) – list of dictionaries for each Scenario as described in Scenario and -ScenarioSeries.build().

        • -
      • title (str) – title for group of scenarios
        • -
      • description (str) – description for group of scenarios
        • +ScenarioSeries.build().

        +

      • title (str) – title for group of scenarios

        • +

      • description (str) – description for group of scenarios

      -
    Returns:

    None

    -
    +
    +
    Returns
    +

    None

    +
    +
    -run(prms_exec='prms', nproc=None)[source]
    +run(prms_exec='prms', nproc=None)[source]

    Run a “built” ScenarioSeries and make final updates to file structure and metadata.

    - --- - - - - - - -
    Keyword Arguments:
     
      -
    • prms_exec (str) – name of PRMS executable on $PATH or path to -executable. Default = ‘prms’
      • -
    • nproc (int or None) – number of processceors available to +
      +
      Keyword Arguments
      +
        +

      • prms_exec (str) – name of PRMS executable on $PATH or path to +executable. Default = ‘prms’

        • +

      • nproc (int or None) – number of processceors available to parallelize PRMS simulations, if None (default) then use half of what the multiprocessing detects on the -machine.

        • +machine.

      -
    Returns:

    None

    -
    +
    +
    Returns
    +

    None

    +
    +

    Examples

    This example starts where the example ends in ScenarioSeries.build(), calling run will run the @@ -1187,8 +1157,8 @@

    ScenarioSeries
    -

    Note

    -

    As shown, it is important to give appropriate scenario titles when +

    Note

    +

    As shown, it is important to give appropriate scenario titles when building a ScenarioSeries dictionary in order to later understand how parameters were modified in each scenario. If not one would have to rely on the individual metadata.json files @@ -1203,7 +1173,7 @@

    ScenarioSeriesOptimizer

    -class prms_python.Optimizer(parameters, data, control_file, working_dir, title, description=None)[source]
    +class prms_python.Optimizer(parameters, data, control_file, working_dir, title, description=None)[source]

    Container for PRMS parameter optimization and related routines.

    Currently the monte_carlo method provides random parameter resampling routines using uniform and normal random variables.

    @@ -1228,90 +1198,79 @@

    Optimizer
    -monte_carlo(reference_path, param_names, statvar_name, stage, n_sims=10, method='uniform', mu_factor=1, noise_factor=0.1, nproc=None)[source]
    +monte_carlo(reference_path, param_names, statvar_name, stage, n_sims=10, method='uniform', mu_factor=1, noise_factor=0.1, nproc=None)[source]

    The monte_carlo method of Optimizer performs parameter random resampling techniques to a set of PRMS parameters and executes and manages the corresponding simulations.

    - --- - - - - - - - - -
    Parameters:
      -
    • reference_path (str) – path to measured data for optimization
      • -
    • param_names (list) – list of parameter names to resample
      • -
    • statvar_name (str) – name of statisical variable output name for -optimization
      • -
    • stage (str) – custom name of optimization stage e.g. ‘ddsolrad’
      • +
      +
      Parameters
      +
        +

      • reference_path (str) – path to measured data for optimization

        • +

      • param_names (list) – list of parameter names to resample

        • +

      • statvar_name (str) – name of statisical variable output name for +optimization

        • +

      • stage (str) – custom name of optimization stage e.g. ‘ddsolrad’

      -
    Keyword Arguments:
     
      -
    • n_sims (int) – number of simulations to conduct -parameter optimization/uncertaitnty analysis.
      • -
    • method (str) – resampling method for parameters (normal or uniform)
      • -
    • mu_factor (float) – coefficient to scale mean of the parameter(s) + +
      Keyword Arguments
      +
        +

      • n_sims (int) – number of simulations to conduct +parameter optimization/uncertaitnty analysis.

        • +

      • method (str) – resampling method for parameters (normal or uniform)

        • +

      • mu_factor (float) – coefficient to scale mean of the parameter(s) to resample from when using the normal distribution to resample i.e. a value of 1.5 will sample from a normal rv with mean -50% higher than the original parameter mean

        • -
      • noise_factor (float) – scales the variance of noise to add to -parameter values when using normal rv (method=’normal’)
        • -
      • nproc (int) – number of processors available to run PRMS simulations
        • +50% higher than the original parameter mean

        +

      • noise_factor (float) – scales the variance of noise to add to +parameter values when using normal rv (method=’normal’)

        • +

      • nproc (int) – number of processors available to run PRMS simulations

      -
    Returns:

    None

    -
    +
    +
    Returns
    +

    None

    +
    +

    -plot_optimization(freq='daily', method='time_series', plot_vars='both', plot_1to1=True, return_fig=False, n_plots=4)[source]
    +plot_optimization(freq='daily', method='time_series', plot_vars='both', plot_1to1=True, return_fig=False, n_plots=4)[source]

    Basic plotting of current optimization results with limited options. Plots measured, original simluated, and optimization simulated variabes Not recommended for plotting results when n_sims is very large, instead use options from an OptimizationResult object, or employ a user-defined method using the result data.

    - --- - - - - - - - - -
    Keyword Arguments:
     
      -
    • freq (str) – frequency of time series plots, value can be ‘daily’ -or ‘monthly’ for solar radiation
      • -
    • method (str) – ‘time_series’ for time series sub plot of each +
      +
      Keyword Arguments
      +
        +

      • freq (str) – frequency of time series plots, value can be ‘daily’ +or ‘monthly’ for solar radiation

        • +

      • method (str) – ‘time_series’ for time series sub plot of each simulation alongside measured radiation. Another choice is ‘correlation’ which plots each measured daily solar radiation value versus the corresponding simulated variable as subplots one for each simulation in the optimization. With coefficients -of determiniationi i.e. square of pearson correlation coef.

        • -
      • plot_vars (str) – what to plot alongside simulated srad: +of determiniationi i.e. square of pearson correlation coef.

        • +

      • plot_vars (str) – what to plot alongside simulated srad: ‘meas’: plot simulated along with measured swrad ‘orig’: plot simulated along with the original simulated swrad -‘both’: plot simulated, with original simulation and measured

        • -
      • plot_1to1 (bool) – if True plot one to one line on correlation -scatter plot, otherwise exclude.
        • -
      • return_fig (bool) – flag whether to return matplotlib figure
        • +‘both’: plot simulated, with original simulation and measured

        +

      • plot_1to1 (bool) – if True plot one to one line on correlation +scatter plot, otherwise exclude.

        • +

      • return_fig (bool) – flag whether to return matplotlib figure

      -
    Returns:

    -
    If kwarg return_fig=True, then return
    -

    copy of the figure that is generated to the user.

    +
    +
    Returns
    +

    +
    If kwarg return_fig=True, then return

    copy of the figure that is generated to the user.

    -
    Return type:

    f (matplotlib.figure.Figure)

    -
    +
    +
    Return type
    +

    f (matplotlib.figure.Figure)

    +
    +
    @@ -1321,7 +1280,7 @@

    OptimizerOptimizationResult

    -class prms_python.OptimizationResult(working_dir, stage)[source]
    +class prms_python.OptimizationResult(working_dir, stage)[source]

    The OptimizationResult object serves to collect and manage output from an Optimizer method. Upon initialization and a given optimization stage that was used when running the Optimizer method, e.g. monte_carlo, @@ -1366,36 +1325,31 @@

    OptimizationResult
    -archive(remove_sims=True, remove_meta=False, metric_freq='daily')[source]
    +archive(remove_sims=True, remove_meta=False, metric_freq='daily')[source]

    Create archive directory to hold json files that contain information of adjusted parameters, model output, and performance metrics for each Optimizer simulation of the OptimizationResult.stage in the OptimizationResult.working_dir.

    - --- - - - - - - -
    Keyword Arguments:
     
      -
    • remove_sims (bool) – If True recursively delete all folders +
      +
      Keyword Arguments
      +
        +

      • remove_sims (bool) – If True recursively delete all folders and files associated with original simulations of the OptimizationResult.stage in the OptimizationResult.working_dir, if False do not delete -simulations.

        • -
      • remove_meta (bool) – Whether to delete original Optimizer -JSON metadata files, default is False.
        • -
      • metric_freq (Str) – Frequency of output metric computation +simulations.

        • +

      • remove_meta (bool) – Whether to delete original Optimizer +JSON metadata files, default is False.

        • +

      • metric_freq (Str) – Frequency of output metric computation for recording of model performance. Can be ‘daily’ (default) or ‘monthly’. Note, other results can be computed -later with archived results.

        • +later with archived results.

      -
    Returns:

    None

    -
    +
    +
    Returns
    +

    None

    +
    +

    @@ -1404,57 +1358,74 @@

    OptimizationResult

    Select helper functions

    +
    +

    load_data

    +
    +
    +prms_python.load_data(data_file)[source]
    +

    Read the data file and load into a datetime indexed Pandas dataframe object.

    +
    +
    Parameters
    +

    data_file (str) – data file path

    +
    +
    Returns
    +

    +
    Pandas dataframe of input time series data

    from data file with datetime index

    +
    +
    +

    +
    +
    Return type
    +

    df (pandas.DataFrame)

    +
    +
    +
    + +
    -

    load_statvar

    +

    load_statvar

    -prms_python.load_statvar(statvar_file)[source]
    +prms_python.load_statvar(statvar_file)[source]

    Read the statvar file and load into a datetime indexed Pandas dataframe object

    - --- - - - - - -
    Parameters:statvar_file (str) – statvar file path
    Returns:
    -
    (pandas.DataFrame) Pandas DataFrame of PRMS variables date indexed
    -
    from statvar file
    +
    +
    Parameters
    +

    statvar_file (str) – statvar file path

    +
    +
    Returns
    +

    +
    (pandas.DataFrame) Pandas DataFrame of PRMS variables date indexed

    from statvar file

    +
    +
    +

    +
    -
    -

    modify_params

    +

    modify_params

    -prms_python.modify_params(params_in, params_out, param_mods=None)[source]
    +prms_python.modify_params(params_in, params_out, param_mods=None)[source]

    Given a parameter file in and a dictionary of param_mods, write modified parameters to params_out.

    - --- - - - - - - - - -
    Parameters:
      -
    • params_in (str) – location on disk of the base parameter file
      • -
    • params_out (str) – location on disk where the modified parameters will -be written
      • +
      +
      Parameters
      +
        +

      • params_in (str) – location on disk of the base parameter file

        • +

      • params_out (str) – location on disk where the modified parameters will +be written

      -
    Keyword Arguments:
     

    param_mods (dict) – param name-keyed, param modification function-valued

    -
    Returns:

    None

    -
    +
    +
    Keyword Arguments
    +

    param_mods (dict) – param name-keyed, param modification function-valued

    +
    +
    Returns
    +

    None

    +
    +

    Example

    Below we modify the monthly jh_coef parameter by increasing it 10% for every month,

    @@ -1473,10 +1444,10 @@

    modify_params
    -

    resample_param

    +

    resample_param

    -prms_python.optimizer.resample_param(params, param_name, how='uniform', mu_factor=1, noise_factor=0.1)[source]
    +prms_python.optimizer.resample_param(params, param_name, how='uniform', mu_factor=1, noise_factor=0.1)[source]

    Resample PRMS parameter by shifting all values by a constant that is taken from a uniform distribution, where the range of the uniform values is equal to the difference between the min and max of the allowable @@ -1489,64 +1460,55 @@

    resample_paramnoise_factor.

    - --- - - - - - - - - - - - - -
    Parameters:
    Keyword Arguments:
     
      -
    • how (str) – distribution to resample parameters from in the case + +
      Keyword Arguments
      +
        +

      • how (str) – distribution to resample parameters from in the case that each parameter element can be resampled (len <=366) -Currently works for uniform and normal distributions.

        • -
      • noise_factor (float) – factor to multiply parameter range by, +Currently works for uniform and normal distributions.

        • +

      • noise_factor (float) – factor to multiply parameter range by, use the result as the standard deviation for the normal rand. variable used to add element wise noise. i.e. higher -noise_factor will result in higher variance. Must be > 0.

        • +noise_factor will result in higher variance. Must be > 0.

      -
    Returns:

    ndarray of param after resampling

    -
    Return type:

    ret (numpy.ndarry)

    -
    Raises:
      -
    • KeyError – if param_name not a valid parameter name
      • -
    • ValueError – if the parameter range has not been set in -Optimizer.param_ranges
      • + +
      Returns
      +

      ndarray of param after resampling

      +
      +
      Return type
      +

      ret (numpy.ndarry)

      +
      +
      Raises
      +
        +

      • KeyError – if param_name not a valid parameter name

        • +

      • ValueError – if the parameter range has not been set in + Optimizer.param_ranges

      -
    +

    +
    -

    nash_sutcliffe

    +

    nash_sutcliffe

    -prms_python.nash_sutcliffe(observed, modeled)[source]
    +prms_python.nash_sutcliffe(observed, modeled)[source]

    Calculates the Nash-Sutcliffe Goodness-of-fit

    - --- - - - -
    Parameters:
      -
    • observed (numpy.ndarray) – historic observational data
      • -
    • modeled (numpy.ndarray) – model output with matching time index
      • +
      +
      Parameters
      +
      -
    +
    +
    @@ -1555,9 +1517,9 @@

    nash_sutcliffe

    Indices and tables

    @@ -1581,7 +1543,7 @@

    Indices and tables

    @@ -1600,6 +1562,7 @@

    Navigation

  • Indices and tables
    • +
  • Citation
    • @@ -1607,21 +1570,36 @@

    Related Topics

    + + +

    Donate/support

    + + + +

    + +Donate + +

    + + + + +
    @@ -1630,8 +1608,8 @@

    Quick search

    ©2016, John Volk and Matthew Turner. | - Powered by Sphinx 1.7.6 - & Alabaster 0.7.11 + Powered by Sphinx 2.2.0 + & Alabaster 0.7.12 | + - - + Command-Line Interface — PRMS-Python v1.0.0 + - + + + + + @@ -123,7 +126,7 @@

    nash_sutcliffe_

    Open nash-sutcliffe.pdf and you should see something just like this:

    -
    +
    _images/nash-sutcliffe.png
    @@ -150,7 +153,7 @@

    nash_sutcliffe_

    -

    @@ -168,6 +171,7 @@

    Navigation

  • API Reference
  • Indices and tables
    • +
  • Citation
    • @@ -180,17 +184,31 @@

    Related Topics

    + + +

    Donate/support

    + + + +

    + +Donate + +

    + + + + +
    @@ -199,8 +217,8 @@

    Quick search

    ©2016, John Volk and Matthew Turner. | - Powered by Sphinx 1.7.6 - & Alabaster 0.7.11 + Powered by Sphinx 2.2.0 + & Alabaster 0.7.12 | + - - + Index — PRMS-Python v1.0.0 + - + + + + + @@ -82,7 +85,7 @@

    D

    - + Donate

    @@ -116,7 +116,7 @@

    All modules for which code is available

    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 diff --git a/docs/build/html/_modules/prms_python/data.html b/docs/build/html/_modules/prms_python/data.html index 5eca28c..d92f252 100644 --- a/docs/build/html/_modules/prms_python/data.html +++ b/docs/build/html/_modules/prms_python/data.html @@ -85,7 +85,7 @@

    Donate/support

    - + Donate

    @@ -375,7 +375,7 @@

    Source code for prms_python.data
    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 diff --git a/docs/build/html/_modules/prms_python/optimizer.html b/docs/build/html/_modules/prms_python/optimizer.html index cc753f4..53f7dbd 100644 --- a/docs/build/html/_modules/prms_python/optimizer.html +++ b/docs/build/html/_modules/prms_python/optimizer.html @@ -85,7 +85,7 @@

    Donate/support

    - + Donate

    @@ -1000,7 +1000,7 @@

    Source code for prms_python.optimizer
    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 diff --git a/docs/build/html/_modules/prms_python/parameters.html b/docs/build/html/_modules/prms_python/parameters.html index d4a7c79..95859c7 100644 --- a/docs/build/html/_modules/prms_python/parameters.html +++ b/docs/build/html/_modules/prms_python/parameters.html @@ -85,7 +85,7 @@

    Donate/support

    - + Donate

    @@ -687,7 +687,7 @@

    Source code for prms_python.parameters
    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 diff --git a/docs/build/html/_modules/prms_python/scenario.html b/docs/build/html/_modules/prms_python/scenario.html index 4eef60c..625013a 100644 --- a/docs/build/html/_modules/prms_python/scenario.html +++ b/docs/build/html/_modules/prms_python/scenario.html @@ -85,7 +85,7 @@

    Donate/support

    - + Donate

    @@ -713,7 +713,7 @@

    Source code for prms_python.scenario
    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 diff --git a/docs/build/html/_modules/prms_python/simulation.html b/docs/build/html/_modules/prms_python/simulation.html index bbc8008..7ca97b9 100644 --- a/docs/build/html/_modules/prms_python/simulation.html +++ b/docs/build/html/_modules/prms_python/simulation.html @@ -85,7 +85,7 @@

    Donate/support

    - + Donate

    @@ -523,7 +523,7 @@

    Source code for prms_python.simulation
    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 diff --git a/docs/build/html/_modules/prms_python/util.html b/docs/build/html/_modules/prms_python/util.html index dbad43c..4b94ee1 100644 --- a/docs/build/html/_modules/prms_python/util.html +++ b/docs/build/html/_modules/prms_python/util.html @@ -85,7 +85,7 @@

    Donate/support

    - + Donate

    @@ -458,7 +458,7 @@

    Source code for prms_python.util
    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 diff --git a/docs/build/html/api.html b/docs/build/html/api.html index f175310..0862afd 100644 --- a/docs/build/html/api.html +++ b/docs/build/html/api.html @@ -91,7 +91,7 @@

    Donate/support

    - + Donate

    @@ -153,8 +153,8 @@

    Data
    Keyword Arguments
      -

    • base_file (str, optional) – path to standard PRMS data file

      • -

    • na_rep (int, optional) – how to represent missing values default = -999

      • +

    • base_file (str, optional) – path to standard PRMS data file

      • +

    • na_rep (int, optional) – how to represent missing values default = -999

    @@ -164,7 +164,7 @@

    Data
    Type
    -

    list

    +

    list

    @@ -175,7 +175,7 @@

    Data
    Type
    -

    tuple

    +

    tuple

    @@ -183,14 +183,14 @@

    Data

    Note

    If using the Data class to create a new data file, it is up to the user -to ensure that the metadata and pandas.DataFrame assigned are correct +to ensure that the metadata and pandas.DataFrame assigned are correct and compatible.

    property data_frame

    A property that gets and sets the climatic forcing data for a standard PRMS -climate input data file as a pandas.DataFrame.

    +climate input data file as a pandas.DataFrame.

    Example

    d is a Data instance, calling

    >>> d.data_frame
@@ -209,10 +209,10 @@ 
    Data
 
    Raises
    
 
      
-
    • ValueError – if attribute is accessed before either assigning a PRMS data
+
    • ValueError – if attribute is accessed before either assigning a PRMS data
     file on Data initialization or not assigning a compatabile
     date-indexed pandas.DataFrame of hydro-climate variables.
      • 
-
    • TypeError – if a data type other than pandas.DataFrame is assigned.
      • 
+
    • TypeError – if a data type other than pandas.DataFrame is assigned.
      •
    @@ -248,14 +248,14 @@

    Data
    Raises
      -

    • ValueError – if data in metadata is accessed before data is assigned, +

    • ValueError – if data in metadata is accessed before data is assigned, e.g. if accessed to write a PRMS data file from a Data instance that was initialized without a valid PRMS data file.

      • -

    • TypeError – if an object that is not a Python dictionary is assigned.

      • +

    • TypeError – if an object that is not a Python dictionary is assigned.

    Type
    -

    dict

    +

    dict

    @@ -270,7 +270,7 @@

    DataParameters
    Returns
    @@ -300,14 +300,14 @@

    Dataout_path.

    Parameters
    -

    out_path (str) – full path to save or copy the current PRMS data +

    out_path (str) – full path to save or copy the current PRMS data in PRMS text format.

    Returns

    None

    Raises
    -

    ValueError – if the write method is called without assigning either +

    ValueError – if the write method is called without assigning either an initial data (base_file) path or assigning correct metadata and data_frame properties.

    @@ -336,7 +336,7 @@

    Parameters
    Parameters
    -

    base_file (str) – path to PRMS parameters file

    +

    base_file (str) – path to PRMS parameters file

    @@ -345,7 +345,7 @@

    Parameters

    path to PRMS parameters file

    Type
    -

    str

    +

    str

    @@ -369,7 +369,7 @@

    Parameters
    Type
    -

    collections.OrderedDict

    +

    collections.OrderedDict

    @@ -396,7 +396,7 @@

    Parameters
    Type
    -

    dict

    +

    dict

    @@ -429,24 +429,24 @@

    ParametersJupyter notebook.

    Parameters
    -

    nrows (int) – The number of rows in the PRMS model grid for plotting +

    nrows (int) – The number of rows in the PRMS model grid for plotting spatial parameters. Will only work correctly for rectangular gridded models with HRU indices starting in the upper left cell moving left to right across columns and down across rows.

    Keyword Arguments
      -

    • which (str) – name of PRMS parameter to plot or ‘all’. If ‘all’ then +

    • which (str) – name of PRMS parameter to plot or ‘all’. If ‘all’ then the function will print 3 multipage pdfs, one for nhru dimensional parameters, one for nhru by monthly parameters, one for other parameters of length > 1, and one html file containing single valued parameters.

      • -

    • out_dir (str) – path to an output dir, default current directory

      • -

    • xlabel (str) – x label for plot(s)

      • -

    • ylabel (str) – y label for plot(s)

      • -

    • cbar_label (str) – label for colorbar on spatial plot(s)

      • -

    • title (str) – plot title

      • -

    • mpl_style (str, list) – name or list of names of matplotlib style sheets to +

    • out_dir (str) – path to an output dir, default current directory

      • +

    • xlabel (str) – x label for plot(s)

      • +

    • ylabel (str) – y label for plot(s)

      • +

    • cbar_label (str) – label for colorbar on spatial plot(s)

      • +

    • title (str) – plot title

      • +

    • mpl_style (str, list) – name or list of names of matplotlib style sheets to use for plot(s).

    @@ -490,7 +490,7 @@

    Parameters
    Parameters
    -

    out_name (str) – path to write Parameters data to PRMS text +

    out_name (str) – path to write Parameters data to PRMS text format.

    Returns
    @@ -523,9 +523,9 @@

    Simulation
    Keyword Arguments
      -

    • input_dir (str) – path to directory that contains control, parameter, +

    • input_dir (str) – path to directory that contains control, parameter, and data files for the simulation

      • -

    • simulation_dir (str) – directory path to bundle inputs and outputs

      • +

    • simulation_dir (str) – directory path to bundle inputs and outputs

    @@ -533,7 +533,7 @@

    Simulationsee Simulation.run()

    Raises
    -

    RuntimeError – if input directory does not contain a PRMS data, +

    RuntimeError – if input directory does not contain a PRMS data, parameters, and control file.

    @@ -548,8 +548,8 @@

    Simulation

    • data (Data) – Data object for simulation

    • parameters (Parameters) – Parameters object for simulation

      • -

    • control_path (str) – path to control file

      • -

    • simulation_dir (str) – path to directory where simulations will be +

    • control_path (str) – path to control file

      • +

    • simulation_dir (str) – path to directory where simulations will be run and where input and output will be stored. If it exists it will be overwritten.

    @@ -573,7 +573,7 @@

    Simulation
    Raises
    -

    TypeError – if data and parameters arguments are not of type +

    TypeError – if data and parameters arguments are not of type Data and Parameters

    @@ -592,7 +592,7 @@

    Simulation
    Keyword Arguments
    -

    prms_exec (str) – name of PRMS executable on $PATH or path to executable

    +

    prms_exec (str) – name of PRMS executable on $PATH or path to executable

    Examples

    @@ -675,7 +675,7 @@

    SimulationSeries
    Parameters
    -

    simulations (list or tuple) – list of Simulation objects +

    simulations (list or tuple) – list of Simulation objects to be run.

    @@ -733,7 +733,7 @@

    SimulationSeries
    Yields
    -

    dict –

    +

    dict –

    dictionary of paths to simulation directory,

    input, and output files.

    @@ -765,9 +765,9 @@

    SimulationSeries
    Keyword Arguments
      -

    • prms_exec (str) – name of PRMS executable on $PATH or path to +

    • prms_exec (str) – name of PRMS executable on $PATH or path to executable

      • -

    • nproc (int or None) – number of logical or physical processors +

    • nproc (int or None) – number of logical or physical processors for parrallel execution of PRMS simulations.

    @@ -778,7 +778,7 @@

    SimulationSeriesNote

    If nproc is not assigned the deault action is to use half of the available processecors on the machine using the Python -multiprocessing module.

    +multiprocessing module.

    @@ -795,22 +795,22 @@

    Scenariojson format +applied to each parameter. The metadata file is in json format making it conveniently read as a Python dictionary.

    Parameters
      -

    • base_dir (str) – path to directory that contains initial control, +

    • base_dir (str) – path to directory that contains initial control, parameter, and data files to be used for Scenario. The parameters file in base_dir will not be modifed instead will be copied to scenario_dir and them modified.

      • -

    • scenario_dir (str) – directory path to bundle inputs and outputs

      • -

    • title (str, optional) – title of Scenario, if given will be +

    • scenario_dir (str) – directory path to bundle inputs and outputs

      • +

    • title (str, optional) – title of Scenario, if given will be added to Scenario.metadata attribute as well as the metadata.json file in scenario_dir written after calling the Scenario.build() and Scenario.run() methods.

      • -

    • description (str, optional) – description of Scenario, also +

    • description (str, optional) – description of Scenario, also is added to Scenario.metadata as title.

    @@ -927,7 +927,7 @@

    ScenarioScenario (calling Scenario.run() ).

    Keyword Arguments
    -

    param_mod_funs (dict) – dictionary with parameter names as keys +

    param_mod_funs (dict) – dictionary with parameter names as keys and Python functions as values to apply to the names (key)

    Returns
    @@ -950,7 +950,7 @@

    ScenarioScenario instance.

    Keyword Arguments
    -

    prms_exec (str) – name of PRMS executable on $PATH or path to +

    prms_exec (str) – name of PRMS executable on $PATH or path to executable

    Returns
    @@ -961,7 +961,7 @@

    ScenarioScenario for full example

    Raises
    -

    RuntimeError – if the Scenario.build() method has not yet +

    RuntimeError – if the Scenario.build() method has not yet been called.

    @@ -982,8 +982,8 @@

    ScenarioSeries

    The ScenarioSeries’ build method creates a file structure under the series directory (scenarios_dir) where each subdirectory is -named with a uuid which can be later matched to its title using -the metadata in scenario_dir/series_metadata.json (see json). +named with a uuid which can be later matched to its title using +the metadata in scenario_dir/series_metadata.json (see json). In the future we may add a way for the user to access the results of the scenario simulations directly through the ScenarioSeries instance, but for now the results are written to disk. Therefore each @@ -993,16 +993,16 @@

    ScenarioSeries
    Parameters
      -

    • base_dir (str) – path to base inputs; ‘control’, ‘parameters’, +

    • base_dir (str) – path to base inputs; ‘control’, ‘parameters’, and ‘data’ must be present there

      • -

    • scenarios_dir (str) – directory where scenario data will be written +

    • scenarios_dir (str) – directory where scenario data will be written to; will be overwritten or created if it does not exist

    Keyword Arguments
      -

    • title (str, optional) – title of the ScenarioSeries instance

      • -

    • description (str, optional) – description of the ScenarioSeries +

    • title (str, optional) – title of the ScenarioSeries instance

      • +

    • description (str, optional) – description of the ScenarioSeries instance

    @@ -1016,7 +1016,7 @@

    ScenarioSeriesScenarioSeries.build().

    Type
    -

    dict

    +

    dict

    @@ -1028,7 +1028,7 @@

    ScenarioSeriesScenarioSeries.build().

    Type
    -

    list

    +

    list

    @@ -1063,7 +1063,7 @@

    ScenarioSeriesScenario subdirectories as shown below.

    Parameters
    -

    scenarios_list (list) – list of dictionaries with key-value +

    scenarios_list (list) – list of dictionaries with key-value pairs being parameter-function definition pairs or title-title string or description-description string.

    @@ -1132,12 +1132,12 @@

    ScenarioSeries
    Parameters
      -

    • base_directory (str) – directory that contains model input files

      • +

    • base_directory (str) – directory that contains model input files

    • parameters_iter (list of dicts) – list of dictionaries for each Scenario as described in Scenario and ScenarioSeries.build().

      • -

    • title (str) – title for group of scenarios

      • -

    • description (str) – description for group of scenarios

      • +

    • title (str) – title for group of scenarios

      • +

    • description (str) – description for group of scenarios

    Returns
    @@ -1154,11 +1154,11 @@

    ScenarioSeries
    Keyword Arguments
      -

    • prms_exec (str) – name of PRMS executable on $PATH or path to +

    • prms_exec (str) – name of PRMS executable on $PATH or path to executable. Default = ‘prms’

      • -

    • nproc (int or None) – number of processceors available to +

    • nproc (int or None) – number of processceors available to parallelize PRMS simulations, if None (default) then use -half of what the multiprocessing detects on the +half of what the multiprocessing detects on the machine.

    @@ -1212,7 +1212,7 @@

    ScenarioSeries}

    -

    Therefore one can use the json file to track between UUID’s and +

    Therefore one can use the json file to track between UUID’s and individual scenario titles. The json files are read as a Python dictionary which makes them particularly convenient. The contents of an individual scenarios metadata.json file included a string @@ -1279,25 +1279,25 @@

    Optimizer
    Parameters
      -

    • reference_path (str) – path to measured data for optimization

      • -

    • param_names (list) – list of parameter names to resample

      • -

    • statvar_name (str) – name of statisical variable output name for +

    • reference_path (str) – path to measured data for optimization

      • +

    • param_names (list) – list of parameter names to resample

      • +

    • statvar_name (str) – name of statisical variable output name for optimization

      • -

    • stage (str) – custom name of optimization stage e.g. ‘ddsolrad’

      • +

    • stage (str) – custom name of optimization stage e.g. ‘ddsolrad’

    Keyword Arguments
      -

    • n_sims (int) – number of simulations to conduct +

    • n_sims (int) – number of simulations to conduct parameter optimization/uncertaitnty analysis.

      • -

    • method (str) – resampling method for parameters (normal or uniform)

      • -

    • mu_factor (float) – coefficient to scale mean of the parameter(s) +

    • method (str) – resampling method for parameters (normal or uniform)

      • +

    • mu_factor (float) – coefficient to scale mean of the parameter(s) to resample from when using the normal distribution to resample i.e. a value of 1.5 will sample from a normal rv with mean 50% higher than the original parameter mean

      • -

    • noise_factor (float) – scales the variance of noise to add to +

    • noise_factor (float) – scales the variance of noise to add to parameter values when using normal rv (method=’normal’)

      • -

    • nproc (int) – number of processors available to run PRMS simulations

      • +

    • nproc (int) – number of processors available to run PRMS simulations

    Returns
    @@ -1317,21 +1317,21 @@

    Optimizer
    Keyword Arguments
      -

    • freq (str) – frequency of time series plots, value can be ‘daily’ +

    • freq (str) – frequency of time series plots, value can be ‘daily’ or ‘monthly’ for solar radiation

      • -

    • method (str) – ‘time_series’ for time series sub plot of each +

    • method (str) – ‘time_series’ for time series sub plot of each simulation alongside measured radiation. Another choice is ‘correlation’ which plots each measured daily solar radiation value versus the corresponding simulated variable as subplots one for each simulation in the optimization. With coefficients of determiniationi i.e. square of pearson correlation coef.

      • -

    • plot_vars (str) – what to plot alongside simulated srad: +

    • plot_vars (str) – what to plot alongside simulated srad: ‘meas’: plot simulated along with measured swrad ‘orig’: plot simulated along with the original simulated swrad ‘both’: plot simulated, with original simulation and measured

      • -

    • plot_1to1 (bool) – if True plot one to one line on correlation +

    • plot_1to1 (bool) – if True plot one to one line on correlation scatter plot, otherwise exclude.

      • -

    • return_fig (bool) – flag whether to return matplotlib figure

      • +

    • return_fig (bool) – flag whether to return matplotlib figure

    Returns
    @@ -1407,12 +1407,12 @@

    OptimizationResult
    Keyword Arguments
    @@ -1465,7 +1465,7 @@

    load_statvar
    Parameters
    -

    statvar_file (str) – statvar file path

    +

    statvar_file (str) – statvar file path

    Returns

    @@ -1488,13 +1488,13 @@

    modify_params
    Parameters
      -

    • params_in (str) – location on disk of the base parameter file

      • -

    • params_out (str) – location on disk where the modified parameters will +

    • params_in (str) – location on disk of the base parameter file

      • +

    • params_out (str) – location on disk where the modified parameters will be written

    Keyword Arguments
    -

    param_mods (dict) – param name-keyed, param modification function-valued

    +

    param_mods (dict) – param name-keyed, param modification function-valued

    Returns

    None

    @@ -1538,15 +1538,15 @@

    resample_paramParameters
    Keyword Arguments
      -

    • how (str) – distribution to resample parameters from in the case +

    • how (str) – distribution to resample parameters from in the case that each parameter element can be resampled (len <=366) Currently works for uniform and normal distributions.

      • -

    • noise_factor (float) – factor to multiply parameter range by, +

    • noise_factor (float) – factor to multiply parameter range by, use the result as the standard deviation for the normal rand. variable used to add element wise noise. i.e. higher noise_factor will result in higher variance. Must be > 0.

      • @@ -1560,8 +1560,8 @@

      resample_param
      Raises
        -

      • KeyError – if param_name not a valid parameter name

        • -

      • ValueError – if the parameter range has not been set in +

      • KeyError – if param_name not a valid parameter name

        • +

      • ValueError – if the parameter range has not been set in Optimizer.param_ranges

      @@ -1578,8 +1578,8 @@

      nash_sutcliffe
      Parameters

    @@ -1605,7 +1605,7 @@

    Indices and tables

    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 diff --git a/docs/build/html/changelog.html b/docs/build/html/changelog.html index 9b92a0d..cc2a9f1 100644 --- a/docs/build/html/changelog.html +++ b/docs/build/html/changelog.html @@ -90,7 +90,7 @@

    Donate/support

    - + Donate

    @@ -165,7 +165,7 @@

    Version 0.1.0

    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 diff --git a/docs/build/html/citation.html b/docs/build/html/citation.html index ee32f1d..ad3f97e 100644 --- a/docs/build/html/citation.html +++ b/docs/build/html/citation.html @@ -87,7 +87,7 @@

    Donate/support

    - + Donate

    @@ -120,7 +120,7 @@

    Citation

    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 diff --git a/docs/build/html/cli.html b/docs/build/html/cli.html index 94e98e1..b7c9282 100644 --- a/docs/build/html/cli.html +++ b/docs/build/html/cli.html @@ -90,7 +90,7 @@

    Donate/support

    - + Donate

    @@ -214,7 +214,7 @@

    nash_sutcliffe_

    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index 783a9f7..b1fa5b4 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -84,7 +84,7 @@

    Donate/support

    - + Donate

    @@ -313,7 +313,7 @@

    W

    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 diff --git a/docs/build/html/index.html b/docs/build/html/index.html index 1162400..7722485 100644 --- a/docs/build/html/index.html +++ b/docs/build/html/index.html @@ -85,7 +85,7 @@

    Donate/support

    - + Donate

    @@ -110,8 +110,8 @@

    PRMS-PythonView on GitHub

    PRMS-Python gives direct access to PRMS input parameters, climate forcing data, and output variables– interfacing PRMS data structures with powerful -scientific Python data structures particularly the numpy.ndarray and -pandas.DataFrame objects. The module contains routines to systematically +scientific Python data structures particularly the numpy.ndarray and +pandas.DataFrame objects. The module contains routines to systematically modify input parameters using user defined methods or a Monte Carlo parameter resampler; such routines then run a series of PRMS simulations in parralel. Metadata is also created and tracked along with each PRMS simulation’s inputs @@ -180,7 +180,7 @@

    Usage

    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 diff --git a/docs/build/html/search.html b/docs/build/html/search.html index bec7eec..a2c0e43 100644 --- a/docs/build/html/search.html +++ b/docs/build/html/search.html @@ -78,7 +78,7 @@

    Donate/support

    - + Donate

    @@ -126,7 +126,7 @@

    Search

    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 diff --git a/docs/build/html/tutorial.html b/docs/build/html/tutorial.html index 2b86a96..4e51743 100644 --- a/docs/build/html/tutorial.html +++ b/docs/build/html/tutorial.html @@ -95,7 +95,7 @@

    Donate/support

    - + Donate

    @@ -125,7 +125,7 @@

    Data

    A PRMS data file holds time series variables that are used as input for running PRMS, e.g. daily air temperature and precipitation. Being tabular and date-indexed -the data file is well represented and managed as a pandas.DataFrame. +the data file is well represented and managed as a pandas.DataFrame. A common paractice in hydrologic modeling is to evaluate hydrologic response to multiple climate change scenarios. The Data class offers function-based modification of time series variable/s so that the user can quickly create new @@ -147,7 +147,7 @@

    Datad.write('test/data/temp_plus2_data')

    -

    The Data.data_frame property is the pandas.DataFrame representation +

    The Data.data_frame property is the pandas.DataFrame representation of hydro-climatic variables found in a PRMS data file. As such it can also be assigned within Python. However in order to create a data file from scratch using the Data object one must also assign the Data.metadata property. @@ -697,7 +697,7 @@

    load_data
    - ©2016-2018, John Volk and Matthew Turner. + ©2016-2020, John Volk and Matthew Turner. | Powered by Sphinx 2.2.0 From fb49e7bb9796e1f0b5932212afb574543cfb000a Mon Sep 17 00:00:00 2001 From: John Volk Date: Tue, 4 Jun 2024 10:51:10 -0400 Subject: [PATCH 08/11] add nan rep for plot * allow user to set null value for parameter plots for masking * update for pandas deprecation error for setting values to a dataframe --- prms_python/parameters.py | 11 ++++++++--- prms_python/util.py | 2 +- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/prms_python/parameters.py b/prms_python/parameters.py index 77a8ad6..f167bdc 100644 --- a/prms_python/parameters.py +++ b/prms_python/parameters.py @@ -144,7 +144,8 @@ def write(self, out_name): ['vartype']])]) def plot(self, nrows, which='all', out_dir=None, xlabel=None,\ - ylabel=None, cbar_label=None, title=None, mpl_style=None): + ylabel=None, cbar_label=None, title=None, mpl_style=None, + na_val=-99999): """ Versatile method that plots most parameters in a standard PRMS parameter file assuming the PRMS model was built on a uniform spatial grid. @@ -177,6 +178,7 @@ def plot(self, nrows, which='all', out_dir=None, xlabel=None,\ title (str): plot title mpl_style (str, list): name or list of names of matplotlib style sheets to use for plot(s). + na_val (float, int): default -99999. Value to mask in plots. Returns: None @@ -235,7 +237,10 @@ def plot(self, nrows, which='all', out_dir=None, xlabel=None,\ try: plt.figure() ax = plt.gca() - im = ax.imshow(params['{}'.format(p)].reshape(nrows,ncols), origin='upper') + + d = np.where(params[p] == na_val, np.nan, params[p]) + im = ax.imshow(d.reshape(nrows,ncols), origin='upper') + # origin upper- assumes indices of parameters starts in upper left divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) @@ -290,7 +295,7 @@ def plot(self, nrows, which='all', out_dir=None, xlabel=None,\ df = pd.DataFrame() df.index.name = 'parameter' for p in p_names: - df.set_value(p, 'value', params[p]) + df.at[p, 'value'] = params[p] df.to_html(OPJ(out_dir,'single_valued_params.html')) ################################################################ # plot single parameter, in case of nhru by monthly param, diff --git a/prms_python/util.py b/prms_python/util.py index c7fa14b..cf5dc1f 100644 --- a/prms_python/util.py +++ b/prms_python/util.py @@ -192,7 +192,7 @@ def load_statvar(statvar_file): skiprows = n_statvars+1 df = pd.read_csv( statvar_file, delim_whitespace=True, skiprows=skiprows, - header=-1, na_values=[missing_value] + header=None, na_values=[missing_value] ) # apply correct header names using metadata retrieved from file From ed088a46d0592268a891aed147a3fc25585b8d57 Mon Sep 17 00:00:00 2001 From: John Volk Date: Mon, 2 Sep 2024 10:12:31 -0400 Subject: [PATCH 09/11] update pandas reader call for deprecation error --- prms_python/data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/prms_python/data.py b/prms_python/data.py index 2e2f831..ab178ec 100644 --- a/prms_python/data.py +++ b/prms_python/data.py @@ -171,7 +171,7 @@ def data_frame(self): elif isinstance(self._data_frame, pd.DataFrame): return self._data_frame - df = pd.read_csv(self.base_file, header = -1, skiprows = self.metadata['data_startline'], + df = pd.read_csv(self.base_file, header = None, skiprows = self.metadata['data_startline'], delim_whitespace = True, na_values = [self.na_rep]) ## read data file df.columns = Data.date_header + self.metadata['data_variables'] date = pd.Series(pd.to_datetime(df.year * 10000 + df.month * 100 +\ From 5f9922ce6e4c1bb0355f64add26e8d4137c638e3 Mon Sep 17 00:00:00 2001 From: John Volk Date: Thu, 5 Sep 2024 14:45:58 -0400 Subject: [PATCH 10/11] specify line deliminator for data.write method --- prms_python/data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/prms_python/data.py b/prms_python/data.py index ab178ec..a0cbeb1 100644 --- a/prms_python/data.py +++ b/prms_python/data.py @@ -259,5 +259,5 @@ def write(self, out_path): with open(out_path,'w') as outf: # write comment header then data outf.write(self._metadata['text_before_header']) df.to_csv(outf, sep=' ', header=None,\ - index=False, na_rep=self.na_rep) + index=False, na_rep=self.na_rep, lineterminator='\n') From 077e0691ce9882872ce1fe61fbd3025d7196da57 Mon Sep 17 00:00:00 2001 From: John Volk Date: Thu, 3 Oct 2024 13:35:29 -0400 Subject: [PATCH 11/11] add nan rep for nhru x nmonths params plots --- prms_python/parameters.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/prms_python/parameters.py b/prms_python/parameters.py index f167bdc..c1608a1 100644 --- a/prms_python/parameters.py +++ b/prms_python/parameters.py @@ -240,7 +240,6 @@ def plot(self, nrows, which='all', out_dir=None, xlabel=None,\ d = np.where(params[p] == na_val, np.nan, params[p]) im = ax.imshow(d.reshape(nrows,ncols), origin='upper') - # origin upper- assumes indices of parameters starts in upper left divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) @@ -261,7 +260,10 @@ def plot(self, nrows, which='all', out_dir=None, xlabel=None,\ for i in range(12): #month plt.figure() ax = plt.gca() - im = ax.imshow(params['{}'.format(p)][i].reshape(nrows, ncols), origin='upper') + + d = params['{}'.format(p)][i].reshape(nrows, ncols) + d = np.where(d == na_val, np.nan, d) + im = ax.imshow(d, origin='upper') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) plt.colorbar(im, cax=cax)