From d1fe06c15220d99d2795d9578091370a62621996 Mon Sep 17 00:00:00 2001 From: forestbat Date: Fri, 24 Jun 2022 16:56:19 +0800 Subject: [PATCH 01/19] add some files --- .github/workflows/publish-to-pypi.yml | 36 +++++++++++++++++++++++++++ .readthedocs.yaml | 23 +++++++++++++++++ MANIFEST.in | 1 + hydromodel/example/__init__.py | 0 setup.py | 23 +++++++++++++++++ test/__init__.py | 0 6 files changed, 83 insertions(+) create mode 100644 .github/workflows/publish-to-pypi.yml create mode 100644 .readthedocs.yaml create mode 100644 MANIFEST.in create mode 100644 hydromodel/example/__init__.py create mode 100644 setup.py create mode 100644 test/__init__.py diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml new file mode 100644 index 0000000..11a4285 --- /dev/null +++ b/.github/workflows/publish-to-pypi.yml @@ -0,0 +1,36 @@ +name: Publish Python distributions to PyPI + +on: push + +jobs: + build-n-publish: + name: Build and publish Python distributions to PyPI + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@master + - name: Set up Python 3.9 + uses: actions/setup-python@v1 + with: + python-version: 3.9 + + - name: Install pypa/build + run: >- + python -m + pip install + build + --user + - name: Build a binary wheel and a source tarball + run: >- + python -m + build + --sdist + --wheel + --outdir dist/ + . + + - name: Publish distribution to PyPI + if: startsWith(github.ref, 'refs/tags') + uses: pypa/gh-action-pypi-publish@master + with: + password: ${{ secrets.PYPI_API_TOKEN }} diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..8b0c447 --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,23 @@ +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the version of Python and other tools you might need +build: + os: ubuntu-20.04 + tools: + python: "3.9" + # You can also specify other tool versions: + # nodejs: "16" + # rust: "1.55" + # golang: "1.17" + +# Build documentation in the docs/ directory with Sphinx +sphinx: + configuration: docs/source/conf.py + +# If using Sphinx, optionally build your docs in additional formats such as PDF +# formats: +# - pdf \ No newline at end of file diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000..57fdc18 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1 @@ +recursive-include XXX *.csv *.txt # 打包需包含csv、txt为后缀的文件；XXX为包名 \ No newline at end of file diff --git a/hydromodel/example/__init__.py b/hydromodel/example/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..29b5c04 --- /dev/null +++ b/setup.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- + +from setuptools import setup, find_packages + +setup( + name="", # 输入项目名称 + version="", # 输入版本号 + keywords=[""], # 输入关键词 + description="", # 输入概述 + long_description="", # 输入描述 + + url="", # 输入项目Github仓库的链接 + author="", # 输入作者名字 + author_email="", # 输入作者邮箱 + license="", # 此为声明文件，一般填写 MIT_license + + packages=find_packages(), + include_package_data=True, + platforms="any", + install_requires=[""], # 输入项目所用的包 + python_requires='>= ', # Python版本要求 +) diff --git a/test/__init__.py b/test/__init__.py new file mode 100644 index 0000000..e69de29 From 615aaf7f44ebcff5050d6cae96ab0b29d7afc14a Mon Sep 17 00:00:00 2001 From: forestbat Date: Sun, 24 Sep 2023 16:00:48 +0800 Subject: [PATCH 02/19] add wangjingyi's code --- requirements.txt | 13 + test/runxaj.yaml | 46 ++ test/test_xaj_bmi.py | 308 ++++++++++ xaj/__init__.py | 0 xaj/calibrate_ga_xaj_bmi.py | 381 ++++++++++++ xaj/calibrate_sceua_xaj_bmi.py | 209 +++++++ xaj/configuration.py | 27 + xaj/constant_unit.py | 63 ++ xaj/dockerfile | 13 + xaj/params.py | 122 ++++ xaj/xaj_bmi.py | 292 +++++++++ xaj/xajmodel.py | 1060 ++++++++++++++++++++++++++++++++ 12 files changed, 2534 insertions(+) create mode 100644 requirements.txt create mode 100644 test/runxaj.yaml create mode 100644 test/test_xaj_bmi.py create mode 100644 xaj/__init__.py create mode 100644 xaj/calibrate_ga_xaj_bmi.py create mode 100644 xaj/calibrate_sceua_xaj_bmi.py create mode 100644 xaj/configuration.py create mode 100644 xaj/constant_unit.py create mode 100644 xaj/dockerfile create mode 100644 xaj/params.py create mode 100644 xaj/xaj_bmi.py create mode 100644 xaj/xajmodel.py diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..57a82b9 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,13 @@ +python=3.10 +ipykernel +numpy +numba +pandas +scikit-learn +deap +spotpy=1.5.14 +seaborn +tqdm +pytest +hydrodataset +bmipy \ No newline at end of file diff --git a/test/runxaj.yaml b/test/runxaj.yaml new file mode 100644 index 0000000..ae57a87 --- /dev/null +++ b/test/runxaj.yaml @@ -0,0 +1,46 @@ +# xaj model configuration +# The current model runs on the daily time step + start_time_str: "1990-01-01" + end_time_str: "2009-12-31" + time_units: "days" + # The current model runs on the hourly time step + # basin_area: "66400" + # start_time_str: "2021-06-01 00:00:00" + # end_time_str: "2021-08-31 23:00:00" + # time_units: "hours" +# initial condition + forcing_data: "/hydromodel/example/01013500_lump_p_pe_q.txt" + json_file: "/hydromodel/example/data_info.json" + npy_file: "/hydromodel/example/basins_lump_p_pe_q.npy" + # forcing_file: "/home/wangjingyi/code/hydro-model-xaj/scripts/尼尔基降雨.csv" + warmup_length: 360 + basin_area: 2252.7 + + + train_period: ["1990-01-01", "2000-12-31"] + test_period: ["2001-01-01", "2009-12-31"] + period: ["1990-01-01", "2009-12-31"] + cv_fold: 1 + algorithm: "SCE_UA" + +#model_info + model_name: "xaj_mz" + source_type: "sources" + source_book: "HF" + +#algorithm_SCE_UA + # algorithm_name: "SCE_UA" + # random_seed: 1234 + # rep: 2 + # ngs: 2 + # kstop: 1 + # peps: 0.001 + # pcento: 0.001 +#algorithm_GA + algorithm_name: "GA" + random_seed: 1234 + run_counts: 3 + pop_num: 50 + cross_prob: 0.5 + mut_prob: 0.5 + save_freq: 1 \ No newline at end of file diff --git a/test/test_xaj_bmi.py b/test/test_xaj_bmi.py new file mode 100644 index 0000000..0ee679b --- /dev/null +++ b/test/test_xaj_bmi.py @@ -0,0 +1,308 @@ +import logging + +import definitions +from xaj.configuration import read_config +from xaj.xaj_bmi import xajBmi + +logging.basicConfig(level=logging.INFO) + +import pandas as pd +import os +from pathlib import Path +import numpy as np +import fnmatch +import socket +from datetime import datetime + +from hydromodel.utils import hydro_utils +from hydromodel.data.data_preprocess import ( + cross_valid_data, + split_train_test, +) +from xaj.calibrate_sceua_xaj_bmi import calibrate_by_sceua +from xaj.calibrate_ga_xaj_bmi import ( + calibrate_by_ga, + show_ga_result, +) +from hydromodel.visual.pyspot_plots import show_calibrate_result, show_test_result +from hydromodel.data.data_postprocess import ( + renormalize_params, + read_save_sceua_calibrated_params, + save_streamflow, + summarize_metrics, + summarize_parameters, +) +from hydromodel.utils import hydro_constant + + +def test_bmi(): + ''' + model = xajBmi() + print(model.get_component_name()) + model.initialize("runxaj.yaml") + print("Start time:", model.get_start_time()) + print("End time:", model.get_end_time()) + print("Current time:", model.get_current_time()) + print("Time step:", model.get_time_step()) + print("Time units:", model.get_time_units()) + print(model.get_input_var_names()) + print(model.get_output_var_names()) + + discharge = [] + ET = [] + time = [] + while model.get_current_time() <= model.get_end_time(): + time.append(model.get_current_time()) + model.update() + + discharge=model.get_value("discharge") + ET=model.get_value("ET") + + results = pd.DataFrame({ + 'discharge': discharge.flatten(), + 'ET': ET.flatten(), + }) + results.to_csv('/home/wangjingyi/code/hydro-model-xaj/scripts/xaj.csv') + model.finalize() + ''' + # 模型率定 + config = read_config(os.path.relpath("runxaj.yaml")) + forcing_data = Path(str(definitions.ROOT_DIR) + str(config['forcing_data'])) + train_period = config['train_period'] + test_period = config['test_period'] + # period = config['period'] + json_file = Path(str(definitions.ROOT_DIR) + str(config['json_file'])) + npy_file = Path(str(definitions.ROOT_DIR) + str(config['npy_file'])) + cv_fold = config['cv_fold'] + warmup_length = config['warmup_length'] + # model_info + model_name = config['model_name'] + source_type = config['source_type'] + source_book = config['source_book'] + # algorithm + algorithm_name = config['algorithm_name'] + + if not (cv_fold > 1): + # no cross validation + periods = np.sort( + [train_period[0], train_period[1], test_period[0], test_period[1]] + ) + if cv_fold > 1: + cross_valid_data(json_file, npy_file, periods, warmup_length, cv_fold) + else: + split_train_test(json_file, npy_file, train_period, test_period) + + kfold = [ + int(f_name[len("data_info_fold"): -len("_test.json")]) + for f_name in os.listdir(os.path.dirname(forcing_data)) + if fnmatch.fnmatch(f_name, "*_fold*_test.json") + ] + kfold = np.sort(kfold) + for fold in kfold: + print(f"Start to calibrate the {fold}-th fold") + train_data_info_file = os.path.join( + os.path.dirname(forcing_data), f"data_info_fold{str(fold)}_train.json" + ) + train_data_file = os.path.join( + os.path.dirname(forcing_data), f"basins_lump_p_pe_q_fold{str(fold)}_train.npy" + ) + test_data_info_file = os.path.join( + os.path.dirname(forcing_data), f"data_info_fold{str(fold)}_test.json" + ) + test_data_file = os.path.join( + os.path.dirname(forcing_data), f"basins_lump_p_pe_q_fold{str(fold)}_test.npy" + ) + if ( + os.path.exists(train_data_info_file) is False + or os.path.exists(train_data_file) is False + or os.path.exists(test_data_info_file) is False + or os.path.exists(test_data_file) is False + ): + raise FileNotFoundError( + "The data files are not found, please run datapreprocess4calibrate.py first." + ) + data_train = hydro_utils.unserialize_numpy(train_data_file) + data_test = hydro_utils.unserialize_numpy(test_data_file) + data_info_train = hydro_utils.unserialize_json_ordered(train_data_info_file) + data_info_test = hydro_utils.unserialize_json_ordered(test_data_info_file) + current_time = datetime.now().strftime("%b%d_%H-%M-%S") + # one directory for one model + one hyperparam setting and one basin + save_dir = os.path.join(os.path.dirname(forcing_data), current_time + "_" + socket.gethostname() + "_fold" + str(fold)) + if algorithm_name == "SCE_UA": + random_seed = config['random_seed'] + rep = config['rep'] + ngs = config['ngs'] + kstop = config['kstop'] + peps = config['peps'] + pcento = config['pcento'] + for i in range(len(data_info_train["basin"])): + basin_id = data_info_train["basin"][i] + basin_area = data_info_train["area"][i] + # one directory for one model + one hyperparam setting and one basin + spotpy_db_dir = os.path.join( + save_dir, + basin_id, + ) + + if not os.path.exists(spotpy_db_dir): + os.makedirs(spotpy_db_dir) + db_name = os.path.join(spotpy_db_dir, "SCEUA_" + model_name) + sampler = calibrate_by_sceua( + data_train[:, i: i + 1, 0:2], + data_train[:, i: i + 1, -1:], + db_name, + warmup_length=warmup_length, + model={ + 'name': model_name, + 'source_type': source_type, + 'source_book': source_book + }, + algorithm={ + 'name': algorithm_name, + 'random_seed': random_seed, + 'rep': rep, + 'ngs': ngs, + 'kstop': kstop, + 'peps': peps, + 'pcento': pcento + }, + ) + + show_calibrate_result( + sampler.setup, + db_name, + warmup_length=warmup_length, + save_dir=spotpy_db_dir, + basin_id=basin_id, + train_period=data_info_train["time"], + basin_area=basin_area, + ) + params = read_save_sceua_calibrated_params( + basin_id, spotpy_db_dir, db_name + ) + + model = xajBmi() + model.initialize(os.path.relpath("runxaj.yaml"), params, data_test[:, i: i + 1, 0:2]) + j = 0 + while j <= len(data_info_test["time"]): + model.update() + j += 1 + q_sim = model.get_value("discharge") + + qsim = hydro_constant.convert_unit( + q_sim, + unit_now="mm/day", + unit_final=hydro_constant.unit["streamflow"], + basin_area=basin_area, + ) + + qobs = hydro_constant.convert_unit( + data_test[warmup_length:, i: i + 1, -1:], + unit_now="mm/day", + unit_final=hydro_constant.unit["streamflow"], + basin_area=basin_area, + ) + test_result_file = os.path.join( + spotpy_db_dir, + "test_qsim_" + model_name + "_" + str(basin_id) + ".csv", + ) + pd.DataFrame(qsim.reshape(-1, 1)).to_csv( + test_result_file, + sep=",", + index=False, + header=False, + ) + test_date = pd.to_datetime( + data_info_test["time"][warmup_length:] + ).values.astype("datetime64[D]") + show_test_result( + basin_id, test_date, qsim, qobs, save_dir=spotpy_db_dir + ) + elif algorithm_name == "GA": + random_seed = config['random_seed'] + run_counts = config['run_counts'] + pop_num = config['pop_num'] + cross_prob = config['cross_prob'] + mut_prob = config['mut_prob'] + save_freq = config['save_freq'] + for i in range(len(data_info_train["basin"])): + basin_id = data_info_train["basin"][i] + basin_area = data_info_train["area"][i] + # one directory for one model + one hyperparam setting and one basin + deap_db_dir = os.path.join(save_dir, basin_id) + + if not os.path.exists(deap_db_dir): + os.makedirs(deap_db_dir) + calibrate_by_ga( + data_train[:, i: i + 1, 0:2], + data_train[:, i: i + 1, -1:], + deap_db_dir, + warmup_length=warmup_length, + model={ + 'name': model_name, + 'source_type': source_type, + 'source_book': source_book + }, + ga_param={ + 'name': algorithm_name, + 'random_seed': random_seed, + 'run_counts': run_counts, + 'pop_num': pop_num, + 'cross_prob': cross_prob, + 'mut_prob': mut_prob, + 'save_freq': save_freq + }, + ) + show_ga_result( + deap_db_dir, + warmup_length=warmup_length, + basin_id=basin_id, + the_data=data_train[:, i: i + 1, :], + the_period=data_info_train["time"], + basin_area=basin_area, + model_info={ + 'name': model_name, + 'source_type': source_type, + 'source_book': source_book + }, + train_mode=True, + ) + show_ga_result( + deap_db_dir, + warmup_length=warmup_length, + basin_id=basin_id, + the_data=data_test[:, i: i + 1, :], + the_period=data_info_test["time"], + basin_area=basin_area, + model_info={ + 'name': model_name, + 'source_type': source_type, + 'source_book': source_book + }, + train_mode=False, + ) + else: + raise NotImplementedError( + "We don't provide this calibrate method! Choose from 'SCE_UA' or 'GA'!" + ) + summarize_parameters(save_dir, model_info={ + 'name': model_name, + 'source_type': source_type, + 'source_book': source_book + }) + renormalize_params(save_dir, model_info={ + 'name': model_name, + 'source_type': source_type, + 'source_book': source_book + }) + summarize_metrics(save_dir, model_info={ + 'name': model_name, + 'source_type': source_type, + 'source_book': source_book + }) + save_streamflow(save_dir, model_info={ + 'name': model_name, + 'source_type': source_type, + 'source_book': source_book, + }, fold=fold) + print(f"Finish calibrating the {fold}-th fold") diff --git a/xaj/__init__.py b/xaj/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/xaj/calibrate_ga_xaj_bmi.py b/xaj/calibrate_ga_xaj_bmi.py new file mode 100644 index 0000000..555b671 --- /dev/null +++ b/xaj/calibrate_ga_xaj_bmi.py @@ -0,0 +1,381 @@ +"""Calibrate XAJ model using DEAP""" +import os +import pickle +import random +import sys +from pathlib import Path + +import numpy as np +import pandas as pd +from deap import base, creator +from deap import tools +from tqdm import tqdm + +sys.path.append(os.path.dirname(Path(os.path.abspath(__file__)).parent.parent)) +import definitions +from hydromodel.models.model_config import MODEL_PARAM_DICT +from hydromodel.utils import hydro_constant, hydro_utils +from hydromodel.utils import stat +from hydromodel.utils.stat import statRmse +from hydromodel.visual.hydro_plot import plot_sim_and_obs, plot_train_iteration +from xaj.xaj_bmi import xajBmi + + +def evaluate(individual, x_input, y_true, warmup_length, model): + """ + Calculate fitness for optimization + + Parameters + ---------- + individual + individual is the params of XAJ (see details in xaj.py); we initialize all parameters in range [0,1] + x_input + input of XAJ + y_true + observation data; we use the part after warmup period + warmup_length + the length of warmup period + model + model's name: "xaj", "xaj_mz", "gr4j", or "hymod" + + Returns + ------- + float + fitness + """ + # print("Calculate fitness:") + # NOTE: Now only support one basin's calibration for once now + params = np.array(individual).reshape(1, -1) + if model["name"] in ["xaj", "xaj_mz"]: + # xaj model's output include streamflow and evaporation now, + # but now we only calibrate the model with streamflow + model = xajBmi() + model.initialize(os.path.relpath('runxaj.yaml'), params, x_input) + while model.get_current_time() <= model.get_end_time('train'): + model.update() + sim = model.get_value("discharge") + sim = np.expand_dims(sim, 0) + sim = np.expand_dims(sim, 1) + sim = np.transpose(sim, [2, 1, 0]) + else: + raise NotImplementedError("We don't provide this model now") + rmses = statRmse(y_true[warmup_length:, :, :], sim) + rmse = rmses.mean(axis=0) + # print(f"-----------------RMSE: {str(rmse)}------------------------") + return rmse + + +def checkBounds(min, max): + """ + A decorator to set bounds for individuals in a population + + Parameters + ---------- + min + the lower bound of individuals + max + the upper bound of individuals + + Returns + ------- + Function + a wrapper for clipping data into a given bound + """ + + def decorator(func): + def wrapper(*args, **kargs): + offspring = func(*args, **kargs) + for child in offspring: + for i in range(len(child)): + if child[i] > max: + child[i] = max + elif child[i] < min: + child[i] = min + return offspring + + return wrapper + + return decorator + + +MIN = 0 +MAX = 1 + + +def calibrate_by_ga( + input_data, observed_output, deap_dir, warmup_length=30, model=None, ga_param=None +): + """ + Use GA algorithm to find optimal parameters for hydrologic models + + Parameters + ---------- + input_data + the input data for model + observed_output + the "true" values, i.e. observations + deap_dir + the directory to save the results + warmup_length + the length of warmup period + model + the model setting + ga_param + random_seed: 1234 + run_counts: int = 40, running counts + pop_num: int = 50, the number of individuals in the population + cross_prob: float = 0.5, the probability with which two individuals are crossed + mut_prob: float=0.5, the probability for mutating an individual + + Returns + ------- + toolbox.population + optimal_params + """ + if model is None: + model = { + "name": "xaj_mz", + "source_type": "sources", + "source_book": "HF", + } + if ga_param is None: + ga_param = { + "random_seed": 1234, + "run_counts": 5, + "pop_num": 50, + "cross_prob": 0.5, + "mut_prob": 0.5, + "save_freq": 1, + } + np.random.seed(ga_param["random_seed"]) + param_num = len(MODEL_PARAM_DICT[model["name"]]["param_name"]) + creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) + creator.create("Individual", list, fitness=creator.FitnessMin) + toolbox = base.Toolbox() + toolbox.register("attribute", random.random) + toolbox.register( + "individual", + tools.initRepeat, + creator.Individual, + toolbox.attribute, + n=param_num, + ) + toolbox.register("population", tools.initRepeat, list, toolbox.individual) + + toolbox.register("mate", tools.cxTwoPoint) + toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.1) + toolbox.register("select", tools.selTournament, tournsize=3) + toolbox.register( + "evaluate", + evaluate, + x_input=input_data, + y_true=observed_output, + warmup_length=warmup_length, + model=model, + ) + + toolbox.decorate("mate", checkBounds(MIN, MAX)) + toolbox.decorate("mutate", checkBounds(MIN, MAX)) + pop = toolbox.population(n=ga_param["pop_num"]) + # cxpb is the probability with which two individuals are crossed + # mutpb is the probability for mutating an individual + cxpb, mutpb = ga_param["cross_prob"], ga_param["mut_prob"] + + # save the best individual + halloffame = tools.HallOfFame(maxsize=1) + logbook = tools.Logbook() + stats = tools.Statistics(lambda ind: ind.fitness.values) + stats.register("avg", np.mean) + stats.register("min", np.min) + + # Evaluate the entire population for the first time + print("Initiliazing population...") + fitnesses = map(toolbox.evaluate, pop) + for ind, fit in zip(pop, fitnesses): + ind.fitness.values = fit + halloffame.update(pop) + record = stats.compile(pop) + logbook.record(gen=0, evals=len(pop), **record) + cp = dict( + population=pop, + generation=0, + halloffame=halloffame, + logbook=logbook, + rndstate=random.getstate(), + ) + with open(os.path.join(deap_dir, "epoch0.pkl"), "wb") as cp_file: + pickle.dump(cp, cp_file) + + for gen in tqdm(range(ga_param["run_counts"]), desc="GA calibrating"): + + print(f"Generation {gen} started...") + # Select the next generation individuals + offspring = toolbox.select(pop, len(pop)) + # Clone the selected individuals + offspring = list(map(toolbox.clone, offspring)) + + # Apply crossover and mutation on the offspring + for child1, child2 in zip(offspring[::2], offspring[1::2]): + if random.random() < cxpb: + toolbox.mate(child1, child2) + del child1.fitness.values + del child2.fitness.values + + for mutant in offspring: + if random.random() < mutpb: + toolbox.mutate(mutant) + del mutant.fitness.values + + # Evaluate the individuals with an invalid fitness + invalid_ind = [ind for ind in offspring if not ind.fitness.valid] + fitnesses = map(toolbox.evaluate, invalid_ind) + for ind, fit in tqdm( + zip(invalid_ind, fitnesses), + desc=f"{str(gen + 1)} generation fitness calculating", + ): + ind.fitness.values = fit + + halloffame.update(offspring) + record = stats.compile(offspring) + # +1 means start from 1, 0 means initial generation + logbook.record(gen=gen + 1, evals=len(invalid_ind), **record) + # The population is entirely replaced by the offspring + pop[:] = offspring + print( + f"Best individual of {str(gen + 1)}" + + f" generation is: {halloffame[0]}, {halloffame[0].fitness.values}" + ) + if gen % ga_param["save_freq"] == 0: + # Fill the dictionary using the dict(key=value[, ...]) constructor + cp = dict( + population=pop, + generation=gen + 1, + halloffame=halloffame, + logbook=logbook, + rndstate=random.getstate(), + ) + + with open( + os.path.join(deap_dir, f"epoch{str(gen + 1)}.pkl"), "wb" + ) as cp_file: + pickle.dump(cp, cp_file) + print(f"Files of generation {gen} saved.") + top10 = tools.selBest(pop, k=10) + return pop + + +def show_ga_result( + deap_dir, + warmup_length, + basin_id, + the_data, + the_period, + basin_area, + model_info, + result_unit="mm/day", + train_mode=True, +): + """ + show the result of GA + """ + # https://stackoverflow.com/questions/61065222/python-deap-and-multiprocessing-on-windows-attributeerror + creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) + creator.create("Individual", list, fitness=creator.FitnessMin) + with open(os.path.join(deap_dir, "epoch2.pkl"), "rb") as cp_file: + cp = pickle.load(cp_file) + pop = cp["population"] + logbook = cp["logbook"] + halloffame = cp["halloffame"] + print(f"Best individual is: {halloffame[0]}, {halloffame[0].fitness.values}") + train_test_flag = "train" if train_mode else "test" + + model = xajBmi() + model.initialize("runxaj.yaml", np.array(list(halloffame[0])).reshape(1, -1), the_data[:, :, 0:2]) + while model.get_current_time() <= model.get_end_time('train'): + model.update() + best_simulation = model.get_value("discharge") + + convert_unit_sim = hydro_constant.convert_unit( + np.array(best_simulation).reshape(1, -1), + # best_simulation, + result_unit, + hydro_constant.unit["streamflow"], + basin_area=basin_area, + ) + convert_unit_obs = hydro_constant.convert_unit( + np.array(the_data[warmup_length:, :, -1:]).reshape(1, -1), + result_unit, + hydro_constant.unit["streamflow"], + basin_area=basin_area, + ) + # save calibrated results of calibration period + the_result_file = os.path.join( + deap_dir, + f"{train_test_flag}_qsim_" + model_info["name"] + "_" + str(basin_id) + ".csv", + ) + pd.DataFrame(convert_unit_sim.reshape(-1, 1)).to_csv( + the_result_file, + sep=",", + index=False, + header=False, + ) + # calculation rmse、nashsutcliffe and bias for training period + stat_error = stat.statError( + convert_unit_obs, + convert_unit_sim, + ) + print(f"{train_test_flag}ing metrics:", basin_id, stat_error) + hydro_utils.serialize_json_np( + stat_error, os.path.join(deap_dir, f"{train_test_flag}_metrics.json") + ) + t_range = pd.to_datetime(the_period[warmup_length:]).values.astype("datetime64[D]") + save_fig = os.path.join(deap_dir, f"{train_test_flag}_results.png") + if train_mode: + save_param_file = os.path.join(deap_dir, basin_id + "_calibrate_params.txt") + pd.DataFrame(list(halloffame[0])).to_csv( + save_param_file, sep=",", index=False, header=True + ) + fit_mins = logbook.select("min") + plot_train_iteration(fit_mins, os.path.join(deap_dir, "train_iteration.png")) + plot_sim_and_obs( + t_range, + convert_unit_sim.flatten(), + convert_unit_obs.flatten(), + save_fig, + ) + + +if __name__ == "__main__": + data_dir = os.path.join( + definitions.ROOT_DIR, + "hydromodel", + "example", + "exp004", + ) + deap_dir = os.path.join( + data_dir, + "Dec25_16-33-56_LAPTOP-DNQOPPMS_fold0_HFsources", + "60668", + ) + train_data_info_file = os.path.join(data_dir, "data_info_fold0_train.json") + train_data_file = os.path.join(data_dir, "basins_lump_p_pe_q_fold0_train.npy") + data_train = hydro_utils.unserialize_numpy(train_data_file) + data_info_train = hydro_utils.unserialize_json_ordered(train_data_info_file) + model_info = { + "name": "xaj_mz", + "source_type": "sources", + "source_book": "HF", + } + train_period = data_info_train["time"] + basin_area = data_info_train["area"][0] + + show_ga_result( + deap_dir, + 365, + "60668", + data_train[:, 0:1, :], + train_period, + basin_area, + model_info, + result_unit="mm/day", + ) diff --git a/xaj/calibrate_sceua_xaj_bmi.py b/xaj/calibrate_sceua_xaj_bmi.py new file mode 100644 index 0000000..ac7cf9b --- /dev/null +++ b/xaj/calibrate_sceua_xaj_bmi.py @@ -0,0 +1,209 @@ +from typing import Union +import numpy as np +import spotpy +from spotpy.parameter import Uniform, ParameterSet +from spotpy.objectivefunctions import rmse +from hydromodel.models.model_config import MODEL_PARAM_DICT +from xaj.xaj_bmi import xajBmi + + +class SpotSetup(object): + def __init__( + self, + p_and_e, + qobs, + warmup_length=30, + model={ + "name": "xaj_mz", + "source_type": "sources", + "source_book": "HF", + }, + obj_func=None, + ): + """ + Set up for Spotpy + + Parameters + ---------- + p_and_e + inputs of model + qobs + observation data + warmup_length + GR4J model need warmup period + model + we support "gr4j", "hymod", and "xaj" + model_func_param + parameters of model function + obj_func + objective function, typically RMSE + """ + self.parameter_names = MODEL_PARAM_DICT[model["name"]]["param_name"] + self.model = model + self.params = [] + for par_name in self.parameter_names: + # All parameters' range are [0,1], we will transform them to normal range in the model + self.params.append(Uniform(par_name, low=0.0, high=1.0)) + # Just a way to keep this example flexible and applicable to various examples + self.obj_func = obj_func + # Load Observation data from file + self.p_and_e = p_and_e + # chose observation data after warmup period + self.true_obs = qobs[warmup_length:, :, :] + self.warmup_length = warmup_length + + def parameters(self): + return spotpy.parameter.generate(self.params) + + def simulation(self, x: ParameterSet) -> Union[list, np.array]: + """ + run xaj model + + Parameters + ---------- + x + the parameters of xaj. This function only has this one parameter. + + Returns + ------- + Union[list, np.array] + simulated result from xaj + """ + # Here the model is started with one parameter combination + # parameter, 2-dim variable: [basin=1, parameter] + params = np.array(x).reshape(1, -1) + model = xajBmi() + # xaj model's output include streamflow and evaporation now, + # but now we only calibrate the model with streamflow + + model.initialize('runxaj.yaml', params, self.p_and_e) + while model.get_current_time() <= model.get_end_time('train'): + model.update() + sim=model.get_value("discharge") + sim = np.expand_dims(sim, 0) + sim = np.expand_dims(sim, 1) + sim = np.transpose(sim, [2,1,0]) + return sim[:, 0, 0] + + def evaluation(self) -> Union[list, np.array]: + """ + read observation values + + Returns + ------- + Union[list, np.array] + observation + """ + # TODO: we only support one basin's calibration now + + return self.true_obs[:, 0, 0] + + def objective_function( + self, + simulation: Union[list, np.array], + evaluation: Union[list, np.array], + params=None, + ) -> float: + """ + A user defined objective function to calculate fitness. + + Parameters + ---------- + simulation: + simulation results + evaluation: + evaluation results + params: + parameters leading to the simulation + + Returns + ------- + float + likelihood + """ + # SPOTPY expects to get one or multiple values back, + # that define the performance of the model run + print(simulation.shape, evaluation.shape) + if not self.obj_func: + # This is used if not overwritten by user + like = rmse(evaluation, simulation) + else: + # Way to ensure flexible spot setup class + like = self.obj_func(evaluation, simulation) + return like + + +def calibrate_by_sceua( + p_and_e, + qobs, + dbname, + warmup_length=30, + model={ + "name": "xaj_mz", + "source_type": "sources", + "source_book": "HF", + }, + algorithm={ + "name": "SCE_UA", + "random_seed": 1234, + "rep": 1000, + "ngs": 1000, + "kstop": 500, + "peps": 0.001, + "pcento": 0.001, + }, +): + """ + Function for calibrating model by SCE-UA + + Now we only support one basin's calibration in one sampler + + Parameters + ---------- + p_and_e + inputs of model + qobs + observation data + dbname + where save the result file of sampler + warmup_length + the length of warmup period + model + we support "gr4j", "hymod", and "xaj", parameters for hydro model + calibrate_algo + calibrate algorithm. For example, if you want to calibrate xaj model, + and use sce-ua algorithm -- random seed=2000, rep=5000, ngs=7, kstop=3, peps=0.1, pcento=0.1 + + Returns + ------- + None + """ + random_seed = algorithm["random_seed"] + rep = algorithm["rep"] + ngs = algorithm["ngs"] + kstop = algorithm["kstop"] + peps = algorithm["peps"] + pcento = algorithm["pcento"] + np.random.seed(random_seed) # Makes the results reproduceable + + # Initialize the xaj example + # In this case, we tell the setup which algorithm we want to use, so + # we can use this exmaple for different algorithms + spot_setup = SpotSetup( + p_and_e, + qobs, + warmup_length=warmup_length, + model=model, + obj_func=spotpy.objectivefunctions.rmse, + ) + # Select number of maximum allowed repetitions + sampler = spotpy.algorithms.sceua( + spot_setup, + dbname=dbname, + dbformat="csv", + random_state=random_seed, + ) + # Start the sampler, one can specify ngs, kstop, peps and pcento id desired + sampler.sample(rep, ngs=ngs, kstop=kstop, peps=peps, pcento=pcento) + print("Calibrate Finished!") + return sampler diff --git a/xaj/configuration.py b/xaj/configuration.py new file mode 100644 index 0000000..708f736 --- /dev/null +++ b/xaj/configuration.py @@ -0,0 +1,27 @@ +import yaml +import numpy as np +from xaj.xajmodel import xaj_state as xaj_state + + +def read_config(config_file): + with open(config_file, encoding='utf8') as f: + config = yaml.safe_load(f) + return config + + +def extract_forcing(forcing_data): + # p_and_e_df = forcing_data[["rainfall[mm]", "TURC [mm d-1]"]] + p_and_e_df = forcing_data[["prcp(mm/day)", "petfao56(mm/day)"]] + p_and_e = np.expand_dims(p_and_e_df.values, axis=1) + return p_and_e_df, p_and_e + + +def warmup(p_and_e_warmup, params_state, warmup_length, model_info): + q_sim, es, *w0, w1, w2, s0, fr0, qi0, qg0 = xaj_state( + p_and_e_warmup, + params_state, + warmup_length=warmup_length, + model_info=model_info, + return_state=True, + ) + return q_sim, es, *w0, w1, w2, s0, fr0, qi0, qg0 diff --git a/xaj/constant_unit.py b/xaj/constant_unit.py new file mode 100644 index 0000000..f09eef7 --- /dev/null +++ b/xaj/constant_unit.py @@ -0,0 +1,63 @@ +""" +Author: Wenyu Ouyang +Date: 2022-12-08 09:24:54 +LastEditTime: 2022-12-08 09:51:54 +LastEditors: Wenyu Ouyang +Description: some constant for hydro model +FilePath: /hydro-model-xaj/hydromodel/utils/hydro_constant.py +Copyright (c) 2021-2022 Wenyu Ouyang. All rights reserved. +""" +# unify the unit of each variable +unit = {"streamflow": "m3/s"} +def convert_unit(data, unit_now, unit_final, **kwargs): + """ + convert unit of variable + + Parameters + ---------- + data + data to be converted + unit_now + unit of variable now + unit_final + unit of variable after conversion + **kwargs + other parameters required for conversion + + Returns + ------- + data + data after conversion + """ + if unit_now == "mm/day" and unit_final == "m3/s": + result = mm_per_day_to_m3_per_sec(basin_area=kwargs["basin_area"], q=data) + else: + raise ValueError("unit conversion not supported") + return result + + +def mm_per_day_to_m3_per_sec(basin_area, q): + """ + trans mm/day to m3/s for xaj models + + Parameters + ---------- + basin_area + we need to know the area of a basin so that we can perform this transformation + q + original streamflow data + + Returns + ------- + + """ + # 1 ft3 = 0.02831685 m3 + # ft3tom3 = 2.831685e-2 + # 1 km2 = 10^6 m2 + km2tom2 = 1e6 + # 1 m = 1000 mm + mtomm = 1000 + # 1 day = 24 * 3600 s + daytos = 24 * 3600 + q_trans = q * basin_area * km2tom2 / (mtomm * daytos) + return q_trans diff --git a/xaj/dockerfile b/xaj/dockerfile new file mode 100644 index 0000000..af94fa3 --- /dev/null +++ b/xaj/dockerfile @@ -0,0 +1,13 @@ +FROM ewatercycle/wflow-grpc4bmi +MAINTAINER wangjingyi <1160527180@qq.com> + +# Install grpc4bmi +RUN pip install grpc4bmi==0.2.8 + +# Set environment +WORKDIR /home +ENV BMI_MODULE=bmixaj +ENV BMI_CLASS=Bmixaj +ENV BMI_PORT=55555 +ENTRYPOINT ["run-bmi-server", "--name", "xaj.xaj_bmi.xajBmi", "--path", "/home/wangjingyi/code/hydro-model-xaj/xaj"] +EXPOSE 55555 \ No newline at end of file diff --git a/xaj/params.py b/xaj/params.py new file mode 100644 index 0000000..49f3393 --- /dev/null +++ b/xaj/params.py @@ -0,0 +1,122 @@ +""" +Author: Wenyu Ouyang +Date: 2022-10-25 21:16:22 +LastEditTime: 2022-12-25 16:06:05 +LastEditors: Wenyu Ouyang +Description: some basic config for hydro-model-xaj models +FilePath: \hydro-model-xaj\hydromodel\models\model_config.py +Copyright (c) 2021-2022 Wenyu Ouyang. All rights reserved. +""" +from collections import OrderedDict + +# NOTE: Don't change the parameter settings + +MODEL_PARAM_DICT = { + "xaj": { + "param_name": [ + # Allen, R.G., L. Pereira, D. Raes, and M. Smith, 1998. + # Crop Evapotranspiration, Food and Agriculture Organization of the United Nations, + # Rome, Italy. FAO publication 56. ISBN 92-5-104219-5. 290p. + "K", # ratio of potential evapotranspiration to reference crop evaporation generally from Allen, 1998 + "B", # The exponent of the tension water capacity curve + "IM", # The ratio of the impervious to the total area of the basin + "UM", # Tension water capacity in the upper layer + "LM", # Tension water capacity in the lower layer + "DM", # Tension water capacity in the deepest layer + "C", # The coefficient of deep evapotranspiration + "SM", # The areal mean of the free water capacity of surface soil layer + "EX", # The exponent of the free water capacity curve + "KI", # Outflow coefficients of interflow + "KG", # Outflow coefficients of groundwater + "CS", # The recession constant of channel system + "L", # Lag time + "CI", # The recession constant of the lower interflow + "CG", # The recession constant of groundwater storage + ], + "param_range": OrderedDict( + { + "K": [0.1, 0.1], + "B": [0.1, 0.1], + "IM": [0.1, 0.1], + "UM": [10, 10.0], + "LM": [70.0, 70.0], + "DM": [80.0, 80.0], + "C": [0.1, 0.1], + "SM": [50, 50], + "EX": [1.2, 1.2], + "KI": [0.3, 0.3], + "KG": [0.3, 0.3], + "CS": [0.5, 0.5], + "L": [5.0, 5.0], # unit is day + "CI": [0.5, 0.5], + "CG": [0.98, 0.98], + } + ), + }, + "xaj_mz": { + "param_name": [ + # Allen, R.G., L. Pereira, D. Raes, and M. Smith, 1998. + # Crop Evapotranspiration, Food and Agriculture Organization of the United Nations, + # Rome, Italy. FAO publication 56. ISBN 92-5-104219-5. 290p. + "K", # ratio of potential evapotranspiration to reference crop evaporation generally from Allen, 1998 + "B", # The exponent of the tension water capacity curve + "IM", # The ratio of the impervious to the total area of the basin + "UM", # Tension water capacity in the upper layer + "LM", # Tension water capacity in the lower layer + "DM", # Tension water capacity in the deepest layer + "C", # The coefficient of deep evapotranspiration + "SM", # The areal mean of the free water capacity of surface soil layer + "EX", # The exponent of the free water capacity curve + "KI", # Outflow coefficients of interflow + "KG", # Outflow coefficients of groundwater + "A", # parameter of mizuRoute + "THETA", # parameter of mizuRoute + "CI", # The recession constant of the lower interflow + "CG", # The recession constant of groundwater storage + "KERNEL", # kernel size of mizuRoute unit hydrograph when using convolution method + ], + "param_range": OrderedDict( + { + "K": [0.1, 1.0], + "B": [0.1, 0.4], + "IM": [0.01, 0.1], + "UM": [0.0, 20.0], + "LM": [60.0, 90.0], + "DM": [60.0, 120.0], + "C": [0.0, 0.2], + "SM": [1.0, 100.0], + "EX": [1.0, 1.5], + "KI": [0.0, 0.7], + "KG": [0.0, 0.7], + "A": [0.0, 2.9], + "THETA": [0.0, 6.5], + "CI": [0.0, 0.9], + "CG": [0.98, 0.998], + "KERNEL": [1, 15], + } + ), + }, + "gr4j": { + "param_name": ["x1", "x2", "x3", "x4"], + "param_range": OrderedDict( + { + "x1": [100.0, 1200.0], + "x2": [-5.0, 3.0], + "x3": [20.0, 300.0], + "x4": [1.1, 2.9], + } + ), + }, + "hymod": { + "param_name": ["cmax", "bexp", "alpha", "ks", "kq"], + "param_range": OrderedDict( + { + "cmax": [1.0, 500.0], + "bexp": [0.1, 2.0], + "alpha": [0.1, 0.99], + "ks": [0.001, 0.10], + "kq": [0.1, 0.99], + } + ), + }, +} diff --git a/xaj/xaj_bmi.py b/xaj/xaj_bmi.py new file mode 100644 index 0000000..f8fc628 --- /dev/null +++ b/xaj/xaj_bmi.py @@ -0,0 +1,292 @@ +from typing import Tuple +from bmipy import Bmi +import numpy as np + +from xaj import configuration +from xaj.xajmodel import xaj_route, xaj_runoff +import datetime +import logging + +logger = logging.getLogger(__name__) + +PRECISION = 1e-5 + + +class xajBmi(Bmi): + """Empty model wrapped in a BMI interface.""" + name = "hydro-model-xaj" + input_var_names = ("precipitation", "ETp") + output_var_names = ("ET", "discharge") + var_units = {"precipitation": "mm/day", "ETp": "mm/day", "discharge": "mm/day", "ET": "mm/day"} + + def __init__(self): + """Create a BmiHeat model that is ready for initialization.""" + self.time_step = 0 + + def initialize(self, config_file, params, p_and_e): + try: + logger.info("xaj: initialize_model") + config = configuration.read_config(config_file) + model_name = config['model_name'] + source_type = config['source_type'] + source_book = config['source_book'] + # forcing_data = pd.read_csv(config['forcing_data']) + model_info = { + 'model_name': model_name, + 'source_type': source_type, + 'source_book': source_book, + + } + # p_and_e_df, p_and_e = configuration.extract_forcing(forcing_data) + p_and_e_warmup = p_and_e[0:config['warmup_length'], :, :] + self.q_sim_state, self.es_state, self.w0, self.w1, self.w2, self.s0, self.fr0, self.qi0, self.qg0 = configuration.warmup( + p_and_e_warmup, params, config['warmup_length'], model_info) + + self.params = params + self.warmup_length = config['warmup_length'] + self._start_time_str = config['start_time_str'] + self._end_time_str = config['end_time_str'] + self._time_units = config['time_units'] + # self.p_and_e_df = p_and_e_df + self.p_and_e = p_and_e + self.config = config + self.model_info = model_info + + train_period = config['train_period'] + test_period = config['test_period'] + self.train_start_time_str = train_period[0] + self.train_end_time_str = train_period[1] + self.test_start_time_str = test_period[0] + self.test_end_time_str = test_period[1] + + except: + import traceback + traceback.print_exc() + raise + + def update(self): + """Update model for a single time step.""" + + self.time_step += 1 + p_and_e_sim = self.p_and_e[self.warmup_length:self.time_step + self.warmup_length] + # p_and_e_sim = self.p_and_e[0:self.time_step] + self.runoff_im, self.rss_, self.ris_, self.rgs_, self.es_runoff, self.rss = xaj_runoff(p_and_e_sim, + w0=self.w0, s0=self.s0, + fr0=self.fr0, + params_runoff=self.params, + return_state=False, + model_info=self.model_info, + ) + if self.time_step + self.warmup_length >= self.p_and_e.shape[0]: + q_sim, es = xaj_route(p_and_e_sim, + params_route=self.params, + model_info=self.model_info, + runoff_im=self.runoff_im, + rss_=self.rss_, + ris_=self.ris_, + rgs_=self.rgs_, + rss=self.rss, + qi0=self.qi0, + qg0=self.qg0, + es=self.es_runoff, + ) + self.p_sim = p_and_e_sim[:, :, 0] + self.e_sim = p_and_e_sim[:, :, 1] + # q_sim = convert_unit( + # q_sim, + # unit_now="mm/day", + # unit_final=unit["streamflow"], + # basin_area=float(self.basin_area), + # ) + self.q_sim = q_sim[:, :, :] + self.es = es[:, :, :] + + def update_until(self, time): + while self.get_current_time() + 0.001 < time: + self.update() + + def finalize(self) -> None: + """Finalize model.""" + self.model = None + + def get_component_name(self) -> str: + return "xaj" + + def get_input_item_count(self) -> int: + return len(self.input_var_names) + + def get_output_item_count(self) -> int: + return len(self.output_var_names) + + def get_input_var_names(self) -> Tuple[str]: + return self.input_var_names + + def get_output_var_names(self) -> Tuple[str]: + return self.output_var_names + + def get_var_grid(self, name: str) -> int: + raise NotImplementedError() + + def get_var_type(self, name: str) -> str: + return 'float64' + + def get_var_units(self, name: str) -> str: + return self.var_units[name] + + def get_var_itemsize(self, name: str) -> int: + return np.dtype(self.get_var_type(name)).itemsize + + def get_var_nbytes(self, name: str) -> int: + return self.get_value_ptr(name).nbytes + + def get_var_location(self, name: str) -> str: + raise NotImplementedError() + + def get_start_time(self, period): + if period == 'train': + return self.start_Time(self.train_start_time_str) + if period == 'test': + return self.start_Time(self.test_start_time_str) + # return self.start_Time(self._start_time_str) + + def get_current_time(self): + # return self.start_Time(self._start_time_str) + datetime.timedelta(self.time_step+self.warmup_length) + if self._time_units == 'hours': + time_step = datetime.timedelta(hours=self.time_step) + elif self._time_units == 'days': + time_step = datetime.timedelta(days=self.time_step) + return self.start_Time(self._start_time_str) + time_step + + def get_end_time(self, period): + if period == 'train': + return self.end_Time(self.train_end_time_str) + if period == 'test': + return self.end_Time(self.test_end_time_str) + # return self.end_Time(self._end_time_str) + + def get_time_units(self) -> str: + return self._time_units + + def get_time_step(self) -> float: + return 1 + + def get_value(self, name: str) -> None: + logger.info("getting value for var %s", name) + return self.get_value_ptr(name).flatten() + + def get_value_ptr(self, name: str) -> np.ndarray: + if name == 'discharge': + return self.q_sim + elif name == 'ET': + return self.es + + def get_value_at_indices( + self, name: str, inds: np.ndarray + ) -> np.ndarray: + + return self.get_value_ptr(name).take(inds) + + def set_value(self, name: str, src: np.ndarray): + + val = self.get_value_ptr(name) + val[:] = src.reshape(val.shape) + + def set_value_at_indices( + self, name: str, inds: np.ndarray, src: np.ndarray + ) -> None: + val = self.get_value_ptr(name) + val.flat[inds] = src + + # Grid information + def get_grid_rank(self, grid: int) -> int: + raise NotImplementedError() + + def get_grid_size(self, grid: int) -> int: + raise NotImplementedError() + + def get_grid_type(self, grid: int) -> str: + raise NotImplementedError() + + # Uniform rectilinear + def get_grid_shape(self, grid: int, shape: np.ndarray) -> np.ndarray: + raise NotImplementedError() + + def get_grid_spacing(self, grid: int, spacing: np.ndarray) -> np.ndarray: + raise NotImplementedError() + + def get_grid_origin(self, grid: int, origin: np.ndarray) -> np.ndarray: + raise NotImplementedError() + + # Non-uniform rectilinear, curvilinear + def get_grid_x(self, grid: int, x: np.ndarray) -> np.ndarray: + raise NotImplementedError() + + def get_grid_y(self, grid: int, y: np.ndarray) -> np.ndarray: + raise NotImplementedError() + + def get_grid_z(self, grid: int, z: np.ndarray) -> np.ndarray: + raise NotImplementedError() + + def get_grid_node_count(self, grid: int) -> int: + raise NotImplementedError() + + def get_grid_edge_count(self, grid: int) -> int: + raise NotImplementedError() + + def get_grid_face_count(self, grid: int) -> int: + raise NotImplementedError() + + def get_grid_edge_nodes(self, grid: int, edge_nodes: np.ndarray) -> np.ndarray: + raise NotImplementedError() + + def get_grid_face_edges(self, grid: int, face_edges: np.ndarray) -> np.ndarray: + raise NotImplementedError() + + def get_grid_face_nodes(self, grid: int, face_nodes: np.ndarray) -> np.ndarray: + raise NotImplementedError() + + def get_grid_nodes_per_face( + self, grid: int, nodes_per_face: np.ndarray + ) -> np.ndarray: + raise NotImplementedError() + + def start_Time(self, _start_time_str): + + try: + if " " in _start_time_str: + date, time = _start_time_str.split(" ") + else: + date = _start_time_str + time = None + year, month, day = date.split("-") + self._startTime = datetime.date(int(year), int(month), int(day)) + + if time: + hour, minute, second = time.split(":") + # self._startTime = self._startTime.replace(hour=int(hour), + # minute=int(minute), + # second=int(second)) + self._startTime = datetime.datetime(int(year), int(month), int(day), int(hour), int(minute), + int(second)) + except ValueError: + raise ValueError("Invalid start date format!") + + return self._startTime + + def end_Time(self, _end_time_str): + + try: + if " " in _end_time_str: + date, time = _end_time_str.split(" ") + else: + date = _end_time_str + time = None + year, month, day = date.split("-") + self._endTime = datetime.date(int(year), int(month), int(day)) + + if time: + hour, minute, second = time.split(":") + self._endTime = datetime.datetime(int(year), int(month), int(day), int(hour), int(minute), int(second)) + except ValueError: + raise ValueError("Invalid start date format!") + return self._endTime diff --git a/xaj/xajmodel.py b/xaj/xajmodel.py new file mode 100644 index 0000000..2027955 --- /dev/null +++ b/xaj/xajmodel.py @@ -0,0 +1,1060 @@ +""" +Core code for XinAnJiang model +""" +import logging +from typing import Union +import numpy as np +from numba import jit +from scipy.special import gamma + +from xaj.params import MODEL_PARAM_DICT + +PRECISION = 1e-5 + + +@jit +def calculate_evap(lm, c, wu0, wl0, prcp, pet) -> tuple[np.array, np.array, np.array]: + """ + Three-layers evaporation model from "Watershed Hydrologic Simulation" written by Prof. RenJun Zhao. + The book is Chinese, and its name is 《流域水文模拟》; + The three-layers evaporation model is described in Page 76; + The method is same with that in Page 22-23 in "Hydrologic Forecasting (5-th version)" written by Prof. Weimin Bao. + This book's Chinese name is 《水文预报》 + + Parameters + ---------- + lm + average soil moisture storage capacity of lower layer + c + coefficient of deep layer + wu0 + initial soil moisture of upper layer; update in each time step + wl0 + initial soil moisture of lower layer; update in each time step + prcp + basin mean precipitation + pet + potential evapotranspiration + + Returns + ------- + tuple[np.array,np.array,np.array] + eu/el/ed are evaporation from upper/lower/deeper layer, respectively + """ + eu = np.where(wu0 + prcp >= pet, pet, wu0 + prcp) + ed = np.where((wl0 < c * lm) & (wl0 < c * (pet - eu)), c * (pet - eu) - wl0, 0.0) + el = np.where( + wu0 + prcp >= pet, + 0.0, + np.where( + wl0 >= c * lm, + (pet - eu) * wl0 / lm, + np.where(wl0 >= c * (pet - eu), c * (pet - eu), wl0), + ), + ) + return eu, el, ed + + +@jit +def calculate_prcp_runoff(b, im, wm, w0, pe) -> tuple[np.array, np.array]: + """ + Calculates the amount of runoff generated from rainfall after entering the underlying surface. + + Same in "Watershed Hydrologic Simulation" and "Hydrologic Forecasting (5-th version)" + + Parameters + ---------- + b + B; exponent coefficient + im + IMP; imperiousness coefficient + wm + average soil moisture storage capacity + w0 + initial soil moisture + pe + net precipitation + + Returns + ------- + tuple[np.array,np.array] + r -- runoff; r_im -- runoff of impervious part + """ + wmm = wm * (1.0 + b) + a = wmm * (1.0 - (1.0 - w0 / wm) ** (1.0 / (1.0 + b))) + if np.isnan(a).any(): + raise ArithmeticError("Please check if w0>wm or b is a negative value!") + r_cal = np.where( + pe > 0.0, + np.where( + pe + a < wmm, + # 1e-5 is a precision which we set to guarantee float's calculation is correct + pe - (wm - w0) + wm * (1.0 - np.minimum(a + pe, wmm) / wmm) ** (1.0 + b), + pe - (wm - w0), + ), + np.full(pe.shape, 0.0), + ) + r = np.maximum(r_cal, 0.0) + # separate impervious part with the other + r_im_cal = pe * im + r_im = np.maximum(r_im_cal, 0.0) + return r, r_im + + +def calculate_w_storage( + um, lm, dm, wu0, wl0, wd0, eu, el, ed, pe, r +) -> tuple[np.array, np.array, np.array]: + """ + Update the soil moisture values of the three layers. + + According to the equation 2.60 in the book《水文预报》 + + Parameters + ---------- + um + average soil moisture storage capacity of the upper layer + lm + average soil moisture storage capacity of the lower layer + dm + average soil moisture storage capacity of the deep layer + wu0 + initial values of soil moisture in upper layer + wl0 + initial values of soil moisture in lower layer + wd0 + initial values of soil moisture in deep layer + eu + evaporation of the upper layer; it isn't used in this function + el + evaporation of the lower layer + ed + evaporation of the deep layer + pe + net precipitation; it is able to be negative value in this function + r + runoff + + Returns + ------- + tuple[np.array,np.array,np.array] + wu,wl,wd -- soil moisture in upper, lower and deep layer + """ + # pe>0: the upper soil moisture was added firstly, then lower layer, and the final is deep layer + # pe<=0: no additional water, just remove evapotranspiration, + # but note the case: e >= p > 0 + # (1) if wu0 + p > e, then e = eu (2) else, wu must be zero + wu = np.where( + pe > 0.0, + np.where(wu0 + pe - r < um, wu0 + pe - r, um), + np.where(wu0 + pe > 0.0, wu0 + pe, 0.0), + ) + # calculate wd before wl because it is easier to cal using where statement + wd = np.where( + pe > 0.0, + np.where(wu0 + wl0 + pe - r > um + lm, wu0 + wl0 + wd0 + pe - r - um - lm, wd0), + wd0 - ed, + ) + # water balance (equation 2.2 in Page 13, also shown in Page 23) + # if wu0 + p > e, then e = eu; else p must be used in upper layer, + # so no matter what the case is, el didn't include p, neither ed + wl = np.where(pe > 0.0, wu0 + wl0 + wd0 + pe - r - wu - wd, wl0 - el) + # the water storage should be in reasonable range + wu_ = np.clip(wu, a_min=0.0, a_max=um) + wl_ = np.clip(wl, a_min=0.0, a_max=lm) + wd_ = np.clip(wd, a_min=0.0, a_max=dm) + return wu_, wl_, wd_ + + +def generation(p_and_e, k, b, im, um, lm, dm, c, wu0=None, wl0=None, wd0=None) -> tuple: + """ + Single-step runoff generation in XAJ. + + Parameters + ---------- + p_and_e + precipitation and potential evapotranspiration + k + ratio of potential evapotranspiration to reference crop evaporation + b + exponent parameter + um + average soil moisture storage capacity of the upper layer + lm + average soil moisture storage capacity of the lower layer + dm + average soil moisture storage capacity of the deep layer + im + impermeability coefficient + c + coefficient of deep layer + wu0 + initial values of soil moisture in upper layer + wl0 + initial values of soil moisture in lower layer + wd0 + initial values of soil moisture in deep layer + + Returns + ------- + tuple[tuple, tuple] + (r, rim, e, pe), (wu, wl, wd); all variables are np.array + """ + # make sure physical variables' value ranges are correct + prcp = np.maximum(p_and_e[:, 0], 0.0) + # get potential evapotranspiration + pet = np.maximum(p_and_e[:, 1] * k, 0.0) + # wm + wm = um + lm + dm + if wu0 is None: + # just an initial value + wu0 = 0.6 * um + if wl0 is None: + wl0 = 0.6 * lm + if wd0 is None: + wd0 = 0.6 * dm + w0_ = wu0 + wl0 + wd0 + + # w0 need locate in correct range so that following calculation could be right + # To make sure float data's calculation is correct, we'd better minus a precision (1e-5) + w0 = np.minimum(w0_, wm - 1e-5) + + # Calculate the amount of evaporation from storage + eu, el, ed = calculate_evap(lm, c, wu0, wl0, prcp, pet) + e = eu + el + ed + + # Calculate the runoff generated by net precipitation + prcp_difference = prcp - e + pe = np.maximum(prcp_difference, 0.0) + r, rim = calculate_prcp_runoff(b, im, wm, w0, pe) + # Update wu, wl, wd + wu, wl, wd = calculate_w_storage( + um, lm, dm, wu0, wl0, wd0, eu, el, ed, prcp_difference, r + ) + + return (r, rim, e, pe), (wu, wl, wd) + + +def sources(pe, r, sm, ex, ki, kg, s0=None, fr0=None, book="HF") -> tuple: + """ + Divide the runoff to different sources + + We use the initial version from the paper of the inventor of the XAJ model -- Prof. Renjun Zhao: + "Analysis of parameters of the XinAnJiang model". Its Chinese name is <<新安江模型参数的分析>>, + which could be found by searching in "Baidu Xueshu". + The module's code can also be found in "Watershed Hydrologic Simulation" (WHS) Page 174. + It is nearly same with that in "Hydrologic Forecasting" (HF) Page 148-149 + We use the period average runoff as input and the unit period is day so we don't need to difference it as books show + + We also provide code for formula from《水文预报》"Hydrologic Forecasting" (HF) the fifth version. Page 40-41 and 150-151; + the procedures in 《工程水文学》"Engineering Hydrology" (EH) the third version are different we also provide. + they are in the "sources5mm" function. + + Parameters + ------------ + pe + net precipitation + r + runoff from xaj_generation + sm + areal mean free water capacity of the surface layer + ex + exponent of the free water capacity curve + ki + outflow coefficients of the free water storage to interflow relationships + kg + outflow coefficients of the free water storage to groundwater relationships + s0 + free water capacity of last period + fr0 + runoff area of last period + + Return + ------------ + tuple[tuple, tuple] + rs -- surface runoff; ri-- interflow runoff; rg -- groundwater runoff; + s1 -- final free water capacity; + all variables are numpy array + + """ + # maximum free water storage capacity in a basin + ms = sm * (1.0 + ex) + if fr0 is None: + fr0 = 0.1 + if s0 is None: + s0 = 0.5 * sm + # For free water storage, because s is related to fr and s0 and fr0 are both values of last period, + # we have to trans the initial value of s from last period to this one. + # both WHS（流域水文模拟）'s sample code and HF（水文预报） use s = fr0 * s0 / fr. + # I think they both think free water reservoir as a cubic tank. Its height is s and area of bottom rectangle is fr + # we will have a cubic tank with varying bottom and height, + # and fixed boundary (in HF sm is fixed) or none-fixed boundary (in EH smmf is not fixed) + # but notice r's list like" [1,0] which 1 is the 1st period's runoff and 0 is the 2nd period's runoff + # after 1st period, the s1 could not be zero, but in the 2nd period, fr=0, then we cannot set s=0, because some water still in the tank + # fr's formula could be found in Eq. 9 in "Analysis of parameters of the XinAnJiang model", + # Here our r doesn't include rim, so there is no need to remove rim from r; this is also the method in 《水文预报》（HF） + + # NOTE: when r is 0, fr should be 0, however, s1 may not be zero and it still hold some water, + # then fr can not be 0, otherwise when fr is used as denominator it lead to error, + # so we have to deal with this case later, for example, when r=0, we cannot use pe * fr to replace r + # because fr get the value of last period, and it is not 0 + fr = np.copy(fr0) + if any(fr == 0.0): + raise ArithmeticError( + "Please check fr's value, fr==0.0 will cause error in the next step!" + ) + # r=0, then r/pe must be 0 + fr_mask = r > 0.0 + fr[fr_mask] = r[fr_mask] / pe[fr_mask] + if np.isnan(fr).any(): + raise ArithmeticError("Please check pe's data! there may be 0.0") + + # if fr=0, then we cannot get ss, but ss should not be 0, because s1 of last period may not be 0 and it still hold some water + ss = np.copy(s0) + # same initialization for s + s = np.copy(s0) + + ss[fr_mask] = fr0[fr_mask] * s0[fr_mask] / fr[fr_mask] + + if book == "HF": + ss = np.minimum(ss, sm) + au = ms * (1.0 - (1.0 - ss / sm) ** (1.0 / (1.0 + ex))) + if np.isnan(au).any(): + raise ValueError( + "Error: NaN values detected. Try set clip function or check your data!!!" + ) + # the first condition should be r > 0.0, when r=0, rs must be 0, fig 6-12 in EH or fig 5-4 in HF + # so we have to use "pe" carefully!!! when r>0.0, we use pe, otherwise we don't use it!!! + rs = np.full(r.shape, 0.0) + rs[fr_mask] = np.where( + pe[fr_mask] + au[fr_mask] < ms[fr_mask], + # equation 2-85 in HF + fr[fr_mask] + * ( + pe[fr_mask] + - sm[fr_mask] + + ss[fr_mask] + + sm[fr_mask] + * ( + ( + 1 + - np.minimum(pe[fr_mask] + au[fr_mask], ms[fr_mask]) + / ms[fr_mask] + ) + ** (1 + ex[fr_mask]) + ) + ), + # equation 2-86 in HF + fr[fr_mask] * (pe[fr_mask] + ss[fr_mask] - sm[fr_mask]), + ) + rs = np.minimum(rs, r) + + # ri's mask is not same as rs's, because last period's s may not be 0 + # and in this time, ri and rg could be larger than 0 + # we need firstly calculate the updated s, s's mask is same as fr_mask, + # when r==0, then s will be equal to last period's + # equation 2-87 in HF, some free water leave or save, so we update free water storage + s[fr_mask] = ss[fr_mask] + (r[fr_mask] - rs[fr_mask]) / fr[fr_mask] + s = np.minimum(s, sm) + if np.isnan(s).any(): + raise ArithmeticError("Please check fr's data! there may be 0.0") + + elif book == "EH": + # smmf should be with correpond with s + smmf = ms * (1 - (1 - fr) ** (1 / ex)) + smf = smmf / (1 + ex) + ss = np.minimum(ss, smf) + au = smmf * (1 - (1 - ss / smf) ** (1 / (1 + ex))) + if np.isnan(au).any(): + raise ValueError( + "Error: NaN values detected. Try set clip function or check your data!!!" + ) + + # rs's mask is keep with fr_mask + rs = np.full(r.shape, 0.0) + rs[fr_mask] = np.where( + pe[fr_mask] + au[fr_mask] < smmf[fr_mask], + ( + pe[fr_mask] + - smf[fr_mask] + + ss[fr_mask] + + smf[fr_mask] + * ( + 1 + - np.minimum(pe[fr_mask] + au[fr_mask], smmf[fr_mask]) + / smmf[fr_mask] + ) + ** (ex[fr_mask] + 1) + ) + * fr[fr_mask], + (pe[fr_mask] + ss[fr_mask] - smf[fr_mask]) * fr[fr_mask], + ) + rs = np.minimum(rs, r) + s[fr_mask] = ss[fr_mask] + (r[fr_mask] - rs[fr_mask]) / fr[fr_mask] + s = np.minimum(s, smf) + else: + raise ValueError("Please set book as 'HF' or 'EH'!") + # the following part is same for both HF and EH. Even the formula is different, but their meaning is same + # equation 2-88 in HF, next interflow and ground water will be released from the updated free water storage + # We use the period average runoff as input and the general unit period is day. + # Hence, we directly use ki and kg rather than ki_{Δt} in books. + ri = ki * s * fr + rg = kg * s * fr + # ri = np.where(s < PRECISION, np.full(r.shape, 0.0), ki * s * fr) + # rg = np.where(s < PRECISION, np.full(r.shape, 0.0), kg * s * fr) + # equation 2-89 in HF; although it looks different with that in WHS, they are actually same + # Finally, calculate the final free water storage + s1 = s * (1 - ki - kg) + # s1 = np.where(s1 < PRECISION, np.full(s1.shape, 0.0), s1) + return (rs, ri, rg), (s1, fr) + + +def sources5mm( + pe, + runoff, + sm, + ex, + ki, + kg, + s0=None, + fr0=None, + time_interval_hours=24, + book="HF", +): + """ + Divide the runoff to different sources according to books -- 《水文预报》HF 5th edition and 《工程水文学》EH 3rd edition + + Parameters + ---------- + pe + net precipitation + runoff + runoff from xaj_generation + sm + areal mean free water capacity of the surface layer + ex + exponent of the free water capacity curve + ki + outflow coefficients of the free water storage to interflow relationships + kg + outflow coefficients of the free water storage to groundwater relationships + s0 + initial free water capacity + fr0 + initial area of generation + time_interval_hours + 由于Ki、Kg、Ci、Cg都是以24小时为时段长定义的,需根据时段长转换 + book + the methods in 《水文预报》HF 5th edition and 《工程水文学》EH 3rd edition are different, + hence, both are provided, and the default is the former -- "ShuiWenYuBao"; + the other one is "GongChengShuiWenXue" + + Returns + ------- + tuple[tuple, tuple] + rs_s -- surface runoff; rss_s-- interflow runoff; rg_s -- groundwater runoff; + (fr_ds[-1], s_ds[-1]): state variables' final value; + all variables are numpy array + """ + # 由于Ki、Kg都是以24小时为时段长定义的，需根据时段长转换 + hours_per_day = 24 + # 非整除情况，时段+1 + residue_temp = hours_per_day % time_interval_hours + if residue_temp != 0: + residue_temp = 1 + period_num_1d = int(hours_per_day / time_interval_hours) + residue_temp + # 当kss+kg>1时，根式为偶数运算时，kss_period会成为复数，这里会报错；另外注意分母可能为0，kss不可取0 + # 对kss+kg的取值进行限制 + kss_period = (1 - (1 - (ki + kg)) ** (1 / period_num_1d)) / (1 + kg / ki) + kg_period = kss_period * kg / ki + + # 流域最大点自由水蓄水容量深 + smm = sm * (1 + ex) + if s0 is None: + s0 = 0.50 * sm + if fr0 is None: + fr0 = 0.1 + # don't use np.where here, because it will cause some warning + fr = np.copy(fr0) + fr_mask = runoff > 0.0 + fr[fr_mask] = runoff[fr_mask] / pe[fr_mask] + if np.all(runoff < 5): + n = 1 + else: + # when modeling multiple basins, the number of divides is not the same, so we use the maximum number + r_max = np.max(runoff) + residue_temp = r_max % 5 + if residue_temp != 0: + residue_temp = 1 + n = int(r_max / 5) + residue_temp + rn = runoff / n + pen = pe / n + kss_d = (1 - (1 - (kss_period + kg_period)) ** (1 / n)) / ( + 1 + kg_period / kss_period + ) + kg_d = kss_d * kg_period / kss_period + # kss_d = kss_period + # kg_d = kg_period + rs = np.full(runoff.shape, 0.0) + rss = np.full(runoff.shape, 0.0) + rg = np.full(runoff.shape, 0.0) + + s_ds = [] + fr_ds = [] + s_ds.append(s0) + fr_ds.append(fr0) + + for j in range(n): + fr0_d = fr_ds[j] + s0_d = s_ds[j] + # equation 5-32 in HF, but strange, cause each period, rn/pen is same, fr_d should be same + # fr_d = 1 - (1 - fr) ** (1 / n) + fr_d = fr + + ss_d = np.copy(s0_d) + s_d = np.copy(s0_d) + s1_d = np.copy(s0_d) + + ss_d[fr_mask] = fr0_d[fr_mask] * s0_d[fr_mask] / fr_d[fr_mask] + + if book == "HF": + ss_d = np.minimum(ss_d, sm) + # ms = smm + au = smm * (1.0 - (1.0 - ss_d / sm) ** (1.0 / (1.0 + ex))) + if np.isnan(au).any(): + raise ValueError( + "Error: NaN values detected. Try set clip function or check your data!!!" + ) + rs_j = np.full(rn.shape, 0.0) + rs_j[fr_mask] = np.where( + pen[fr_mask] + au[fr_mask] < smm[fr_mask], + # equation 5-26 in HF + fr_d[fr_mask] + * ( + pen[fr_mask] + - sm[fr_mask] + + ss_d[fr_mask] + + sm[fr_mask] + * ( + ( + 1 + - np.minimum(pen[fr_mask] + au[fr_mask], smm[fr_mask]) + / smm[fr_mask] + ) + ** (1 + ex[fr_mask]) + ) + ), + # equation 5-27 in HF + fr_d[fr_mask] * (pen[fr_mask] + ss_d[fr_mask] - sm[fr_mask]), + ) + rs_j = np.minimum(rs_j, rn) + s_d[fr_mask] = ss_d[fr_mask] + (rn[fr_mask] - rs_j[fr_mask]) / fr_d[fr_mask] + s_d = np.minimum(s_d, sm) + + elif book == "EH": + smmf = smm * (1 - (1 - fr_d) ** (1 / ex)) + smf = smmf / (1 + ex) + ss_d = np.minimum(ss_d, smf) + au = smmf * (1 - (1 - ss_d / smf) ** (1 / (1 + ex))) + if np.isnan(au).any(): + raise ValueError( + "Error: NaN values detected. Try set clip function or check your data!!!" + ) + rs_j = np.full(rn.shape, 0.0) + rs_j[fr_mask] = np.where( + pen[fr_mask] + au[fr_mask] < smmf[fr_mask], + ( + pen[fr_mask] + - smf[fr_mask] + + ss_d[fr_mask] + + smf[fr_mask] + * ( + 1 + - np.minimum(pen[fr_mask] + au[fr_mask], smmf[fr_mask]) + / smmf[fr_mask] + ) + ** (ex[fr_mask] + 1) + ) + * fr_d[fr_mask], + (pen[fr_mask] + ss_d[fr_mask] - smf[fr_mask]) * fr_d[fr_mask], + ) + rs_j = np.minimum(rs_j, rn) + s_d[fr_mask] = ss_d[fr_mask] + (rn[fr_mask] - rs_j[fr_mask]) / fr_d[fr_mask] + s_d = np.minimum(s_d, smf) + + else: + raise NotImplementedError( + "We don't have this implementation! Please chose 'HF' or 'EH'!!" + ) + rss_j = s_d * kss_d * fr_d + rg_j = s_d * kg_d * fr_d + s1_d = s_d * (1 - kss_d - kg_d) + + rs = rs + rs_j + rss = rss + rss_j + rg = rg + rg_j + # 赋值s_d和fr_d到数组中，以给下一段做初值 + s_ds.append(s1_d) + fr_ds.append(fr_d) + + return (rs, rss, rg), (s_ds[-1], fr_ds[-1]) + + +@jit +def linear_reservoir(x, weight, last_y=None) -> np.array: + """ + Linear reservoir's release function + + Parameters + ---------- + x + the input to the linear reservoir + weight + the coefficient of linear reservoir + last_y + the output of last period + + Returns + ------- + np.array + one-step forward result + """ + weight1 = 1 - weight + if last_y is None: + last_y = np.full(weight.shape, 0.001) + y = weight * last_y + weight1 * x + return y + + +def uh_conv(x, uh_from_gamma): + """ + Function for 1d-convolution calculation + + Parameters + ---------- + x + x is a sequence-first variable; the dim of x is [seq, batch, feature=1]; + feature must be 1 + uh_from_gamma + unit hydrograph from uh_gamma; the dim: [len_uh, batch, feature=1]; + feature must be 1 + + Returns + ------- + np.array + convolution + """ + outputs = np.full(x.shape, 0.0) + time_length, batch_size, feature_size = x.shape + if feature_size > 1: + logging.error("We only support one-dim convolution now!!!") + for i in range(batch_size): + uh = uh_from_gamma[:, i, 0] + inputs = x[:, i, 0] + outputs[:, i, 0] = np.convolve(inputs, uh)[:time_length] + return outputs + + +def uh_gamma(a, theta, len_uh=15): + """ + A simple two-parameter Gamma distribution as a unit-hydrograph to route instantaneous runoff from a hydrologic model + The method comes from mizuRoute -- http://www.geosci-model-dev.net/9/2223/2016/ + + Parameters + ---------- + a + shape parameter + theta + timescale parameter + len_uh + the time length of the unit hydrograph + Returns + ------- + torch.Tensor + the unit hydrograph, dim: [seq, batch, feature] + """ + # dims of a: time_seq (same all time steps), batch, feature=1 + m = a.shape + if len_uh > m[0]: + raise RuntimeError( + "length of unit hydrograph should be smaller than the whole length of input" + ) + # aa > 0, here we set minimum 0.1 (min of a is 0, set when calling this func); First dimension of a is repeat + aa = np.maximum(0.0, a[0:len_uh, :, :]) + 0.1 + # theta > 0, here set minimum 0.5 + theta = np.maximum(0.0, theta[0:len_uh, :, :]) + 0.5 + # len_f, batch, feature + t = np.expand_dims( + np.swapaxes(np.tile(np.arange(0.5, len_uh * 1.0), (m[1], 1)), 0, 1), axis=-1 + ) + denominator = gamma(aa) * (theta**aa) + # [len_f, m[1], m[2]] + w = 1 / denominator * (t ** (aa - 1)) * (np.exp(-t / theta)) + w = w / w.sum(0) # scale to 1 for each UH + return w + + +def xaj_runoff( + p_and_e, + params_runoff: Union[np.array, list], + *w0, s0, fr0, + **kwargs, +) -> Union[tuple, np.array]: + model_name = kwargs["name"] if "name" in kwargs else "xaj" + source_type = kwargs["source_type"] if "source_type" in kwargs else "sources" + source_book = kwargs["source_book"] if "source_book" in kwargs else "HF" + # params + param_ranges = MODEL_PARAM_DICT[model_name]["param_range"] + if model_name == "xaj": + route_method = "CSL" + elif model_name == "xaj_mz": + route_method = "MZ" + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CS' or 'MZ'!" + ) + xaj_params = [ + (value[1] - value[0]) * params_runoff[:, i] + value[0] + for i, (key, value) in enumerate(param_ranges.items()) + ] + k = xaj_params[0] + b = xaj_params[1] + im = xaj_params[2] + um = xaj_params[3] + lm = xaj_params[4] + dm = xaj_params[5] + c = xaj_params[6] + sm = xaj_params[7] + ex = xaj_params[8] + ki_ = xaj_params[9] + kg_ = xaj_params[10] + # ki+kg should be smaller than 1; if not, we scale them + ki = np.where(ki_ + kg_ < 1.0, ki_, (1.0 - PRECISION) / (ki_ + kg_) * ki_) + kg = np.where(ki_ + kg_ < 1.0, kg_, (1.0 - PRECISION) / (ki_ + kg_) * kg_) + + + # w0 = (0.5 * um, 0.5 * lm, 0.5 * dm) + inputs = p_and_e[:, :, :] + runoff_ims_ = np.full(inputs.shape[:2], 0.0) + rss_ = np.full(inputs.shape[:2], 0.0) + ris_ = np.full(inputs.shape[:2], 0.0) + rgs_ = np.full(inputs.shape[:2], 0.0) + es_ = np.full(inputs.shape[:2], 0.0) + for i in range(inputs.shape[0]): + if i == 0: + (r, rim, e, pe), w = generation( + inputs[i, :, :], k, b, im, um, lm, dm, c, *w0 + ) + if source_type == "sources": + (rs, ri, rg), (s, fr) = sources( + pe, r, sm, ex, ki, kg, s0, fr0, book=source_book + ) + elif source_type == "sources5mm": + (rs, ri, rg), (s, fr) = sources5mm( + pe, r, sm, ex, ki, kg, s0, fr0, book=source_book + ) + else: + raise NotImplementedError("No such divide-sources method") + else: + (r, rim, e, pe), w = generation( + inputs[i, :, :], k, b, im, um, lm, dm, c, *w + ) + if source_type == "sources": + (rs, ri, rg), (s, fr) = sources( + pe, r, sm, ex, ki, kg, s, fr, book=source_book + ) + elif source_type == "sources5mm": + (rs, ri, rg), (s, fr) = sources5mm( + pe, r, sm, ex, ki, kg, s, fr, book=source_book + ) + else: + raise NotImplementedError("No such divide-sources method") + # impevious part is pe * im + runoff_ims_[i, :] = rim + # so for non-imprvious part, the result should be corrected + rss_[i, :] = rs * (1 - im) + ris_[i, :] = ri * (1 - im) + rgs_[i, :] = rg * (1 - im) + es_[i, :] = e + # seq, batch, feature + runoff_im = np.expand_dims(runoff_ims_, axis=2) + rss = np.expand_dims(rss_, axis=2) + es = np.expand_dims(es_, axis=2) + return runoff_im, rss_,ris_, rgs_, es,rss + +def xaj_route( + p_and_e, + params_route: Union[np.array, list], + ris_, + rgs_, + rss_, + rss, + runoff_im, + qi0, + qg0, + es, + **kwargs, +)-> Union[tuple, np.array]: + + model_name = kwargs["name"] if "name" in kwargs else "xaj" + source_type = kwargs["source_type"] if "source_type" in kwargs else "sources" + source_book = kwargs["source_book"] if "source_book" in kwargs else "HF" + # params + param_ranges = MODEL_PARAM_DICT[model_name]["param_range"] + if model_name == "xaj": + route_method = "CSL" + elif model_name == "xaj_mz": + route_method = "MZ" + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CS' or 'MZ'!" + ) + xaj_params = [ + (value[1] - value[0]) * params_route[:, i] + value[0] + for i, (key, value) in enumerate(param_ranges.items()) + ] + k = xaj_params[0] + b = xaj_params[1] + im = xaj_params[2] + um = xaj_params[3] + lm = xaj_params[4] + dm = xaj_params[5] + c = xaj_params[6] + sm = xaj_params[7] + ex = xaj_params[8] + ki_ = xaj_params[9] + kg_ = xaj_params[10] + # ki+kg should be smaller than 1; if not, we scale them + ki = np.where(ki_ + kg_ < 1.0, ki_, (1.0 - PRECISION) / (ki_ + kg_) * ki_) + kg = np.where(ki_ + kg_ < 1.0, kg_, (1.0 - PRECISION) / (ki_ + kg_) * kg_) + if route_method == "CSL": + cs = xaj_params[11] + l = xaj_params[12] + + elif route_method == "MZ": + # we will use routing method from mizuRoute -- http://www.geosci-model-dev.net/9/2223/2016/ + a = xaj_params[11] + theta = xaj_params[12] + # make it as a parameter + kernel_size = int(xaj_params[15]) + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CS' or 'MZ'!" + ) + ci = xaj_params[13] + cg = xaj_params[14] + + inputs = p_and_e[:, :, :] + qs = np.full(inputs.shape[:2], 0.0) + if route_method == "CSL": + qt = np.full(inputs.shape[:2], 0.0) + for i in range(inputs.shape[0]): + if i == 0: + qi = linear_reservoir(ris_[i], ci, qi0) + qg = linear_reservoir(rgs_[i], cg, qg0) + else: + qi = linear_reservoir(ris_[i], ci, qi) + qg = linear_reservoir(rgs_[i], cg, qg) + qs_ = rss_[i] + qt[i, :] = qs_ + qi + qg + + for j in range(len(l)): + lag = int(l[j]) + for i in range(0,lag): + qs[i, j] = qt[i, j] + if i == lag: + break + for i in range(lag, inputs.shape[0]): + qs[i, j] = cs[j] * qs[i - 1, j] + (1 - cs[j]) * qt[i - lag, j] + + elif route_method == "MZ": + rout_a = a.repeat(rss.shape[0]).reshape(rss.shape) + rout_b = theta.repeat(rss.shape[0]).reshape(rss.shape) + conv_uh = uh_gamma(rout_a, rout_b, kernel_size) + qs_ = uh_conv(runoff_im + rss, conv_uh) + for i in range(inputs.shape[0]): + if i == 0: + qi = linear_reservoir(ris_[i], ci, qi0) + qg = linear_reservoir(rgs_[i], cg, qg0) + else: + qi = linear_reservoir(ris_[i], ci, qi) + qg = linear_reservoir(rgs_[i], cg, qg) + qs[i, :] = qs_[i, :, 0] + qi + qg + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CSL' or 'MZ'!" + ) + + # seq, batch, feature + q_sim = np.expand_dims(qs, axis=2) + return q_sim, es + +def xaj_state( + p_and_e, + params: Union[np.array, list], + return_state=True, + warmup_length=365, + **kwargs, +) -> Union[tuple, np.array]: + # default values for some function parameters + model_name = kwargs["name"] if "name" in kwargs else "xaj" + source_type = kwargs["source_type"] if "source_type" in kwargs else "sources" + source_book = kwargs["source_book"] if "source_book" in kwargs else "HF" + # params + param_ranges = MODEL_PARAM_DICT[model_name]["param_range"] + if model_name == "xaj": + route_method = "CSL" + elif model_name == "xaj_mz": + route_method = "MZ" + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CS' or 'MZ'!" + ) + xaj_params = [ + (value[1] - value[0]) * params[:, i] + value[0] + for i, (key, value) in enumerate(param_ranges.items()) + ] + k = xaj_params[0] + b = xaj_params[1] + im = xaj_params[2] + um = xaj_params[3] + lm = xaj_params[4] + dm = xaj_params[5] + c = xaj_params[6] + sm = xaj_params[7] + ex = xaj_params[8] + ki_ = xaj_params[9] + kg_ = xaj_params[10] + # ki+kg should be smaller than 1; if not, we scale them + ki = np.where(ki_ + kg_ < 1.0, ki_, (1.0 - PRECISION) / (ki_ + kg_) * ki_) + kg = np.where(ki_ + kg_ < 1.0, kg_, (1.0 - PRECISION) / (ki_ + kg_) * kg_) + if route_method == "CSL": + cs = xaj_params[11] + l = xaj_params[12] + + elif route_method == "MZ": + # we will use routing method from mizuRoute -- http://www.geosci-model-dev.net/9/2223/2016/ + a = xaj_params[11] + theta = xaj_params[12] + # make it as a parameter + kernel_size = int(xaj_params[15]) + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CS' or 'MZ'!" + ) + ci = xaj_params[13] + cg = xaj_params[14] + + + # initialize state values + # if warmup_length > 0: + # p_and_e_warmup = p_and_e[0:warmup_length, :, :] + # _q, _e, *w0, s0, fr0, qi0, qg0 = xaj_state( + # p_and_e_warmup, + # params, + # return_state=True, + # warmup_length=0, + # **kwargs, + # ) + # else: + w0 = (0.5 * um, 0.5 * lm, 0.5 * dm) + s0 = 0.5 * sm + fr0 = np.full(ex.shape, 0.1) + qi0 = np.full(ci.shape, 0.1) + qg0 = np.full(cg.shape, 0.1) + + #state_variables + # inputs = p_and_e[warmup_length:, :, :] + inputs = p_and_e[:, :, :] + runoff_ims_ = np.full(inputs.shape[:2], 0.0) + rss_ = np.full(inputs.shape[:2], 0.0) + ris_ = np.full(inputs.shape[:2], 0.0) + rgs_ = np.full(inputs.shape[:2], 0.0) + es_ = np.full(inputs.shape[:2], 0.0) + for i in range(inputs.shape[0]): + if i == 0: + (r, rim, e, pe), w = generation( + inputs[i, :, :], k, b, im, um, lm, dm, c, *w0 + ) + if source_type == "sources": + (rs, ri, rg), (s, fr) = sources( + pe, r, sm, ex, ki, kg, s0, fr0, book=source_book + ) + elif source_type == "sources5mm": + (rs, ri, rg), (s, fr) = sources5mm( + pe, r, sm, ex, ki, kg, s0, fr0, book=source_book + ) + else: + raise NotImplementedError("No such divide-sources method") + else: + (r, rim, e, pe), w = generation( + inputs[i, :, :], k, b, im, um, lm, dm, c, *w + ) + if source_type == "sources": + (rs, ri, rg), (s, fr) = sources( + pe, r, sm, ex, ki, kg, s, fr, book=source_book + ) + elif source_type == "sources5mm": + (rs, ri, rg), (s, fr) = sources5mm( + pe, r, sm, ex, ki, kg, s, fr, book=source_book + ) + else: + raise NotImplementedError("No such divide-sources method") + # impevious part is pe * im + runoff_ims_[i, :] = rim + # so for non-imprvious part, the result should be corrected + rss_[i, :] = rs * (1 - im) + ris_[i, :] = ri * (1 - im) + rgs_[i, :] = rg * (1 - im) + es_[i, :] = e + # seq, batch, feature + runoff_im = np.expand_dims(runoff_ims_, axis=2) + rss = np.expand_dims(rss_, axis=2) + es = np.expand_dims(es_, axis=2) + + qs = np.full(inputs.shape[:2], 0.0) + + if route_method == "CSL": + qt = np.full(inputs.shape[:2], 0.0) + for i in range(inputs.shape[0]): + if i == 0: + qi = linear_reservoir(ris_[i], ci, qi0) + qg = linear_reservoir(rgs_[i], cg, qg0) + else: + qi = linear_reservoir(ris_[i], ci, qi) + qg = linear_reservoir(rgs_[i], cg, qg) + qs_ = rss_[i] + qt[i, :] = qs_ + qi + qg + + for j in range(len(l)): + # print(range(len(l))) + lag = int(l[j]) + for i in range(0,lag): + qs[i, j] = qt[i, j] + if i == lag: + break + for i in range(lag, inputs.shape[0]): + # print(inputs.shape[0]) + qs[i, j] = cs[j] * qs[i - 1, j] + (1 - cs[j]) * qt[i - lag, j] + + elif route_method == "MZ": + rout_a = a.repeat(rss.shape[0]).reshape(rss.shape) + rout_b = theta.repeat(rss.shape[0]).reshape(rss.shape) + conv_uh = uh_gamma(rout_a, rout_b, kernel_size) + qs_ = uh_conv(runoff_im + rss, conv_uh) + for i in range(inputs.shape[0]): + if i == 0: + qi = linear_reservoir(ris_[i], ci, qi0) + qg = linear_reservoir(rgs_[i], cg, qg0) + else: + qi = linear_reservoir(ris_[i], ci, qi) + qg = linear_reservoir(rgs_[i], cg, qg) + qs[i, :] = qs_[i, :, 0] + qi + qg + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CSL' or 'MZ'!" + ) + + # seq, batch, feature + q_sim = np.expand_dims(qs, axis=2) + if return_state: + return q_sim, es, *w, s, fr, qi, qg From 3a64eca73ec773f55430843e2571fd8aef906e3a Mon Sep 17 00:00:00 2001 From: forestbat Date: Sun, 24 Sep 2023 16:21:38 +0800 Subject: [PATCH 03/19] add requirements.txt --- requirements.txt | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 57a82b9..74513e9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,4 +10,7 @@ seaborn tqdm pytest hydrodataset -bmipy \ No newline at end of file +bmipy +pyyaml +requests +spotpy \ No newline at end of file From 15e90fa6defbd826fdbf31c199e5f520ac2afb37 Mon Sep 17 00:00:00 2001 From: forestbat Date: Mon, 9 Oct 2023 12:02:44 +0800 Subject: [PATCH 04/19] add dmca_esr.py and its test to util directory --- hydromodel/example/division_flow.csv | 9132 ++++++++++++++++++++++++++ hydromodel/example/division_rain.csv | 9132 ++++++++++++++++++++++++++ hydromodel/utils/dmca_esr.py | 223 + setup.py | 12 +- test/test_session_division.py | 35 + 5 files changed, 18528 insertions(+), 6 deletions(-) create mode 100644 hydromodel/example/division_flow.csv create mode 100644 hydromodel/example/division_rain.csv create mode 100644 hydromodel/utils/dmca_esr.py create mode 100644 test/test_session_division.py diff --git a/hydromodel/example/division_flow.csv b/hydromodel/example/division_flow.csv new file mode 100644 index 0000000..23dddb9 --- /dev/null +++ b/hydromodel/example/division_flow.csv @@ -0,0 +1,9132 @@ +flow +0.571000 +0.623000 +1.202000 +1.342000 +0.898000 +0.809000 +1.246000 +1.352000 +1.295000 +1.079000 +2.093000 +2.941000 +1.445000 +1.013000 +0.889000 +1.128000 +2.489000 +1.490000 +1.161000 +2.640000 +1.253000 +1.134000 +1.556000 +2.974000 +1.834000 +5.458000 +2.490000 +3.318000 +3.036000 +6.627000 +7.136000 +5.402000 +4.535000 +3.582000 +2.730000 +4.550000 +2.556000 +2.157000 +5.923000 +4.910000 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+2.625000 +0.619000 +4.224000 +1.210000 +2.956000 +2.128000 +0.804000 +0.978000 +5.989000 +4.413000 +0.881000 +0.507000 +3.135000 +2.478000 +0.154000 +0.106000 +0.081000 +1.224000 \ No newline at end of file diff --git a/hydromodel/utils/dmca_esr.py b/hydromodel/utils/dmca_esr.py new file mode 100644 index 0000000..397fd77 --- /dev/null +++ b/hydromodel/utils/dmca_esr.py @@ -0,0 +1,223 @@ +import numpy as np + + +def movmean(X, n): + ones = np.ones(X.shape) + kernel = np.ones(n) + return np.convolve(X, kernel, mode='same') / np.convolve(ones, kernel, mode='same') + + +def step1_step2_tr_and_fluctuations_timeseries(rain, flow, rain_min, max_window): + """ + :param rain: 降雨量向量，单位mm/h，需注意与mm/day之间的单位转化 + :param flow: 径流量向量，单位m³/h，需注意与m³/day之间的单位转化 + :param rain_min: 最小降雨量阈值 + :param max_window: 场次划分最大窗口，决定场次长度 + """ + rain = rain.T + flow = flow.T + rain_int = np.nancumsum(rain) + flow_int = np.nancumsum(flow) + T = rain.size + rain_mean = np.empty(((max_window - 1) // 2, T)) + flow_mean = np.empty(((max_window - 1) // 2, T)) + fluct_rain = np.empty(((max_window - 1) // 2, T)) + fluct_flow = np.empty(((max_window - 1) // 2, T)) + F_rain = np.empty((max_window - 1) // 2) + F_flow = np.empty((max_window - 1) // 2) + F_rain_flow = np.empty((max_window - 1) // 2) + rho = np.empty((max_window - 1) // 2) + for window in np.arange(3, max_window + 1, 2): + int_index = int((window - 1) / 2 - 1) + start_slice = int(window - 0.5 * (window - 1)) + dst_slice = int(T - 0.5 * (window - 1)) + # 新建一个循环体长度*数据长度的大数组 + rain_mean[int_index] = movmean(rain_int, window) + flow_mean[int_index] = movmean(flow_int, window) + fluct_rain[int_index] = rain_int - rain_mean[int_index, :] + F_rain[int_index] = (1 / (T - window + 1)) * np.nansum( + (fluct_rain[int_index, start_slice:dst_slice]) ** 2) + fluct_flow[int_index, np.newaxis] = flow_int - flow_mean[int_index, :] + F_flow[int_index] = (1 / (T - window + 1)) * np.nansum( + (fluct_flow[int_index, start_slice:dst_slice]) ** 2) + F_rain_flow[int_index] = (1 / (T - window + 1)) * np.nansum( + (fluct_rain[int_index, start_slice:dst_slice]) * ( + fluct_flow[int_index, start_slice:dst_slice])) + rho[int_index] = F_rain_flow[int_index] / ( + np.sqrt(F_rain[int_index]) * np.sqrt(F_flow[int_index])) + pos_min = np.argmin(rho) + Tr = pos_min + 1 + tol_fluct_rain = (rain_min / (2 * Tr + 1)) * Tr + tol_fluct_flow = flow_int[-1] / 1e15 + fluct_rain[pos_min, np.fabs(fluct_rain[pos_min, :]) < tol_fluct_rain] = 0 + fluct_flow[pos_min, np.fabs(fluct_flow[pos_min, :]) < tol_fluct_flow] = 0 + fluct_rain_Tr = fluct_rain[pos_min, :] + fluct_flow_Tr = fluct_flow[pos_min, :] + fluct_bivariate_Tr = fluct_rain_Tr * fluct_flow_Tr + fluct_bivariate_Tr[np.fabs(fluct_bivariate_Tr) < np.finfo(np.float64).eps] = 0 # 便于比较 + return Tr, fluct_rain_Tr, fluct_flow_Tr, fluct_bivariate_Tr + + +def step3_core_identification(fluct_bivariate_Tr): + d = np.diff(fluct_bivariate_Tr, prepend=[0], append=[0]) # 计算相邻数值差分，为0代表两端点处于0区间 + d[np.fabs(d) < np.finfo(np.float64).eps] = 0 # 确保计算正确 + d = np.logical_not(d) # 求0-1数组，为真代表为0区间 + d0 = np.logical_not(np.convolve(d, [1, 1], 'valid')) # 对相邻元素做OR，代表原数组数值是否处于某一0区间，再取反表示取有效值 + valid = np.logical_or(fluct_bivariate_Tr, d0) # 有效core + d_ = np.diff(valid, prepend=[0], append=[0]) # 求差分方便取上下边沿 + beginning_core = np.argwhere(d_ == 1) # 上边沿为begin + end_core = np.argwhere(d_ == -1) - 1 # 下边沿为end + return beginning_core, end_core + + +def step4_end_rain_events(beginning_core, end_core, rain, fluct_rain_Tr, rain_min): + end_rain = end_core.copy() + rain = rain.T + for g in range(end_core.size): + if end_core[g] + 2 < fluct_rain_Tr.size and \ + (np.fabs(fluct_rain_Tr[end_core[g] + 1]) < np.finfo(np.float64).eps and np.fabs(fluct_rain_Tr[end_core[g] + 2]) < np.finfo(np.float64).eps): + # case 1&2 + if np.fabs(rain[end_core[g]]) < np.finfo(np.float64).eps: + # case 1 + while end_rain[g] > beginning_core[g] and np.fabs(rain[end_rain[g]]) < np.finfo(np.float64).eps: + end_rain[g] = end_rain[g] - 1 + else: + # case 2 + bound = beginning_core[g + 1] if g + 1 < beginning_core.size else rain.size + while end_rain[g] < bound and rain[end_rain[g]] > rain_min: + end_rain[g] = end_rain[g] + 1 + end_rain[g] = end_rain[g] - 1 # 回到最后一个 + else: + # case 3 + # 若在降水，先跳过 + while end_rain[g] >= beginning_core[g] and rain[end_rain[g]] > rain_min: + end_rain[g] = end_rain[g] - 1 + while end_rain[g] >= beginning_core[g] and rain[end_rain[g]] < rain_min: + end_rain[g] = end_rain[g] - 1 + return end_rain + + +def step5_beginning_rain_events(beginning_core, end_rain, rain, fluct_rain_Tr, rain_min): + beginning_rain = beginning_core.copy() + rain = rain.T + for g in range(beginning_core.size): + if beginning_core[g] - 2 >= 0 \ + and (np.fabs(fluct_rain_Tr[beginning_core[g] - 1]) < np.finfo(np.float64).eps and np.fabs(fluct_rain_Tr[beginning_core[g] - 2]) < np.finfo( + np.float64).eps) \ + and np.fabs(rain[beginning_core[g]]) < np.finfo(np.float64).eps: + # case 1 + while beginning_rain[g] < end_rain[g] and np.fabs(rain[beginning_rain[g]]) < np.finfo(np.float64).eps: + beginning_rain[g] = beginning_rain[g] + 1 + else: + # case 2&3 + bound = end_rain[g - 1] if g - 1 >= 0 else -1 + while beginning_rain[g] > bound and rain[beginning_rain[g]] > rain_min: + beginning_rain[g] = beginning_rain[g] - 1 + beginning_rain[g] = beginning_rain[g] + 1 # 回到第一个 + return beginning_rain + + +def step6_checks_on_rain_events(beginning_rain, end_rain, rain, rain_min, beginning_core, end_core): + rain = rain.T + beginning_rain = beginning_rain.copy() + end_rain = end_rain.copy() + if beginning_rain[0] == 0: # 掐头 + beginning_rain = beginning_rain[1:] + end_rain = end_rain[1:] + beginning_core = beginning_core[1:] + end_core = end_core[1:] + if end_rain[-1] == rain.size - 1: # 去尾 + beginning_rain = beginning_rain[:-2] + end_rain = end_rain[:-2] + beginning_core = beginning_core[:-2] + end_core = end_core[:-2] + error_time_reversed = beginning_rain > end_rain + error_wrong_delimiter = np.logical_or(rain[beginning_rain - 1] > rain_min, rain[end_rain + 1] > rain_min) + beginning_rain[error_time_reversed] = -2 + beginning_rain[error_wrong_delimiter] = -2 + end_rain[error_time_reversed] = -2 + end_rain[error_wrong_delimiter] = -2 + beginning_core[error_time_reversed] = -2 + beginning_core[error_wrong_delimiter] = -2 + end_core[error_time_reversed] = -2 + end_core[error_wrong_delimiter] = -2 + beginning_rain = beginning_rain[beginning_rain != -2] + end_rain = end_rain[end_rain != -2] + beginning_core = beginning_core[beginning_core != -2] + end_core = end_core[end_core != -2] + return beginning_rain, end_rain, beginning_core, end_core + + +def step7_end_flow_events(end_rain_checked, beginning_core, end_core, rain, fluct_rain_Tr, fluct_flow_Tr, Tr): + end_flow = np.empty(end_core.size, dtype=int) + for g in range(end_rain_checked.size): + if end_core[g] + 2 < fluct_rain_Tr.size and \ + (np.fabs(fluct_rain_Tr[end_core[g] + 1]) < np.finfo(np.float64).eps and np.fabs(fluct_rain_Tr[end_core[g] + 2]) < np.finfo(np.float64).eps): + # case 1 + end_flow[g] = end_rain_checked[g] + bound = beginning_core[g + 1] + Tr if g + 1 < beginning_core.size else rain.size + bound = min(bound, rain.size) # 防溢出 + # 若flow为负，先跳过 + while end_flow[g] < bound and fluct_flow_Tr[end_flow[g]] <= 0: + end_flow[g] = end_flow[g] + 1 + while end_flow[g] < bound and fluct_flow_Tr[end_flow[g]] > 0: + end_flow[g] = end_flow[g] + 1 + end_flow[g] = end_flow[g] - 1 # 回到最后一个 + else: + # case 2 + end_flow[g] = end_core[g] + while end_flow[g] >= beginning_core[g] and fluct_flow_Tr[end_flow[g]] <= 0: + end_flow[g] = end_flow[g] - 1 + return end_flow + + +def step8_beginning_flow_events(beginning_rain_checked, end_rain_checked, rain, beginning_core, fluct_rain_Tr, fluct_flow_Tr): + beginning_flow = np.empty(beginning_rain_checked.size, dtype=int) + for g in range(beginning_rain_checked.size): + if beginning_core[g] - 2 >= 0 \ + and (np.fabs(fluct_rain_Tr[beginning_core[g] - 1]) < np.finfo(np.float64).eps and np.fabs(fluct_rain_Tr[beginning_core[g] - 2]) < np.finfo( + np.float64).eps): + beginning_flow[g] = beginning_rain_checked[g] # case 1 + else: + beginning_flow[g] = beginning_core[g] # case 2 + while beginning_flow[g] < end_rain_checked[g] and fluct_flow_Tr[beginning_flow[g]] >= 0: + beginning_flow[g] = beginning_flow[g] + 1 + return beginning_flow + + +def step9_checks_on_flow_events(beginning_rain_checked, end_rain_checked, beginning_flow, end_flow, fluct_flow_Tr): + error_time_reversed = beginning_flow > end_flow + error_wrong_fluct = np.logical_or(np.logical_or(fluct_flow_Tr[beginning_flow] > 0, fluct_flow_Tr[end_flow] < 0), np.logical_or(beginning_flow < + beginning_rain_checked, end_flow < end_rain_checked)) + beginning_flow[error_time_reversed] = -3 + beginning_flow[error_wrong_fluct] = -3 + end_flow[error_time_reversed] = -3 + end_flow[error_wrong_fluct] = -3 + beginning_flow = beginning_flow[beginning_flow != -3] + end_flow = end_flow[end_flow != -3] + return beginning_flow, end_flow + + +def step10_checks_on_overlapping_events(beginning_rain_ungrouped, end_rain_ungrouped, beginning_flow_ungrouped, end_flow_ungrouped, time): + # rain + order1 = np.reshape(np.hstack((np.reshape(beginning_rain_ungrouped, (-1, 1)), + np.reshape(end_rain_ungrouped, (-1, 1)))), (1, -1)) + reversed1 = np.diff(order1) <= 0 + order1[np.hstack((reversed1, [[False]]))] = -2 + order1[np.hstack(([[False]], reversed1))] = -2 + order1 = order1[order1 != -2] + # flow + order2 = np.reshape(np.hstack((np.reshape(beginning_flow_ungrouped, (-1, 1)), + np.reshape(end_flow_ungrouped, (-1, 1)))), (1, -1)) + reversed2 = np.diff(order2) <= 0 + order2[np.hstack((reversed2, [[False]]))] = -3 + order2[np.hstack(([[False]], reversed2))] = -3 + order2 = order2[order2 != -3] + # group + rain_grouped = np.reshape(order1, (-1, 2)).T + beginning_rain_grouped = rain_grouped[0] + end_rain_grouped = rain_grouped[1] + flow_grouped = np.reshape(order2, (-1, 2)).T + beginning_flow_grouped = flow_grouped[0] + end_flow_grouped = flow_grouped[1] + return time[beginning_rain_grouped], time[end_rain_grouped], time[beginning_flow_grouped], time[end_flow_grouped] \ No newline at end of file diff --git a/setup.py b/setup.py index 29b5c04..e91a5b7 100644 --- a/setup.py +++ b/setup.py @@ -4,20 +4,20 @@ from setuptools import setup, find_packages setup( - name="", # 输入项目名称 - version="", # 输入版本号 + name="hydromodel_calibrate_base", # 输入项目名称 + version="0.0.1", # 输入版本号 keywords=[""], # 输入关键词 description="", # 输入概述 long_description="", # 输入描述 - url="", # 输入项目Github仓库的链接 - author="", # 输入作者名字 + url="https://github.com/iHeadWater/hydro-model-xaj", # 输入项目Github仓库的链接 + author="iHeadWater", # 输入作者名字 author_email="", # 输入作者邮箱 - license="", # 此为声明文件，一般填写 MIT_license + license="MIT_license", # 此为声明文件，一般填写 MIT_license packages=find_packages(), include_package_data=True, platforms="any", install_requires=[""], # 输入项目所用的包 - python_requires='>= ', # Python版本要求 + python_requires='>= 3.6 ', # Python版本要求 ) diff --git a/test/test_session_division.py b/test/test_session_division.py new file mode 100644 index 0000000..882e366 --- /dev/null +++ b/test/test_session_division.py @@ -0,0 +1,35 @@ +import os + +import pandas as pd + +import definitions +from hydromodel.utils.dmca_esr import step1_step2_tr_and_fluctuations_timeseries, step3_core_identification, step4_end_rain_events, \ + step5_beginning_rain_events, step6_checks_on_rain_events, step7_end_flow_events, step8_beginning_flow_events, \ + step9_checks_on_flow_events, step10_checks_on_overlapping_events + + +def test_session_division_new(): + rain = pd.read_csv(os.path.join(definitions.ROOT_DIR, 'hydromodel/example/division_rain.csv'))['rain'] + flow = pd.read_csv(os.path.join(definitions.ROOT_DIR, 'hydromodel/example/division_flow.csv'))['flow'] + time = rain.index.to_numpy() + rain = rain.to_numpy()/24 + flow = flow.to_numpy()/24 + rain_min = 0.02 + max_window = 100 + Tr, fluct_rain_Tr, fluct_flow_Tr, fluct_bivariate_Tr = step1_step2_tr_and_fluctuations_timeseries(rain, flow, rain_min, max_window) + beginning_core, end_core = step3_core_identification(fluct_bivariate_Tr) + end_rain = step4_end_rain_events(beginning_core, end_core, rain, fluct_rain_Tr, rain_min) + beginning_rain = step5_beginning_rain_events(beginning_core, end_rain, rain, fluct_rain_Tr, rain_min) + beginning_rain_checked, end_rain_checked, beginning_core, end_core = step6_checks_on_rain_events(beginning_rain, end_rain, rain, + rain_min, beginning_core, + end_core) + end_flow = step7_end_flow_events(end_rain_checked, beginning_core, end_core, rain, fluct_rain_Tr, fluct_flow_Tr, Tr) + beginning_flow = step8_beginning_flow_events(beginning_rain_checked, end_rain_checked, rain, beginning_core, fluct_rain_Tr, + fluct_flow_Tr) + beginning_flow_checked, end_flow_checked = step9_checks_on_flow_events(beginning_rain_checked, end_rain_checked, beginning_flow, + end_flow, fluct_flow_Tr) + BEGINNING_RAIN, BEGINNING_FLOW, END_RAIN, END_FLOW = step10_checks_on_overlapping_events(beginning_flow_checked, end_flow_checked, + beginning_flow_checked, + end_flow_checked, time) + print(BEGINNING_RAIN, BEGINNING_FLOW, END_RAIN, END_FLOW) + print(len(BEGINNING_RAIN), len(BEGINNING_FLOW), len(END_RAIN), len(END_FLOW)) \ No newline at end of file From f592bd64bc31f4fb83c0c1f556c0821df4caaeff Mon Sep 17 00:00:00 2001 From: forestbat Date: Mon, 9 Oct 2023 17:51:12 +0800 Subject: [PATCH 05/19] fix mistake in test_session_division.py --- test/test_session_division.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/test_session_division.py b/test/test_session_division.py index 882e366..23c3f6a 100644 --- a/test/test_session_division.py +++ b/test/test_session_division.py @@ -28,8 +28,8 @@ def test_session_division_new(): fluct_flow_Tr) beginning_flow_checked, end_flow_checked = step9_checks_on_flow_events(beginning_rain_checked, end_rain_checked, beginning_flow, end_flow, fluct_flow_Tr) - BEGINNING_RAIN, BEGINNING_FLOW, END_RAIN, END_FLOW = step10_checks_on_overlapping_events(beginning_flow_checked, end_flow_checked, + BEGINNING_RAIN, END_RAIN, BEGINNING_FLOW, END_FLOW = step10_checks_on_overlapping_events(beginning_flow_checked, end_flow_checked, beginning_flow_checked, end_flow_checked, time) - print(BEGINNING_RAIN, BEGINNING_FLOW, END_RAIN, END_FLOW) - print(len(BEGINNING_RAIN), len(BEGINNING_FLOW), len(END_RAIN), len(END_FLOW)) \ No newline at end of file + print(BEGINNING_RAIN, END_RAIN, BEGINNING_FLOW, END_FLOW) + print(len(BEGINNING_RAIN), len(END_RAIN), len(BEGINNING_FLOW), len(END_FLOW)) \ No newline at end of file From 4c9b6791d357a4cf42cc244565ddec785214eb02 Mon Sep 17 00:00:00 2001 From: forestbat Date: Mon, 9 Oct 2023 18:46:00 +0800 Subject: [PATCH 06/19] fix mistake in test_session_division.py --- test/test_session_division.py | 35 ++++++++++++++++++++++------------- 1 file changed, 22 insertions(+), 13 deletions(-) diff --git a/test/test_session_division.py b/test/test_session_division.py index 23c3f6a..cc00831 100644 --- a/test/test_session_division.py +++ b/test/test_session_division.py @@ -3,7 +3,8 @@ import pandas as pd import definitions -from hydromodel.utils.dmca_esr import step1_step2_tr_and_fluctuations_timeseries, step3_core_identification, step4_end_rain_events, \ +from hydromodel.utils.dmca_esr import step1_step2_tr_and_fluctuations_timeseries, step3_core_identification, \ + step4_end_rain_events, \ step5_beginning_rain_events, step6_checks_on_rain_events, step7_end_flow_events, step8_beginning_flow_events, \ step9_checks_on_flow_events, step10_checks_on_overlapping_events @@ -12,24 +13,32 @@ def test_session_division_new(): rain = pd.read_csv(os.path.join(definitions.ROOT_DIR, 'hydromodel/example/division_rain.csv'))['rain'] flow = pd.read_csv(os.path.join(definitions.ROOT_DIR, 'hydromodel/example/division_flow.csv'))['flow'] time = rain.index.to_numpy() - rain = rain.to_numpy()/24 - flow = flow.to_numpy()/24 + rain = rain.to_numpy() / 24 + flow = flow.to_numpy() / 24 rain_min = 0.02 max_window = 100 - Tr, fluct_rain_Tr, fluct_flow_Tr, fluct_bivariate_Tr = step1_step2_tr_and_fluctuations_timeseries(rain, flow, rain_min, max_window) + Tr, fluct_rain_Tr, fluct_flow_Tr, fluct_bivariate_Tr = step1_step2_tr_and_fluctuations_timeseries(rain, flow, + rain_min, + max_window) beginning_core, end_core = step3_core_identification(fluct_bivariate_Tr) end_rain = step4_end_rain_events(beginning_core, end_core, rain, fluct_rain_Tr, rain_min) beginning_rain = step5_beginning_rain_events(beginning_core, end_rain, rain, fluct_rain_Tr, rain_min) - beginning_rain_checked, end_rain_checked, beginning_core, end_core = step6_checks_on_rain_events(beginning_rain, end_rain, rain, - rain_min, beginning_core, + beginning_rain_checked, end_rain_checked, beginning_core, end_core = step6_checks_on_rain_events(beginning_rain, + end_rain, rain, + rain_min, + beginning_core, end_core) end_flow = step7_end_flow_events(end_rain_checked, beginning_core, end_core, rain, fluct_rain_Tr, fluct_flow_Tr, Tr) - beginning_flow = step8_beginning_flow_events(beginning_rain_checked, end_rain_checked, rain, beginning_core, fluct_rain_Tr, - fluct_flow_Tr) - beginning_flow_checked, end_flow_checked = step9_checks_on_flow_events(beginning_rain_checked, end_rain_checked, beginning_flow, + beginning_flow = step8_beginning_flow_events(beginning_rain_checked, end_rain_checked, rain, beginning_core, + fluct_rain_Tr, + fluct_flow_Tr) + beginning_flow_checked, end_flow_checked = step9_checks_on_flow_events(beginning_rain_checked, end_rain_checked, + beginning_flow, end_flow, fluct_flow_Tr) - BEGINNING_RAIN, END_RAIN, BEGINNING_FLOW, END_FLOW = step10_checks_on_overlapping_events(beginning_flow_checked, end_flow_checked, - beginning_flow_checked, - end_flow_checked, time) + BEGINNING_RAIN, END_RAIN, BEGINNING_FLOW, END_FLOW = step10_checks_on_overlapping_events(beginning_rain_checked, + end_rain_checked, + beginning_flow_checked, + end_flow_checked, + time) print(BEGINNING_RAIN, END_RAIN, BEGINNING_FLOW, END_FLOW) - print(len(BEGINNING_RAIN), len(END_RAIN), len(BEGINNING_FLOW), len(END_FLOW)) \ No newline at end of file + print(len(BEGINNING_RAIN), len(END_RAIN), len(BEGINNING_FLOW), len(END_FLOW)) From 235592ab507a4123d664cf26e5971944b14ac668 Mon Sep 17 00:00:00 2001 From: forestbat Date: Tue, 10 Oct 2023 15:03:20 +0800 Subject: [PATCH 07/19] change pypi distribute workflow --- .github/workflows/publish-to-pypi.yml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml index 11a4285..daeafc1 100644 --- a/.github/workflows/publish-to-pypi.yml +++ b/.github/workflows/publish-to-pypi.yml @@ -1,6 +1,8 @@ name: Publish Python distributions to PyPI -on: push +on: + push: + workflow_dispatch: jobs: build-n-publish: @@ -33,4 +35,4 @@ jobs: if: startsWith(github.ref, 'refs/tags') uses: pypa/gh-action-pypi-publish@master with: - password: ${{ secrets.PYPI_API_TOKEN }} + password: ${{ secrets.HYDRO_XAJ_TOKEN }} From 18071f5429ace069046eb79f738c6495279f41c8 Mon Sep 17 00:00:00 2001 From: forestbat Date: Tue, 10 Oct 2023 15:17:40 +0800 Subject: [PATCH 08/19] update workflow and add user --- .github/workflows/publish-to-pypi.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml index daeafc1..f300259 100644 --- a/.github/workflows/publish-to-pypi.yml +++ b/.github/workflows/publish-to-pypi.yml @@ -35,4 +35,5 @@ jobs: if: startsWith(github.ref, 'refs/tags') uses: pypa/gh-action-pypi-publish@master with: + user: __token__ password: ${{ secrets.HYDRO_XAJ_TOKEN }} From 74c2c3b95fc8ccd7979be1971222342e1bb3831a Mon Sep 17 00:00:00 2001 From: forestbat Date: Tue, 10 Oct 2023 15:57:38 +0800 Subject: [PATCH 09/19] update workflow --- .github/workflows/publish-to-pypi.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml index f300259..2bc7304 100644 --- a/.github/workflows/publish-to-pypi.yml +++ b/.github/workflows/publish-to-pypi.yml @@ -32,8 +32,7 @@ jobs: . - name: Publish distribution to PyPI - if: startsWith(github.ref, 'refs/tags') - uses: pypa/gh-action-pypi-publish@master + uses: pypa/gh-action-pypi-publish@release/v1 with: user: __token__ password: ${{ secrets.HYDRO_XAJ_TOKEN }} From 6ceb8945ce47deb2893861b84cb951584476a7e7 Mon Sep 17 00:00:00 2001 From: forestbat Date: Tue, 10 Oct 2023 16:15:37 +0800 Subject: [PATCH 10/19] update setup.py to add long_description --- setup.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index e91a5b7..1a34d50 100644 --- a/setup.py +++ b/setup.py @@ -3,12 +3,16 @@ from setuptools import setup, find_packages +with open("README.md") as readme_file: + readme = readme_file.read() + setup( name="hydromodel_calibrate_base", # 输入项目名称 version="0.0.1", # 输入版本号 keywords=[""], # 输入关键词 description="", # 输入概述 - long_description="", # 输入描述 + long_description=readme, + long_description_content_type="text/markdown", url="https://github.com/iHeadWater/hydro-model-xaj", # 输入项目Github仓库的链接 author="iHeadWater", # 输入作者名字 From c12830c6b56fd2bc1484eb07665d55dffcd7480b Mon Sep 17 00:00:00 2001 From: forestbat Date: Tue, 10 Oct 2023 16:17:22 +0800 Subject: [PATCH 11/19] update setup.py to add long_description --- setup.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/setup.py b/setup.py index 1a34d50..af6d280 100644 --- a/setup.py +++ b/setup.py @@ -13,12 +13,10 @@ description="", # 输入概述 long_description=readme, long_description_content_type="text/markdown", - url="https://github.com/iHeadWater/hydro-model-xaj", # 输入项目Github仓库的链接 author="iHeadWater", # 输入作者名字 author_email="", # 输入作者邮箱 license="MIT_license", # 此为声明文件，一般填写 MIT_license - packages=find_packages(), include_package_data=True, platforms="any", From 514dbdab1228844d479ea734f0eb4de674943bb4 Mon Sep 17 00:00:00 2001 From: forestbat Date: Thu, 12 Oct 2023 16:33:53 +0800 Subject: [PATCH 12/19] Update setup.py change pypi project name --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index af6d280..d019c08 100644 --- a/setup.py +++ b/setup.py @@ -7,7 +7,7 @@ readme = readme_file.read() setup( - name="hydromodel_calibrate_base", # 输入项目名称 + name="hydromodel", # 输入项目名称 version="0.0.1", # 输入版本号 keywords=[""], # 输入关键词 description="", # 输入概述 From 9865afe3167d1c539a26f881e36f1d168fd17e36 Mon Sep 17 00:00:00 2001 From: ouyangwenyu Date: Sat, 28 Oct 2023 09:49:14 +0800 Subject: [PATCH 13/19] add bim package for env --- environment-dev.yml | 5 +++++ environment.yml | 3 ++- setup.py | 19 ++++++++++++------- test/test_data.py | 2 +- 4 files changed, 20 insertions(+), 9 deletions(-) diff --git a/environment-dev.yml b/environment-dev.yml index 81764f5..288a934 100644 --- a/environment-dev.yml +++ b/environment-dev.yml @@ -13,9 +13,14 @@ dependencies: - spotpy=1.5.14 - seaborn - tqdm + - yaml + - bmipy + # for dev - sphinx - pytest - black + - flake8 + # pip - pip - pip: - hydrodataset diff --git a/environment.yml b/environment.yml index 02544ac..da831b9 100644 --- a/environment.yml +++ b/environment.yml @@ -13,7 +13,8 @@ dependencies: - spotpy=1.5.14 - seaborn - tqdm - - pytest + - yaml + - bmipy - pip - pip: - hydrodataset diff --git a/setup.py b/setup.py index d019c08..e143dd7 100644 --- a/setup.py +++ b/setup.py @@ -1,11 +1,16 @@ -#!/usr/bin/env python -# -*- coding:utf-8 -*- - +""" +Author: Wenyu Ouyang +Date: 2023-10-28 09:16:46 +LastEditTime: 2023-10-28 09:27:22 +LastEditors: Wenyu Ouyang +Description: setup.py for hydromodel package +FilePath: \hydro-model-xaj\setup.py +Copyright (c) 2023-2024 Wenyu Ouyang. All rights reserved. +""" +import pathlib from setuptools import setup, find_packages -with open("README.md") as readme_file: - readme = readme_file.read() - +readme = pathlib.Path("README.md").read_text() setup( name="hydromodel", # 输入项目名称 version="0.0.1", # 输入版本号 @@ -21,5 +26,5 @@ include_package_data=True, platforms="any", install_requires=[""], # 输入项目所用的包 - python_requires='>= 3.6 ', # Python版本要求 + python_requires=">= 3.7 ", # Python版本要求 ) diff --git a/test/test_data.py b/test/test_data.py index 6844fc7..1bbcb2a 100644 --- a/test/test_data.py +++ b/test/test_data.py @@ -1,7 +1,7 @@ """ Author: Wenyu Ouyang Date: 2022-10-25 21:16:22 -LastEditTime: 2022-11-28 14:50:30 +LastEditTime: 2023-10-28 09:18:48 LastEditors: Wenyu Ouyang Description: Test for data preprocess FilePath: \hydro-model-xaj\test\test_data.py From 7a7ad304778dae3472655170eafdd89690c89548 Mon Sep 17 00:00:00 2001 From: ouyangwenyu Date: Sat, 28 Oct 2023 15:56:11 +0800 Subject: [PATCH 14/19] delete xaj dir --- {xaj => hydromodel/app}/dockerfile | 0 .../calibrate}/calibrate_ga_xaj_bmi.py | 2 +- .../calibrate}/calibrate_sceua_xaj_bmi.py | 2 +- {xaj => hydromodel/models}/configuration.py | 2 +- hydromodel/models/xaj.py | 173 +++ {xaj => hydromodel/models}/xaj_bmi.py | 154 ++- {xaj => hydromodel/utils}/constant_unit.py | 0 test/test_xaj_bmi.py | 4 +- xaj/__init__.py | 0 xaj/params.py | 122 -- xaj/xajmodel.py | 1060 ----------------- 11 files changed, 268 insertions(+), 1251 deletions(-) rename {xaj => hydromodel/app}/dockerfile (100%) rename {xaj => hydromodel/calibrate}/calibrate_ga_xaj_bmi.py (99%) rename {xaj => hydromodel/calibrate}/calibrate_sceua_xaj_bmi.py (99%) rename {xaj => hydromodel/models}/configuration.py (92%) rename {xaj => hydromodel/models}/xaj_bmi.py (67%) rename {xaj => hydromodel/utils}/constant_unit.py (100%) delete mode 100644 xaj/__init__.py delete mode 100644 xaj/params.py delete mode 100644 xaj/xajmodel.py diff --git a/xaj/dockerfile b/hydromodel/app/dockerfile similarity index 100% rename from xaj/dockerfile rename to hydromodel/app/dockerfile diff --git a/xaj/calibrate_ga_xaj_bmi.py b/hydromodel/calibrate/calibrate_ga_xaj_bmi.py similarity index 99% rename from xaj/calibrate_ga_xaj_bmi.py rename to hydromodel/calibrate/calibrate_ga_xaj_bmi.py index 555b671..71dd54a 100644 --- a/xaj/calibrate_ga_xaj_bmi.py +++ b/hydromodel/calibrate/calibrate_ga_xaj_bmi.py @@ -18,7 +18,7 @@ from hydromodel.utils import stat from hydromodel.utils.stat import statRmse from hydromodel.visual.hydro_plot import plot_sim_and_obs, plot_train_iteration -from xaj.xaj_bmi import xajBmi +from hydromodel.models.xaj_bmi import xajBmi def evaluate(individual, x_input, y_true, warmup_length, model): diff --git a/xaj/calibrate_sceua_xaj_bmi.py b/hydromodel/calibrate/calibrate_sceua_xaj_bmi.py similarity index 99% rename from xaj/calibrate_sceua_xaj_bmi.py rename to hydromodel/calibrate/calibrate_sceua_xaj_bmi.py index ac7cf9b..404cd4b 100644 --- a/xaj/calibrate_sceua_xaj_bmi.py +++ b/hydromodel/calibrate/calibrate_sceua_xaj_bmi.py @@ -4,7 +4,7 @@ from spotpy.parameter import Uniform, ParameterSet from spotpy.objectivefunctions import rmse from hydromodel.models.model_config import MODEL_PARAM_DICT -from xaj.xaj_bmi import xajBmi +from hydromodel.models.xaj_bmi import xajBmi class SpotSetup(object): diff --git a/xaj/configuration.py b/hydromodel/models/configuration.py similarity index 92% rename from xaj/configuration.py rename to hydromodel/models/configuration.py index 708f736..219abd8 100644 --- a/xaj/configuration.py +++ b/hydromodel/models/configuration.py @@ -1,6 +1,6 @@ import yaml import numpy as np -from xaj.xajmodel import xaj_state as xaj_state +from hydromodel.models.xaj import xaj_state as xaj_state def read_config(config_file): diff --git a/hydromodel/models/xaj.py b/hydromodel/models/xaj.py index 614d3c0..8aaf9ba 100644 --- a/hydromodel/models/xaj.py +++ b/hydromodel/models/xaj.py @@ -8,6 +8,7 @@ from scipy.special import gamma from hydromodel.models.model_config import MODEL_PARAM_DICT +from xaj.params import MODEL_PARAM_DICT PRECISION = 1e-5 @@ -885,3 +886,175 @@ def xaj( if return_state: return q_sim, es, *w, s, fr, qi, qg return q_sim, es + + +def xaj_state( + p_and_e, + params: Union[np.array, list], + return_state=True, + warmup_length=365, + **kwargs, +) -> Union[tuple, np.array]: + # default values for some function parameters + model_name = kwargs["name"] if "name" in kwargs else "xaj" + source_type = kwargs["source_type"] if "source_type" in kwargs else "sources" + source_book = kwargs["source_book"] if "source_book" in kwargs else "HF" + # params + param_ranges = MODEL_PARAM_DICT[model_name]["param_range"] + if model_name == "xaj": + route_method = "CSL" + elif model_name == "xaj_mz": + route_method = "MZ" + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CS' or 'MZ'!" + ) + xaj_params = [ + (value[1] - value[0]) * params[:, i] + value[0] + for i, (key, value) in enumerate(param_ranges.items()) + ] + k = xaj_params[0] + b = xaj_params[1] + im = xaj_params[2] + um = xaj_params[3] + lm = xaj_params[4] + dm = xaj_params[5] + c = xaj_params[6] + sm = xaj_params[7] + ex = xaj_params[8] + ki_ = xaj_params[9] + kg_ = xaj_params[10] + # ki+kg should be smaller than 1; if not, we scale them + ki = np.where(ki_ + kg_ < 1.0, ki_, (1.0 - PRECISION) / (ki_ + kg_) * ki_) + kg = np.where(ki_ + kg_ < 1.0, kg_, (1.0 - PRECISION) / (ki_ + kg_) * kg_) + if route_method == "CSL": + cs = xaj_params[11] + l = xaj_params[12] + + elif route_method == "MZ": + # we will use routing method from mizuRoute -- http://www.geosci-model-dev.net/9/2223/2016/ + a = xaj_params[11] + theta = xaj_params[12] + # make it as a parameter + kernel_size = int(xaj_params[15]) + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CS' or 'MZ'!" + ) + ci = xaj_params[13] + cg = xaj_params[14] + + + # initialize state values + # if warmup_length > 0: + # p_and_e_warmup = p_and_e[0:warmup_length, :, :] + # _q, _e, *w0, s0, fr0, qi0, qg0 = xaj_state( + # p_and_e_warmup, + # params, + # return_state=True, + # warmup_length=0, + # **kwargs, + # ) + # else: + w0 = (0.5 * um, 0.5 * lm, 0.5 * dm) + s0 = 0.5 * sm + fr0 = np.full(ex.shape, 0.1) + qi0 = np.full(ci.shape, 0.1) + qg0 = np.full(cg.shape, 0.1) + + #state_variables + # inputs = p_and_e[warmup_length:, :, :] + inputs = p_and_e[:, :, :] + runoff_ims_ = np.full(inputs.shape[:2], 0.0) + rss_ = np.full(inputs.shape[:2], 0.0) + ris_ = np.full(inputs.shape[:2], 0.0) + rgs_ = np.full(inputs.shape[:2], 0.0) + es_ = np.full(inputs.shape[:2], 0.0) + for i in range(inputs.shape[0]): + if i == 0: + (r, rim, e, pe), w = generation( + inputs[i, :, :], k, b, im, um, lm, dm, c, *w0 + ) + if source_type == "sources": + (rs, ri, rg), (s, fr) = sources( + pe, r, sm, ex, ki, kg, s0, fr0, book=source_book + ) + elif source_type == "sources5mm": + (rs, ri, rg), (s, fr) = sources5mm( + pe, r, sm, ex, ki, kg, s0, fr0, book=source_book + ) + else: + raise NotImplementedError("No such divide-sources method") + else: + (r, rim, e, pe), w = generation( + inputs[i, :, :], k, b, im, um, lm, dm, c, *w + ) + if source_type == "sources": + (rs, ri, rg), (s, fr) = sources( + pe, r, sm, ex, ki, kg, s, fr, book=source_book + ) + elif source_type == "sources5mm": + (rs, ri, rg), (s, fr) = sources5mm( + pe, r, sm, ex, ki, kg, s, fr, book=source_book + ) + else: + raise NotImplementedError("No such divide-sources method") + # impevious part is pe * im + runoff_ims_[i, :] = rim + # so for non-imprvious part, the result should be corrected + rss_[i, :] = rs * (1 - im) + ris_[i, :] = ri * (1 - im) + rgs_[i, :] = rg * (1 - im) + es_[i, :] = e + # seq, batch, feature + runoff_im = np.expand_dims(runoff_ims_, axis=2) + rss = np.expand_dims(rss_, axis=2) + es = np.expand_dims(es_, axis=2) + + qs = np.full(inputs.shape[:2], 0.0) + + if route_method == "CSL": + qt = np.full(inputs.shape[:2], 0.0) + for i in range(inputs.shape[0]): + if i == 0: + qi = linear_reservoir(ris_[i], ci, qi0) + qg = linear_reservoir(rgs_[i], cg, qg0) + else: + qi = linear_reservoir(ris_[i], ci, qi) + qg = linear_reservoir(rgs_[i], cg, qg) + qs_ = rss_[i] + qt[i, :] = qs_ + qi + qg + + for j in range(len(l)): + # print(range(len(l))) + lag = int(l[j]) + for i in range(0,lag): + qs[i, j] = qt[i, j] + if i == lag: + break + for i in range(lag, inputs.shape[0]): + # print(inputs.shape[0]) + qs[i, j] = cs[j] * qs[i - 1, j] + (1 - cs[j]) * qt[i - lag, j] + + elif route_method == "MZ": + rout_a = a.repeat(rss.shape[0]).reshape(rss.shape) + rout_b = theta.repeat(rss.shape[0]).reshape(rss.shape) + conv_uh = uh_gamma(rout_a, rout_b, kernel_size) + qs_ = uh_conv(runoff_im + rss, conv_uh) + for i in range(inputs.shape[0]): + if i == 0: + qi = linear_reservoir(ris_[i], ci, qi0) + qg = linear_reservoir(rgs_[i], cg, qg0) + else: + qi = linear_reservoir(ris_[i], ci, qi) + qg = linear_reservoir(rgs_[i], cg, qg) + qs[i, :] = qs_[i, :, 0] + qi + qg + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CSL' or 'MZ'!" + ) + + # seq, batch, feature + q_sim = np.expand_dims(qs, axis=2) + if return_state: + return q_sim, es, *w, s, fr, qi, qg diff --git a/xaj/xaj_bmi.py b/hydromodel/models/xaj_bmi.py similarity index 67% rename from xaj/xaj_bmi.py rename to hydromodel/models/xaj_bmi.py index f8fc628..94f1d02 100644 --- a/xaj/xaj_bmi.py +++ b/hydromodel/models/xaj_bmi.py @@ -2,8 +2,8 @@ from bmipy import Bmi import numpy as np -from xaj import configuration -from xaj.xajmodel import xaj_route, xaj_runoff +from hydromodel.models import configuration +from hydromodel.models.xajmodel import xaj_route, xaj_runoff import datetime import logging @@ -14,46 +14,62 @@ class xajBmi(Bmi): """Empty model wrapped in a BMI interface.""" + name = "hydro-model-xaj" input_var_names = ("precipitation", "ETp") output_var_names = ("ET", "discharge") - var_units = {"precipitation": "mm/day", "ETp": "mm/day", "discharge": "mm/day", "ET": "mm/day"} + var_units = { + "precipitation": "mm/day", + "ETp": "mm/day", + "discharge": "mm/day", + "ET": "mm/day", + } def __init__(self): - """Create a BmiHeat model that is ready for initialization.""" + """Create a Bmi model that is ready for initialization.""" self.time_step = 0 def initialize(self, config_file, params, p_and_e): try: logger.info("xaj: initialize_model") config = configuration.read_config(config_file) - model_name = config['model_name'] - source_type = config['source_type'] - source_book = config['source_book'] + model_name = config["model_name"] + source_type = config["source_type"] + source_book = config["source_book"] # forcing_data = pd.read_csv(config['forcing_data']) model_info = { - 'model_name': model_name, - 'source_type': source_type, - 'source_book': source_book, - + "model_name": model_name, + "source_type": source_type, + "source_book": source_book, } # p_and_e_df, p_and_e = configuration.extract_forcing(forcing_data) - p_and_e_warmup = p_and_e[0:config['warmup_length'], :, :] - self.q_sim_state, self.es_state, self.w0, self.w1, self.w2, self.s0, self.fr0, self.qi0, self.qg0 = configuration.warmup( - p_and_e_warmup, params, config['warmup_length'], model_info) + p_and_e_warmup = p_and_e[0 : config["warmup_length"], :, :] + ( + self.q_sim_state, + self.es_state, + self.w0, + self.w1, + self.w2, + self.s0, + self.fr0, + self.qi0, + self.qg0, + ) = configuration.warmup( + p_and_e_warmup, params, config["warmup_length"], model_info + ) self.params = params - self.warmup_length = config['warmup_length'] - self._start_time_str = config['start_time_str'] - self._end_time_str = config['end_time_str'] - self._time_units = config['time_units'] + self.warmup_length = config["warmup_length"] + self._start_time_str = config["start_time_str"] + self._end_time_str = config["end_time_str"] + self._time_units = config["time_units"] # self.p_and_e_df = p_and_e_df self.p_and_e = p_and_e self.config = config self.model_info = model_info - train_period = config['train_period'] - test_period = config['test_period'] + train_period = config["train_period"] + test_period = config["test_period"] self.train_start_time_str = train_period[0] self.train_end_time_str = train_period[1] self.test_start_time_str = test_period[0] @@ -61,6 +77,7 @@ def initialize(self, config_file, params, p_and_e): except: import traceback + traceback.print_exc() raise @@ -68,28 +85,40 @@ def update(self): """Update model for a single time step.""" self.time_step += 1 - p_and_e_sim = self.p_and_e[self.warmup_length:self.time_step + self.warmup_length] + p_and_e_sim = self.p_and_e[ + self.warmup_length : self.time_step + self.warmup_length + ] # p_and_e_sim = self.p_and_e[0:self.time_step] - self.runoff_im, self.rss_, self.ris_, self.rgs_, self.es_runoff, self.rss = xaj_runoff(p_and_e_sim, - w0=self.w0, s0=self.s0, - fr0=self.fr0, - params_runoff=self.params, - return_state=False, - model_info=self.model_info, - ) + ( + self.runoff_im, + self.rss_, + self.ris_, + self.rgs_, + self.es_runoff, + self.rss, + ) = xaj_runoff( + p_and_e_sim, + w0=self.w0, + s0=self.s0, + fr0=self.fr0, + params_runoff=self.params, + return_state=False, + model_info=self.model_info, + ) if self.time_step + self.warmup_length >= self.p_and_e.shape[0]: - q_sim, es = xaj_route(p_and_e_sim, - params_route=self.params, - model_info=self.model_info, - runoff_im=self.runoff_im, - rss_=self.rss_, - ris_=self.ris_, - rgs_=self.rgs_, - rss=self.rss, - qi0=self.qi0, - qg0=self.qg0, - es=self.es_runoff, - ) + q_sim, es = xaj_route( + p_and_e_sim, + params_route=self.params, + model_info=self.model_info, + runoff_im=self.runoff_im, + rss_=self.rss_, + ris_=self.ris_, + rgs_=self.rgs_, + rss=self.rss, + qi0=self.qi0, + qg0=self.qg0, + es=self.es_runoff, + ) self.p_sim = p_and_e_sim[:, :, 0] self.e_sim = p_and_e_sim[:, :, 1] # q_sim = convert_unit( @@ -128,7 +157,7 @@ def get_var_grid(self, name: str) -> int: raise NotImplementedError() def get_var_type(self, name: str) -> str: - return 'float64' + return "float64" def get_var_units(self, name: str) -> str: return self.var_units[name] @@ -143,26 +172,26 @@ def get_var_location(self, name: str) -> str: raise NotImplementedError() def get_start_time(self, period): - if period == 'train': + if period == "train": return self.start_Time(self.train_start_time_str) - if period == 'test': + if period == "test": return self.start_Time(self.test_start_time_str) # return self.start_Time(self._start_time_str) def get_current_time(self): # return self.start_Time(self._start_time_str) + datetime.timedelta(self.time_step+self.warmup_length) - if self._time_units == 'hours': + if self._time_units == "hours": time_step = datetime.timedelta(hours=self.time_step) - elif self._time_units == 'days': + elif self._time_units == "days": time_step = datetime.timedelta(days=self.time_step) return self.start_Time(self._start_time_str) + time_step def get_end_time(self, period): - if period == 'train': + if period == "train": return self.end_Time(self.train_end_time_str) - if period == 'test': + if period == "test": return self.end_Time(self.test_end_time_str) - # return self.end_Time(self._end_time_str) + # return self.end_Time(self._end_time_str) def get_time_units(self) -> str: return self._time_units @@ -175,24 +204,20 @@ def get_value(self, name: str) -> None: return self.get_value_ptr(name).flatten() def get_value_ptr(self, name: str) -> np.ndarray: - if name == 'discharge': + if name == "discharge": return self.q_sim - elif name == 'ET': + elif name == "ET": return self.es - def get_value_at_indices( - self, name: str, inds: np.ndarray - ) -> np.ndarray: - + def get_value_at_indices(self, name: str, inds: np.ndarray) -> np.ndarray: return self.get_value_ptr(name).take(inds) def set_value(self, name: str, src: np.ndarray): - val = self.get_value_ptr(name) val[:] = src.reshape(val.shape) def set_value_at_indices( - self, name: str, inds: np.ndarray, src: np.ndarray + self, name: str, inds: np.ndarray, src: np.ndarray ) -> None: val = self.get_value_ptr(name) val.flat[inds] = src @@ -246,12 +271,11 @@ def get_grid_face_nodes(self, grid: int, face_nodes: np.ndarray) -> np.ndarray: raise NotImplementedError() def get_grid_nodes_per_face( - self, grid: int, nodes_per_face: np.ndarray + self, grid: int, nodes_per_face: np.ndarray ) -> np.ndarray: raise NotImplementedError() def start_Time(self, _start_time_str): - try: if " " in _start_time_str: date, time = _start_time_str.split(" ") @@ -263,18 +287,18 @@ def start_Time(self, _start_time_str): if time: hour, minute, second = time.split(":") - # self._startTime = self._startTime.replace(hour=int(hour), - # minute=int(minute), + # self._startTime = self._startTime.replace(hour=int(hour), + # minute=int(minute), # second=int(second)) - self._startTime = datetime.datetime(int(year), int(month), int(day), int(hour), int(minute), - int(second)) + self._startTime = datetime.datetime( + int(year), int(month), int(day), int(hour), int(minute), int(second) + ) except ValueError: raise ValueError("Invalid start date format!") return self._startTime def end_Time(self, _end_time_str): - try: if " " in _end_time_str: date, time = _end_time_str.split(" ") @@ -286,7 +310,9 @@ def end_Time(self, _end_time_str): if time: hour, minute, second = time.split(":") - self._endTime = datetime.datetime(int(year), int(month), int(day), int(hour), int(minute), int(second)) + self._endTime = datetime.datetime( + int(year), int(month), int(day), int(hour), int(minute), int(second) + ) except ValueError: raise ValueError("Invalid start date format!") return self._endTime diff --git a/xaj/constant_unit.py b/hydromodel/utils/constant_unit.py similarity index 100% rename from xaj/constant_unit.py rename to hydromodel/utils/constant_unit.py diff --git a/test/test_xaj_bmi.py b/test/test_xaj_bmi.py index 0ee679b..c61808c 100644 --- a/test/test_xaj_bmi.py +++ b/test/test_xaj_bmi.py @@ -1,8 +1,8 @@ import logging import definitions -from xaj.configuration import read_config -from xaj.xaj_bmi import xajBmi +from hydromodel.models.configuration import read_config +from hydromodel.models.xaj_bmi import xajBmi logging.basicConfig(level=logging.INFO) diff --git a/xaj/__init__.py b/xaj/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/xaj/params.py b/xaj/params.py deleted file mode 100644 index 49f3393..0000000 --- a/xaj/params.py +++ /dev/null @@ -1,122 +0,0 @@ -""" -Author: Wenyu Ouyang -Date: 2022-10-25 21:16:22 -LastEditTime: 2022-12-25 16:06:05 -LastEditors: Wenyu Ouyang -Description: some basic config for hydro-model-xaj models -FilePath: \hydro-model-xaj\hydromodel\models\model_config.py -Copyright (c) 2021-2022 Wenyu Ouyang. All rights reserved. -""" -from collections import OrderedDict - -# NOTE: Don't change the parameter settings - -MODEL_PARAM_DICT = { - "xaj": { - "param_name": [ - # Allen, R.G., L. Pereira, D. Raes, and M. Smith, 1998. - # Crop Evapotranspiration, Food and Agriculture Organization of the United Nations, - # Rome, Italy. FAO publication 56. ISBN 92-5-104219-5. 290p. - "K", # ratio of potential evapotranspiration to reference crop evaporation generally from Allen, 1998 - "B", # The exponent of the tension water capacity curve - "IM", # The ratio of the impervious to the total area of the basin - "UM", # Tension water capacity in the upper layer - "LM", # Tension water capacity in the lower layer - "DM", # Tension water capacity in the deepest layer - "C", # The coefficient of deep evapotranspiration - "SM", # The areal mean of the free water capacity of surface soil layer - "EX", # The exponent of the free water capacity curve - "KI", # Outflow coefficients of interflow - "KG", # Outflow coefficients of groundwater - "CS", # The recession constant of channel system - "L", # Lag time - "CI", # The recession constant of the lower interflow - "CG", # The recession constant of groundwater storage - ], - "param_range": OrderedDict( - { - "K": [0.1, 0.1], - "B": [0.1, 0.1], - "IM": [0.1, 0.1], - "UM": [10, 10.0], - "LM": [70.0, 70.0], - "DM": [80.0, 80.0], - "C": [0.1, 0.1], - "SM": [50, 50], - "EX": [1.2, 1.2], - "KI": [0.3, 0.3], - "KG": [0.3, 0.3], - "CS": [0.5, 0.5], - "L": [5.0, 5.0], # unit is day - "CI": [0.5, 0.5], - "CG": [0.98, 0.98], - } - ), - }, - "xaj_mz": { - "param_name": [ - # Allen, R.G., L. Pereira, D. Raes, and M. Smith, 1998. - # Crop Evapotranspiration, Food and Agriculture Organization of the United Nations, - # Rome, Italy. FAO publication 56. ISBN 92-5-104219-5. 290p. - "K", # ratio of potential evapotranspiration to reference crop evaporation generally from Allen, 1998 - "B", # The exponent of the tension water capacity curve - "IM", # The ratio of the impervious to the total area of the basin - "UM", # Tension water capacity in the upper layer - "LM", # Tension water capacity in the lower layer - "DM", # Tension water capacity in the deepest layer - "C", # The coefficient of deep evapotranspiration - "SM", # The areal mean of the free water capacity of surface soil layer - "EX", # The exponent of the free water capacity curve - "KI", # Outflow coefficients of interflow - "KG", # Outflow coefficients of groundwater - "A", # parameter of mizuRoute - "THETA", # parameter of mizuRoute - "CI", # The recession constant of the lower interflow - "CG", # The recession constant of groundwater storage - "KERNEL", # kernel size of mizuRoute unit hydrograph when using convolution method - ], - "param_range": OrderedDict( - { - "K": [0.1, 1.0], - "B": [0.1, 0.4], - "IM": [0.01, 0.1], - "UM": [0.0, 20.0], - "LM": [60.0, 90.0], - "DM": [60.0, 120.0], - "C": [0.0, 0.2], - "SM": [1.0, 100.0], - "EX": [1.0, 1.5], - "KI": [0.0, 0.7], - "KG": [0.0, 0.7], - "A": [0.0, 2.9], - "THETA": [0.0, 6.5], - "CI": [0.0, 0.9], - "CG": [0.98, 0.998], - "KERNEL": [1, 15], - } - ), - }, - "gr4j": { - "param_name": ["x1", "x2", "x3", "x4"], - "param_range": OrderedDict( - { - "x1": [100.0, 1200.0], - "x2": [-5.0, 3.0], - "x3": [20.0, 300.0], - "x4": [1.1, 2.9], - } - ), - }, - "hymod": { - "param_name": ["cmax", "bexp", "alpha", "ks", "kq"], - "param_range": OrderedDict( - { - "cmax": [1.0, 500.0], - "bexp": [0.1, 2.0], - "alpha": [0.1, 0.99], - "ks": [0.001, 0.10], - "kq": [0.1, 0.99], - } - ), - }, -} diff --git a/xaj/xajmodel.py b/xaj/xajmodel.py deleted file mode 100644 index 2027955..0000000 --- a/xaj/xajmodel.py +++ /dev/null @@ -1,1060 +0,0 @@ -""" -Core code for XinAnJiang model -""" -import logging -from typing import Union -import numpy as np -from numba import jit -from scipy.special import gamma - -from xaj.params import MODEL_PARAM_DICT - -PRECISION = 1e-5 - - -@jit -def calculate_evap(lm, c, wu0, wl0, prcp, pet) -> tuple[np.array, np.array, np.array]: - """ - Three-layers evaporation model from "Watershed Hydrologic Simulation" written by Prof. RenJun Zhao. - The book is Chinese, and its name is 《流域水文模拟》; - The three-layers evaporation model is described in Page 76; - The method is same with that in Page 22-23 in "Hydrologic Forecasting (5-th version)" written by Prof. Weimin Bao. - This book's Chinese name is 《水文预报》 - - Parameters - ---------- - lm - average soil moisture storage capacity of lower layer - c - coefficient of deep layer - wu0 - initial soil moisture of upper layer; update in each time step - wl0 - initial soil moisture of lower layer; update in each time step - prcp - basin mean precipitation - pet - potential evapotranspiration - - Returns - ------- - tuple[np.array,np.array,np.array] - eu/el/ed are evaporation from upper/lower/deeper layer, respectively - """ - eu = np.where(wu0 + prcp >= pet, pet, wu0 + prcp) - ed = np.where((wl0 < c * lm) & (wl0 < c * (pet - eu)), c * (pet - eu) - wl0, 0.0) - el = np.where( - wu0 + prcp >= pet, - 0.0, - np.where( - wl0 >= c * lm, - (pet - eu) * wl0 / lm, - np.where(wl0 >= c * (pet - eu), c * (pet - eu), wl0), - ), - ) - return eu, el, ed - - -@jit -def calculate_prcp_runoff(b, im, wm, w0, pe) -> tuple[np.array, np.array]: - """ - Calculates the amount of runoff generated from rainfall after entering the underlying surface. - - Same in "Watershed Hydrologic Simulation" and "Hydrologic Forecasting (5-th version)" - - Parameters - ---------- - b - B; exponent coefficient - im - IMP; imperiousness coefficient - wm - average soil moisture storage capacity - w0 - initial soil moisture - pe - net precipitation - - Returns - ------- - tuple[np.array,np.array] - r -- runoff; r_im -- runoff of impervious part - """ - wmm = wm * (1.0 + b) - a = wmm * (1.0 - (1.0 - w0 / wm) ** (1.0 / (1.0 + b))) - if np.isnan(a).any(): - raise ArithmeticError("Please check if w0>wm or b is a negative value!") - r_cal = np.where( - pe > 0.0, - np.where( - pe + a < wmm, - # 1e-5 is a precision which we set to guarantee float's calculation is correct - pe - (wm - w0) + wm * (1.0 - np.minimum(a + pe, wmm) / wmm) ** (1.0 + b), - pe - (wm - w0), - ), - np.full(pe.shape, 0.0), - ) - r = np.maximum(r_cal, 0.0) - # separate impervious part with the other - r_im_cal = pe * im - r_im = np.maximum(r_im_cal, 0.0) - return r, r_im - - -def calculate_w_storage( - um, lm, dm, wu0, wl0, wd0, eu, el, ed, pe, r -) -> tuple[np.array, np.array, np.array]: - """ - Update the soil moisture values of the three layers. - - According to the equation 2.60 in the book《水文预报》 - - Parameters - ---------- - um - average soil moisture storage capacity of the upper layer - lm - average soil moisture storage capacity of the lower layer - dm - average soil moisture storage capacity of the deep layer - wu0 - initial values of soil moisture in upper layer - wl0 - initial values of soil moisture in lower layer - wd0 - initial values of soil moisture in deep layer - eu - evaporation of the upper layer; it isn't used in this function - el - evaporation of the lower layer - ed - evaporation of the deep layer - pe - net precipitation; it is able to be negative value in this function - r - runoff - - Returns - ------- - tuple[np.array,np.array,np.array] - wu,wl,wd -- soil moisture in upper, lower and deep layer - """ - # pe>0: the upper soil moisture was added firstly, then lower layer, and the final is deep layer - # pe<=0: no additional water, just remove evapotranspiration, - # but note the case: e >= p > 0 - # (1) if wu0 + p > e, then e = eu (2) else, wu must be zero - wu = np.where( - pe > 0.0, - np.where(wu0 + pe - r < um, wu0 + pe - r, um), - np.where(wu0 + pe > 0.0, wu0 + pe, 0.0), - ) - # calculate wd before wl because it is easier to cal using where statement - wd = np.where( - pe > 0.0, - np.where(wu0 + wl0 + pe - r > um + lm, wu0 + wl0 + wd0 + pe - r - um - lm, wd0), - wd0 - ed, - ) - # water balance (equation 2.2 in Page 13, also shown in Page 23) - # if wu0 + p > e, then e = eu; else p must be used in upper layer, - # so no matter what the case is, el didn't include p, neither ed - wl = np.where(pe > 0.0, wu0 + wl0 + wd0 + pe - r - wu - wd, wl0 - el) - # the water storage should be in reasonable range - wu_ = np.clip(wu, a_min=0.0, a_max=um) - wl_ = np.clip(wl, a_min=0.0, a_max=lm) - wd_ = np.clip(wd, a_min=0.0, a_max=dm) - return wu_, wl_, wd_ - - -def generation(p_and_e, k, b, im, um, lm, dm, c, wu0=None, wl0=None, wd0=None) -> tuple: - """ - Single-step runoff generation in XAJ. - - Parameters - ---------- - p_and_e - precipitation and potential evapotranspiration - k - ratio of potential evapotranspiration to reference crop evaporation - b - exponent parameter - um - average soil moisture storage capacity of the upper layer - lm - average soil moisture storage capacity of the lower layer - dm - average soil moisture storage capacity of the deep layer - im - impermeability coefficient - c - coefficient of deep layer - wu0 - initial values of soil moisture in upper layer - wl0 - initial values of soil moisture in lower layer - wd0 - initial values of soil moisture in deep layer - - Returns - ------- - tuple[tuple, tuple] - (r, rim, e, pe), (wu, wl, wd); all variables are np.array - """ - # make sure physical variables' value ranges are correct - prcp = np.maximum(p_and_e[:, 0], 0.0) - # get potential evapotranspiration - pet = np.maximum(p_and_e[:, 1] * k, 0.0) - # wm - wm = um + lm + dm - if wu0 is None: - # just an initial value - wu0 = 0.6 * um - if wl0 is None: - wl0 = 0.6 * lm - if wd0 is None: - wd0 = 0.6 * dm - w0_ = wu0 + wl0 + wd0 - - # w0 need locate in correct range so that following calculation could be right - # To make sure float data's calculation is correct, we'd better minus a precision (1e-5) - w0 = np.minimum(w0_, wm - 1e-5) - - # Calculate the amount of evaporation from storage - eu, el, ed = calculate_evap(lm, c, wu0, wl0, prcp, pet) - e = eu + el + ed - - # Calculate the runoff generated by net precipitation - prcp_difference = prcp - e - pe = np.maximum(prcp_difference, 0.0) - r, rim = calculate_prcp_runoff(b, im, wm, w0, pe) - # Update wu, wl, wd - wu, wl, wd = calculate_w_storage( - um, lm, dm, wu0, wl0, wd0, eu, el, ed, prcp_difference, r - ) - - return (r, rim, e, pe), (wu, wl, wd) - - -def sources(pe, r, sm, ex, ki, kg, s0=None, fr0=None, book="HF") -> tuple: - """ - Divide the runoff to different sources - - We use the initial version from the paper of the inventor of the XAJ model -- Prof. Renjun Zhao: - "Analysis of parameters of the XinAnJiang model". Its Chinese name is <<新安江模型参数的分析>>, - which could be found by searching in "Baidu Xueshu". - The module's code can also be found in "Watershed Hydrologic Simulation" (WHS) Page 174. - It is nearly same with that in "Hydrologic Forecasting" (HF) Page 148-149 - We use the period average runoff as input and the unit period is day so we don't need to difference it as books show - - We also provide code for formula from《水文预报》"Hydrologic Forecasting" (HF) the fifth version. Page 40-41 and 150-151; - the procedures in 《工程水文学》"Engineering Hydrology" (EH) the third version are different we also provide. - they are in the "sources5mm" function. - - Parameters - ------------ - pe - net precipitation - r - runoff from xaj_generation - sm - areal mean free water capacity of the surface layer - ex - exponent of the free water capacity curve - ki - outflow coefficients of the free water storage to interflow relationships - kg - outflow coefficients of the free water storage to groundwater relationships - s0 - free water capacity of last period - fr0 - runoff area of last period - - Return - ------------ - tuple[tuple, tuple] - rs -- surface runoff; ri-- interflow runoff; rg -- groundwater runoff; - s1 -- final free water capacity; - all variables are numpy array - - """ - # maximum free water storage capacity in a basin - ms = sm * (1.0 + ex) - if fr0 is None: - fr0 = 0.1 - if s0 is None: - s0 = 0.5 * sm - # For free water storage, because s is related to fr and s0 and fr0 are both values of last period, - # we have to trans the initial value of s from last period to this one. - # both WHS（流域水文模拟）'s sample code and HF（水文预报） use s = fr0 * s0 / fr. - # I think they both think free water reservoir as a cubic tank. Its height is s and area of bottom rectangle is fr - # we will have a cubic tank with varying bottom and height, - # and fixed boundary (in HF sm is fixed) or none-fixed boundary (in EH smmf is not fixed) - # but notice r's list like" [1,0] which 1 is the 1st period's runoff and 0 is the 2nd period's runoff - # after 1st period, the s1 could not be zero, but in the 2nd period, fr=0, then we cannot set s=0, because some water still in the tank - # fr's formula could be found in Eq. 9 in "Analysis of parameters of the XinAnJiang model", - # Here our r doesn't include rim, so there is no need to remove rim from r; this is also the method in 《水文预报》（HF） - - # NOTE: when r is 0, fr should be 0, however, s1 may not be zero and it still hold some water, - # then fr can not be 0, otherwise when fr is used as denominator it lead to error, - # so we have to deal with this case later, for example, when r=0, we cannot use pe * fr to replace r - # because fr get the value of last period, and it is not 0 - fr = np.copy(fr0) - if any(fr == 0.0): - raise ArithmeticError( - "Please check fr's value, fr==0.0 will cause error in the next step!" - ) - # r=0, then r/pe must be 0 - fr_mask = r > 0.0 - fr[fr_mask] = r[fr_mask] / pe[fr_mask] - if np.isnan(fr).any(): - raise ArithmeticError("Please check pe's data! there may be 0.0") - - # if fr=0, then we cannot get ss, but ss should not be 0, because s1 of last period may not be 0 and it still hold some water - ss = np.copy(s0) - # same initialization for s - s = np.copy(s0) - - ss[fr_mask] = fr0[fr_mask] * s0[fr_mask] / fr[fr_mask] - - if book == "HF": - ss = np.minimum(ss, sm) - au = ms * (1.0 - (1.0 - ss / sm) ** (1.0 / (1.0 + ex))) - if np.isnan(au).any(): - raise ValueError( - "Error: NaN values detected. Try set clip function or check your data!!!" - ) - # the first condition should be r > 0.0, when r=0, rs must be 0, fig 6-12 in EH or fig 5-4 in HF - # so we have to use "pe" carefully!!! when r>0.0, we use pe, otherwise we don't use it!!! - rs = np.full(r.shape, 0.0) - rs[fr_mask] = np.where( - pe[fr_mask] + au[fr_mask] < ms[fr_mask], - # equation 2-85 in HF - fr[fr_mask] - * ( - pe[fr_mask] - - sm[fr_mask] - + ss[fr_mask] - + sm[fr_mask] - * ( - ( - 1 - - np.minimum(pe[fr_mask] + au[fr_mask], ms[fr_mask]) - / ms[fr_mask] - ) - ** (1 + ex[fr_mask]) - ) - ), - # equation 2-86 in HF - fr[fr_mask] * (pe[fr_mask] + ss[fr_mask] - sm[fr_mask]), - ) - rs = np.minimum(rs, r) - - # ri's mask is not same as rs's, because last period's s may not be 0 - # and in this time, ri and rg could be larger than 0 - # we need firstly calculate the updated s, s's mask is same as fr_mask, - # when r==0, then s will be equal to last period's - # equation 2-87 in HF, some free water leave or save, so we update free water storage - s[fr_mask] = ss[fr_mask] + (r[fr_mask] - rs[fr_mask]) / fr[fr_mask] - s = np.minimum(s, sm) - if np.isnan(s).any(): - raise ArithmeticError("Please check fr's data! there may be 0.0") - - elif book == "EH": - # smmf should be with correpond with s - smmf = ms * (1 - (1 - fr) ** (1 / ex)) - smf = smmf / (1 + ex) - ss = np.minimum(ss, smf) - au = smmf * (1 - (1 - ss / smf) ** (1 / (1 + ex))) - if np.isnan(au).any(): - raise ValueError( - "Error: NaN values detected. Try set clip function or check your data!!!" - ) - - # rs's mask is keep with fr_mask - rs = np.full(r.shape, 0.0) - rs[fr_mask] = np.where( - pe[fr_mask] + au[fr_mask] < smmf[fr_mask], - ( - pe[fr_mask] - - smf[fr_mask] - + ss[fr_mask] - + smf[fr_mask] - * ( - 1 - - np.minimum(pe[fr_mask] + au[fr_mask], smmf[fr_mask]) - / smmf[fr_mask] - ) - ** (ex[fr_mask] + 1) - ) - * fr[fr_mask], - (pe[fr_mask] + ss[fr_mask] - smf[fr_mask]) * fr[fr_mask], - ) - rs = np.minimum(rs, r) - s[fr_mask] = ss[fr_mask] + (r[fr_mask] - rs[fr_mask]) / fr[fr_mask] - s = np.minimum(s, smf) - else: - raise ValueError("Please set book as 'HF' or 'EH'!") - # the following part is same for both HF and EH. Even the formula is different, but their meaning is same - # equation 2-88 in HF, next interflow and ground water will be released from the updated free water storage - # We use the period average runoff as input and the general unit period is day. - # Hence, we directly use ki and kg rather than ki_{Δt} in books. - ri = ki * s * fr - rg = kg * s * fr - # ri = np.where(s < PRECISION, np.full(r.shape, 0.0), ki * s * fr) - # rg = np.where(s < PRECISION, np.full(r.shape, 0.0), kg * s * fr) - # equation 2-89 in HF; although it looks different with that in WHS, they are actually same - # Finally, calculate the final free water storage - s1 = s * (1 - ki - kg) - # s1 = np.where(s1 < PRECISION, np.full(s1.shape, 0.0), s1) - return (rs, ri, rg), (s1, fr) - - -def sources5mm( - pe, - runoff, - sm, - ex, - ki, - kg, - s0=None, - fr0=None, - time_interval_hours=24, - book="HF", -): - """ - Divide the runoff to different sources according to books -- 《水文预报》HF 5th edition and 《工程水文学》EH 3rd edition - - Parameters - ---------- - pe - net precipitation - runoff - runoff from xaj_generation - sm - areal mean free water capacity of the surface layer - ex - exponent of the free water capacity curve - ki - outflow coefficients of the free water storage to interflow relationships - kg - outflow coefficients of the free water storage to groundwater relationships - s0 - initial free water capacity - fr0 - initial area of generation - time_interval_hours - 由于Ki、Kg、Ci、Cg都是以24小时为时段长定义的,需根据时段长转换 - book - the methods in 《水文预报》HF 5th edition and 《工程水文学》EH 3rd edition are different, - hence, both are provided, and the default is the former -- "ShuiWenYuBao"; - the other one is "GongChengShuiWenXue" - - Returns - ------- - tuple[tuple, tuple] - rs_s -- surface runoff; rss_s-- interflow runoff; rg_s -- groundwater runoff; - (fr_ds[-1], s_ds[-1]): state variables' final value; - all variables are numpy array - """ - # 由于Ki、Kg都是以24小时为时段长定义的，需根据时段长转换 - hours_per_day = 24 - # 非整除情况，时段+1 - residue_temp = hours_per_day % time_interval_hours - if residue_temp != 0: - residue_temp = 1 - period_num_1d = int(hours_per_day / time_interval_hours) + residue_temp - # 当kss+kg>1时，根式为偶数运算时，kss_period会成为复数，这里会报错；另外注意分母可能为0，kss不可取0 - # 对kss+kg的取值进行限制 - kss_period = (1 - (1 - (ki + kg)) ** (1 / period_num_1d)) / (1 + kg / ki) - kg_period = kss_period * kg / ki - - # 流域最大点自由水蓄水容量深 - smm = sm * (1 + ex) - if s0 is None: - s0 = 0.50 * sm - if fr0 is None: - fr0 = 0.1 - # don't use np.where here, because it will cause some warning - fr = np.copy(fr0) - fr_mask = runoff > 0.0 - fr[fr_mask] = runoff[fr_mask] / pe[fr_mask] - if np.all(runoff < 5): - n = 1 - else: - # when modeling multiple basins, the number of divides is not the same, so we use the maximum number - r_max = np.max(runoff) - residue_temp = r_max % 5 - if residue_temp != 0: - residue_temp = 1 - n = int(r_max / 5) + residue_temp - rn = runoff / n - pen = pe / n - kss_d = (1 - (1 - (kss_period + kg_period)) ** (1 / n)) / ( - 1 + kg_period / kss_period - ) - kg_d = kss_d * kg_period / kss_period - # kss_d = kss_period - # kg_d = kg_period - rs = np.full(runoff.shape, 0.0) - rss = np.full(runoff.shape, 0.0) - rg = np.full(runoff.shape, 0.0) - - s_ds = [] - fr_ds = [] - s_ds.append(s0) - fr_ds.append(fr0) - - for j in range(n): - fr0_d = fr_ds[j] - s0_d = s_ds[j] - # equation 5-32 in HF, but strange, cause each period, rn/pen is same, fr_d should be same - # fr_d = 1 - (1 - fr) ** (1 / n) - fr_d = fr - - ss_d = np.copy(s0_d) - s_d = np.copy(s0_d) - s1_d = np.copy(s0_d) - - ss_d[fr_mask] = fr0_d[fr_mask] * s0_d[fr_mask] / fr_d[fr_mask] - - if book == "HF": - ss_d = np.minimum(ss_d, sm) - # ms = smm - au = smm * (1.0 - (1.0 - ss_d / sm) ** (1.0 / (1.0 + ex))) - if np.isnan(au).any(): - raise ValueError( - "Error: NaN values detected. Try set clip function or check your data!!!" - ) - rs_j = np.full(rn.shape, 0.0) - rs_j[fr_mask] = np.where( - pen[fr_mask] + au[fr_mask] < smm[fr_mask], - # equation 5-26 in HF - fr_d[fr_mask] - * ( - pen[fr_mask] - - sm[fr_mask] - + ss_d[fr_mask] - + sm[fr_mask] - * ( - ( - 1 - - np.minimum(pen[fr_mask] + au[fr_mask], smm[fr_mask]) - / smm[fr_mask] - ) - ** (1 + ex[fr_mask]) - ) - ), - # equation 5-27 in HF - fr_d[fr_mask] * (pen[fr_mask] + ss_d[fr_mask] - sm[fr_mask]), - ) - rs_j = np.minimum(rs_j, rn) - s_d[fr_mask] = ss_d[fr_mask] + (rn[fr_mask] - rs_j[fr_mask]) / fr_d[fr_mask] - s_d = np.minimum(s_d, sm) - - elif book == "EH": - smmf = smm * (1 - (1 - fr_d) ** (1 / ex)) - smf = smmf / (1 + ex) - ss_d = np.minimum(ss_d, smf) - au = smmf * (1 - (1 - ss_d / smf) ** (1 / (1 + ex))) - if np.isnan(au).any(): - raise ValueError( - "Error: NaN values detected. Try set clip function or check your data!!!" - ) - rs_j = np.full(rn.shape, 0.0) - rs_j[fr_mask] = np.where( - pen[fr_mask] + au[fr_mask] < smmf[fr_mask], - ( - pen[fr_mask] - - smf[fr_mask] - + ss_d[fr_mask] - + smf[fr_mask] - * ( - 1 - - np.minimum(pen[fr_mask] + au[fr_mask], smmf[fr_mask]) - / smmf[fr_mask] - ) - ** (ex[fr_mask] + 1) - ) - * fr_d[fr_mask], - (pen[fr_mask] + ss_d[fr_mask] - smf[fr_mask]) * fr_d[fr_mask], - ) - rs_j = np.minimum(rs_j, rn) - s_d[fr_mask] = ss_d[fr_mask] + (rn[fr_mask] - rs_j[fr_mask]) / fr_d[fr_mask] - s_d = np.minimum(s_d, smf) - - else: - raise NotImplementedError( - "We don't have this implementation! Please chose 'HF' or 'EH'!!" - ) - rss_j = s_d * kss_d * fr_d - rg_j = s_d * kg_d * fr_d - s1_d = s_d * (1 - kss_d - kg_d) - - rs = rs + rs_j - rss = rss + rss_j - rg = rg + rg_j - # 赋值s_d和fr_d到数组中，以给下一段做初值 - s_ds.append(s1_d) - fr_ds.append(fr_d) - - return (rs, rss, rg), (s_ds[-1], fr_ds[-1]) - - -@jit -def linear_reservoir(x, weight, last_y=None) -> np.array: - """ - Linear reservoir's release function - - Parameters - ---------- - x - the input to the linear reservoir - weight - the coefficient of linear reservoir - last_y - the output of last period - - Returns - ------- - np.array - one-step forward result - """ - weight1 = 1 - weight - if last_y is None: - last_y = np.full(weight.shape, 0.001) - y = weight * last_y + weight1 * x - return y - - -def uh_conv(x, uh_from_gamma): - """ - Function for 1d-convolution calculation - - Parameters - ---------- - x - x is a sequence-first variable; the dim of x is [seq, batch, feature=1]; - feature must be 1 - uh_from_gamma - unit hydrograph from uh_gamma; the dim: [len_uh, batch, feature=1]; - feature must be 1 - - Returns - ------- - np.array - convolution - """ - outputs = np.full(x.shape, 0.0) - time_length, batch_size, feature_size = x.shape - if feature_size > 1: - logging.error("We only support one-dim convolution now!!!") - for i in range(batch_size): - uh = uh_from_gamma[:, i, 0] - inputs = x[:, i, 0] - outputs[:, i, 0] = np.convolve(inputs, uh)[:time_length] - return outputs - - -def uh_gamma(a, theta, len_uh=15): - """ - A simple two-parameter Gamma distribution as a unit-hydrograph to route instantaneous runoff from a hydrologic model - The method comes from mizuRoute -- http://www.geosci-model-dev.net/9/2223/2016/ - - Parameters - ---------- - a - shape parameter - theta - timescale parameter - len_uh - the time length of the unit hydrograph - Returns - ------- - torch.Tensor - the unit hydrograph, dim: [seq, batch, feature] - """ - # dims of a: time_seq (same all time steps), batch, feature=1 - m = a.shape - if len_uh > m[0]: - raise RuntimeError( - "length of unit hydrograph should be smaller than the whole length of input" - ) - # aa > 0, here we set minimum 0.1 (min of a is 0, set when calling this func); First dimension of a is repeat - aa = np.maximum(0.0, a[0:len_uh, :, :]) + 0.1 - # theta > 0, here set minimum 0.5 - theta = np.maximum(0.0, theta[0:len_uh, :, :]) + 0.5 - # len_f, batch, feature - t = np.expand_dims( - np.swapaxes(np.tile(np.arange(0.5, len_uh * 1.0), (m[1], 1)), 0, 1), axis=-1 - ) - denominator = gamma(aa) * (theta**aa) - # [len_f, m[1], m[2]] - w = 1 / denominator * (t ** (aa - 1)) * (np.exp(-t / theta)) - w = w / w.sum(0) # scale to 1 for each UH - return w - - -def xaj_runoff( - p_and_e, - params_runoff: Union[np.array, list], - *w0, s0, fr0, - **kwargs, -) -> Union[tuple, np.array]: - model_name = kwargs["name"] if "name" in kwargs else "xaj" - source_type = kwargs["source_type"] if "source_type" in kwargs else "sources" - source_book = kwargs["source_book"] if "source_book" in kwargs else "HF" - # params - param_ranges = MODEL_PARAM_DICT[model_name]["param_range"] - if model_name == "xaj": - route_method = "CSL" - elif model_name == "xaj_mz": - route_method = "MZ" - else: - raise NotImplementedError( - "We don't provide this route method now! Please use 'CS' or 'MZ'!" - ) - xaj_params = [ - (value[1] - value[0]) * params_runoff[:, i] + value[0] - for i, (key, value) in enumerate(param_ranges.items()) - ] - k = xaj_params[0] - b = xaj_params[1] - im = xaj_params[2] - um = xaj_params[3] - lm = xaj_params[4] - dm = xaj_params[5] - c = xaj_params[6] - sm = xaj_params[7] - ex = xaj_params[8] - ki_ = xaj_params[9] - kg_ = xaj_params[10] - # ki+kg should be smaller than 1; if not, we scale them - ki = np.where(ki_ + kg_ < 1.0, ki_, (1.0 - PRECISION) / (ki_ + kg_) * ki_) - kg = np.where(ki_ + kg_ < 1.0, kg_, (1.0 - PRECISION) / (ki_ + kg_) * kg_) - - - # w0 = (0.5 * um, 0.5 * lm, 0.5 * dm) - inputs = p_and_e[:, :, :] - runoff_ims_ = np.full(inputs.shape[:2], 0.0) - rss_ = np.full(inputs.shape[:2], 0.0) - ris_ = np.full(inputs.shape[:2], 0.0) - rgs_ = np.full(inputs.shape[:2], 0.0) - es_ = np.full(inputs.shape[:2], 0.0) - for i in range(inputs.shape[0]): - if i == 0: - (r, rim, e, pe), w = generation( - inputs[i, :, :], k, b, im, um, lm, dm, c, *w0 - ) - if source_type == "sources": - (rs, ri, rg), (s, fr) = sources( - pe, r, sm, ex, ki, kg, s0, fr0, book=source_book - ) - elif source_type == "sources5mm": - (rs, ri, rg), (s, fr) = sources5mm( - pe, r, sm, ex, ki, kg, s0, fr0, book=source_book - ) - else: - raise NotImplementedError("No such divide-sources method") - else: - (r, rim, e, pe), w = generation( - inputs[i, :, :], k, b, im, um, lm, dm, c, *w - ) - if source_type == "sources": - (rs, ri, rg), (s, fr) = sources( - pe, r, sm, ex, ki, kg, s, fr, book=source_book - ) - elif source_type == "sources5mm": - (rs, ri, rg), (s, fr) = sources5mm( - pe, r, sm, ex, ki, kg, s, fr, book=source_book - ) - else: - raise NotImplementedError("No such divide-sources method") - # impevious part is pe * im - runoff_ims_[i, :] = rim - # so for non-imprvious part, the result should be corrected - rss_[i, :] = rs * (1 - im) - ris_[i, :] = ri * (1 - im) - rgs_[i, :] = rg * (1 - im) - es_[i, :] = e - # seq, batch, feature - runoff_im = np.expand_dims(runoff_ims_, axis=2) - rss = np.expand_dims(rss_, axis=2) - es = np.expand_dims(es_, axis=2) - return runoff_im, rss_,ris_, rgs_, es,rss - -def xaj_route( - p_and_e, - params_route: Union[np.array, list], - ris_, - rgs_, - rss_, - rss, - runoff_im, - qi0, - qg0, - es, - **kwargs, -)-> Union[tuple, np.array]: - - model_name = kwargs["name"] if "name" in kwargs else "xaj" - source_type = kwargs["source_type"] if "source_type" in kwargs else "sources" - source_book = kwargs["source_book"] if "source_book" in kwargs else "HF" - # params - param_ranges = MODEL_PARAM_DICT[model_name]["param_range"] - if model_name == "xaj": - route_method = "CSL" - elif model_name == "xaj_mz": - route_method = "MZ" - else: - raise NotImplementedError( - "We don't provide this route method now! Please use 'CS' or 'MZ'!" - ) - xaj_params = [ - (value[1] - value[0]) * params_route[:, i] + value[0] - for i, (key, value) in enumerate(param_ranges.items()) - ] - k = xaj_params[0] - b = xaj_params[1] - im = xaj_params[2] - um = xaj_params[3] - lm = xaj_params[4] - dm = xaj_params[5] - c = xaj_params[6] - sm = xaj_params[7] - ex = xaj_params[8] - ki_ = xaj_params[9] - kg_ = xaj_params[10] - # ki+kg should be smaller than 1; if not, we scale them - ki = np.where(ki_ + kg_ < 1.0, ki_, (1.0 - PRECISION) / (ki_ + kg_) * ki_) - kg = np.where(ki_ + kg_ < 1.0, kg_, (1.0 - PRECISION) / (ki_ + kg_) * kg_) - if route_method == "CSL": - cs = xaj_params[11] - l = xaj_params[12] - - elif route_method == "MZ": - # we will use routing method from mizuRoute -- http://www.geosci-model-dev.net/9/2223/2016/ - a = xaj_params[11] - theta = xaj_params[12] - # make it as a parameter - kernel_size = int(xaj_params[15]) - else: - raise NotImplementedError( - "We don't provide this route method now! Please use 'CS' or 'MZ'!" - ) - ci = xaj_params[13] - cg = xaj_params[14] - - inputs = p_and_e[:, :, :] - qs = np.full(inputs.shape[:2], 0.0) - if route_method == "CSL": - qt = np.full(inputs.shape[:2], 0.0) - for i in range(inputs.shape[0]): - if i == 0: - qi = linear_reservoir(ris_[i], ci, qi0) - qg = linear_reservoir(rgs_[i], cg, qg0) - else: - qi = linear_reservoir(ris_[i], ci, qi) - qg = linear_reservoir(rgs_[i], cg, qg) - qs_ = rss_[i] - qt[i, :] = qs_ + qi + qg - - for j in range(len(l)): - lag = int(l[j]) - for i in range(0,lag): - qs[i, j] = qt[i, j] - if i == lag: - break - for i in range(lag, inputs.shape[0]): - qs[i, j] = cs[j] * qs[i - 1, j] + (1 - cs[j]) * qt[i - lag, j] - - elif route_method == "MZ": - rout_a = a.repeat(rss.shape[0]).reshape(rss.shape) - rout_b = theta.repeat(rss.shape[0]).reshape(rss.shape) - conv_uh = uh_gamma(rout_a, rout_b, kernel_size) - qs_ = uh_conv(runoff_im + rss, conv_uh) - for i in range(inputs.shape[0]): - if i == 0: - qi = linear_reservoir(ris_[i], ci, qi0) - qg = linear_reservoir(rgs_[i], cg, qg0) - else: - qi = linear_reservoir(ris_[i], ci, qi) - qg = linear_reservoir(rgs_[i], cg, qg) - qs[i, :] = qs_[i, :, 0] + qi + qg - else: - raise NotImplementedError( - "We don't provide this route method now! Please use 'CSL' or 'MZ'!" - ) - - # seq, batch, feature - q_sim = np.expand_dims(qs, axis=2) - return q_sim, es - -def xaj_state( - p_and_e, - params: Union[np.array, list], - return_state=True, - warmup_length=365, - **kwargs, -) -> Union[tuple, np.array]: - # default values for some function parameters - model_name = kwargs["name"] if "name" in kwargs else "xaj" - source_type = kwargs["source_type"] if "source_type" in kwargs else "sources" - source_book = kwargs["source_book"] if "source_book" in kwargs else "HF" - # params - param_ranges = MODEL_PARAM_DICT[model_name]["param_range"] - if model_name == "xaj": - route_method = "CSL" - elif model_name == "xaj_mz": - route_method = "MZ" - else: - raise NotImplementedError( - "We don't provide this route method now! Please use 'CS' or 'MZ'!" - ) - xaj_params = [ - (value[1] - value[0]) * params[:, i] + value[0] - for i, (key, value) in enumerate(param_ranges.items()) - ] - k = xaj_params[0] - b = xaj_params[1] - im = xaj_params[2] - um = xaj_params[3] - lm = xaj_params[4] - dm = xaj_params[5] - c = xaj_params[6] - sm = xaj_params[7] - ex = xaj_params[8] - ki_ = xaj_params[9] - kg_ = xaj_params[10] - # ki+kg should be smaller than 1; if not, we scale them - ki = np.where(ki_ + kg_ < 1.0, ki_, (1.0 - PRECISION) / (ki_ + kg_) * ki_) - kg = np.where(ki_ + kg_ < 1.0, kg_, (1.0 - PRECISION) / (ki_ + kg_) * kg_) - if route_method == "CSL": - cs = xaj_params[11] - l = xaj_params[12] - - elif route_method == "MZ": - # we will use routing method from mizuRoute -- http://www.geosci-model-dev.net/9/2223/2016/ - a = xaj_params[11] - theta = xaj_params[12] - # make it as a parameter - kernel_size = int(xaj_params[15]) - else: - raise NotImplementedError( - "We don't provide this route method now! Please use 'CS' or 'MZ'!" - ) - ci = xaj_params[13] - cg = xaj_params[14] - - - # initialize state values - # if warmup_length > 0: - # p_and_e_warmup = p_and_e[0:warmup_length, :, :] - # _q, _e, *w0, s0, fr0, qi0, qg0 = xaj_state( - # p_and_e_warmup, - # params, - # return_state=True, - # warmup_length=0, - # **kwargs, - # ) - # else: - w0 = (0.5 * um, 0.5 * lm, 0.5 * dm) - s0 = 0.5 * sm - fr0 = np.full(ex.shape, 0.1) - qi0 = np.full(ci.shape, 0.1) - qg0 = np.full(cg.shape, 0.1) - - #state_variables - # inputs = p_and_e[warmup_length:, :, :] - inputs = p_and_e[:, :, :] - runoff_ims_ = np.full(inputs.shape[:2], 0.0) - rss_ = np.full(inputs.shape[:2], 0.0) - ris_ = np.full(inputs.shape[:2], 0.0) - rgs_ = np.full(inputs.shape[:2], 0.0) - es_ = np.full(inputs.shape[:2], 0.0) - for i in range(inputs.shape[0]): - if i == 0: - (r, rim, e, pe), w = generation( - inputs[i, :, :], k, b, im, um, lm, dm, c, *w0 - ) - if source_type == "sources": - (rs, ri, rg), (s, fr) = sources( - pe, r, sm, ex, ki, kg, s0, fr0, book=source_book - ) - elif source_type == "sources5mm": - (rs, ri, rg), (s, fr) = sources5mm( - pe, r, sm, ex, ki, kg, s0, fr0, book=source_book - ) - else: - raise NotImplementedError("No such divide-sources method") - else: - (r, rim, e, pe), w = generation( - inputs[i, :, :], k, b, im, um, lm, dm, c, *w - ) - if source_type == "sources": - (rs, ri, rg), (s, fr) = sources( - pe, r, sm, ex, ki, kg, s, fr, book=source_book - ) - elif source_type == "sources5mm": - (rs, ri, rg), (s, fr) = sources5mm( - pe, r, sm, ex, ki, kg, s, fr, book=source_book - ) - else: - raise NotImplementedError("No such divide-sources method") - # impevious part is pe * im - runoff_ims_[i, :] = rim - # so for non-imprvious part, the result should be corrected - rss_[i, :] = rs * (1 - im) - ris_[i, :] = ri * (1 - im) - rgs_[i, :] = rg * (1 - im) - es_[i, :] = e - # seq, batch, feature - runoff_im = np.expand_dims(runoff_ims_, axis=2) - rss = np.expand_dims(rss_, axis=2) - es = np.expand_dims(es_, axis=2) - - qs = np.full(inputs.shape[:2], 0.0) - - if route_method == "CSL": - qt = np.full(inputs.shape[:2], 0.0) - for i in range(inputs.shape[0]): - if i == 0: - qi = linear_reservoir(ris_[i], ci, qi0) - qg = linear_reservoir(rgs_[i], cg, qg0) - else: - qi = linear_reservoir(ris_[i], ci, qi) - qg = linear_reservoir(rgs_[i], cg, qg) - qs_ = rss_[i] - qt[i, :] = qs_ + qi + qg - - for j in range(len(l)): - # print(range(len(l))) - lag = int(l[j]) - for i in range(0,lag): - qs[i, j] = qt[i, j] - if i == lag: - break - for i in range(lag, inputs.shape[0]): - # print(inputs.shape[0]) - qs[i, j] = cs[j] * qs[i - 1, j] + (1 - cs[j]) * qt[i - lag, j] - - elif route_method == "MZ": - rout_a = a.repeat(rss.shape[0]).reshape(rss.shape) - rout_b = theta.repeat(rss.shape[0]).reshape(rss.shape) - conv_uh = uh_gamma(rout_a, rout_b, kernel_size) - qs_ = uh_conv(runoff_im + rss, conv_uh) - for i in range(inputs.shape[0]): - if i == 0: - qi = linear_reservoir(ris_[i], ci, qi0) - qg = linear_reservoir(rgs_[i], cg, qg0) - else: - qi = linear_reservoir(ris_[i], ci, qi) - qg = linear_reservoir(rgs_[i], cg, qg) - qs[i, :] = qs_[i, :, 0] + qi + qg - else: - raise NotImplementedError( - "We don't provide this route method now! Please use 'CSL' or 'MZ'!" - ) - - # seq, batch, feature - q_sim = np.expand_dims(qs, axis=2) - if return_state: - return q_sim, es, *w, s, fr, qi, qg From c222649b076e92dcde2492bf9a0b15aaf1620f49 Mon Sep 17 00:00:00 2001 From: ouyangwenyu Date: Sat, 28 Oct 2023 16:21:53 +0800 Subject: [PATCH 15/19] refactor xajbim-related funcs, but not fully tested yet --- hydromodel/models/xaj.py | 213 ++++++++++++++++++++++++++++++++-- hydromodel/models/xaj_bmi.py | 3 +- test/test_rr_event_iden.py | 96 +++++++++++++++ test/test_session_division.py | 44 ------- test/test_xaj_bmi.py | 203 +++++++++++++++++--------------- 5 files changed, 413 insertions(+), 146 deletions(-) create mode 100644 test/test_rr_event_iden.py delete mode 100644 test/test_session_division.py diff --git a/hydromodel/models/xaj.py b/hydromodel/models/xaj.py index 8aaf9ba..b780d9b 100644 --- a/hydromodel/models/xaj.py +++ b/hydromodel/models/xaj.py @@ -8,7 +8,6 @@ from scipy.special import gamma from hydromodel.models.model_config import MODEL_PARAM_DICT -from xaj.params import MODEL_PARAM_DICT PRECISION = 1e-5 @@ -888,6 +887,203 @@ def xaj( return q_sim, es +def xaj_runoff( + p_and_e, + params_runoff: Union[np.array, list], + *w0, + s0, + fr0, + **kwargs, +) -> Union[tuple, np.array]: + model_name = kwargs.get("name", "xaj") + source_type = kwargs.get("source_type", "sources") + source_book = kwargs.get("source_book", "HF") + # params + param_ranges = MODEL_PARAM_DICT[model_name]["param_range"] + if model_name == "xaj": + route_method = "CSL" + elif model_name == "xaj_mz": + route_method = "MZ" + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CS' or 'MZ'!" + ) + xaj_params = [ + (value[1] - value[0]) * params_runoff[:, i] + value[0] + for i, (key, value) in enumerate(param_ranges.items()) + ] + k = xaj_params[0] + b = xaj_params[1] + im = xaj_params[2] + um = xaj_params[3] + lm = xaj_params[4] + dm = xaj_params[5] + c = xaj_params[6] + sm = xaj_params[7] + ex = xaj_params[8] + ki_ = xaj_params[9] + kg_ = xaj_params[10] + # ki+kg should be smaller than 1; if not, we scale them + ki = np.where(ki_ + kg_ < 1.0, ki_, (1.0 - PRECISION) / (ki_ + kg_) * ki_) + kg = np.where(ki_ + kg_ < 1.0, kg_, (1.0 - PRECISION) / (ki_ + kg_) * kg_) + + # w0 = (0.5 * um, 0.5 * lm, 0.5 * dm) + inputs = p_and_e[:, :, :] + runoff_ims_ = np.full(inputs.shape[:2], 0.0) + rss_ = np.full(inputs.shape[:2], 0.0) + ris_ = np.full(inputs.shape[:2], 0.0) + rgs_ = np.full(inputs.shape[:2], 0.0) + es_ = np.full(inputs.shape[:2], 0.0) + for i in range(inputs.shape[0]): + if i == 0: + (r, rim, e, pe), w = generation( + inputs[i, :, :], k, b, im, um, lm, dm, c, *w0 + ) + if source_type == "sources": + (rs, ri, rg), (s, fr) = sources( + pe, r, sm, ex, ki, kg, s0, fr0, book=source_book + ) + elif source_type == "sources5mm": + (rs, ri, rg), (s, fr) = sources5mm( + pe, r, sm, ex, ki, kg, s0, fr0, book=source_book + ) + else: + raise NotImplementedError("No such divide-sources method") + else: + (r, rim, e, pe), w = generation( + inputs[i, :, :], k, b, im, um, lm, dm, c, *w + ) + if source_type == "sources": + (rs, ri, rg), (s, fr) = sources( + pe, r, sm, ex, ki, kg, s, fr, book=source_book + ) + elif source_type == "sources5mm": + (rs, ri, rg), (s, fr) = sources5mm( + pe, r, sm, ex, ki, kg, s, fr, book=source_book + ) + else: + raise NotImplementedError("No such divide-sources method") + # impevious part is pe * im + runoff_ims_[i, :] = rim + # so for non-imprvious part, the result should be corrected + rss_[i, :] = rs * (1 - im) + ris_[i, :] = ri * (1 - im) + rgs_[i, :] = rg * (1 - im) + es_[i, :] = e + # seq, batch, feature + runoff_im = np.expand_dims(runoff_ims_, axis=2) + rss = np.expand_dims(rss_, axis=2) + es = np.expand_dims(es_, axis=2) + return runoff_im, rss_, ris_, rgs_, es, rss + + +def xaj_route( + p_and_e, + params_route: Union[np.array, list], + ris_, + rgs_, + rss_, + rss, + runoff_im, + qi0, + qg0, + es, + **kwargs, +) -> Union[tuple, np.array]: + model_name = kwargs.get("name", "xaj") + source_type = kwargs.get("source_type", "sources") + source_book = kwargs.get("source_book", "HF") + # params + param_ranges = MODEL_PARAM_DICT[model_name]["param_range"] + if model_name == "xaj": + route_method = "CSL" + elif model_name == "xaj_mz": + route_method = "MZ" + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CS' or 'MZ'!" + ) + xaj_params = [ + (value[1] - value[0]) * params_route[:, i] + value[0] + for i, (key, value) in enumerate(param_ranges.items()) + ] + k = xaj_params[0] + b = xaj_params[1] + im = xaj_params[2] + um = xaj_params[3] + lm = xaj_params[4] + dm = xaj_params[5] + c = xaj_params[6] + sm = xaj_params[7] + ex = xaj_params[8] + ki_ = xaj_params[9] + kg_ = xaj_params[10] + # ki+kg should be smaller than 1; if not, we scale them + ki = np.where(ki_ + kg_ < 1.0, ki_, (1.0 - PRECISION) / (ki_ + kg_) * ki_) + kg = np.where(ki_ + kg_ < 1.0, kg_, (1.0 - PRECISION) / (ki_ + kg_) * kg_) + if route_method == "CSL": + cs = xaj_params[11] + l = xaj_params[12] + + elif route_method == "MZ": + # we will use routing method from mizuRoute -- http://www.geosci-model-dev.net/9/2223/2016/ + a = xaj_params[11] + theta = xaj_params[12] + # make it as a parameter + kernel_size = int(xaj_params[15]) + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CS' or 'MZ'!" + ) + ci = xaj_params[13] + cg = xaj_params[14] + + inputs = p_and_e[:, :, :] + qs = np.full(inputs.shape[:2], 0.0) + if route_method == "CSL": + qt = np.full(inputs.shape[:2], 0.0) + for i in range(inputs.shape[0]): + if i == 0: + qi = linear_reservoir(ris_[i], ci, qi0) + qg = linear_reservoir(rgs_[i], cg, qg0) + else: + qi = linear_reservoir(ris_[i], ci, qi) + qg = linear_reservoir(rgs_[i], cg, qg) + qs_ = rss_[i] + qt[i, :] = qs_ + qi + qg + + for j in range(len(l)): + lag = int(l[j]) + for i in range(lag): + qs[i, j] = qt[i, j] + if i == lag: + break + for i in range(lag, inputs.shape[0]): + qs[i, j] = cs[j] * qs[i - 1, j] + (1 - cs[j]) * qt[i - lag, j] + + elif route_method == "MZ": + rout_a = a.repeat(rss.shape[0]).reshape(rss.shape) + rout_b = theta.repeat(rss.shape[0]).reshape(rss.shape) + conv_uh = uh_gamma(rout_a, rout_b, kernel_size) + qs_ = uh_conv(runoff_im + rss, conv_uh) + for i in range(inputs.shape[0]): + if i == 0: + qi = linear_reservoir(ris_[i], ci, qi0) + qg = linear_reservoir(rgs_[i], cg, qg0) + else: + qi = linear_reservoir(ris_[i], ci, qi) + qg = linear_reservoir(rgs_[i], cg, qg) + qs[i, :] = qs_[i, :, 0] + qi + qg + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CSL' or 'MZ'!" + ) + + # seq, batch, feature + q_sim = np.expand_dims(qs, axis=2) + return q_sim, es + + def xaj_state( p_and_e, params: Union[np.array, list], @@ -944,7 +1140,6 @@ def xaj_state( ci = xaj_params[13] cg = xaj_params[14] - # initialize state values # if warmup_length > 0: # p_and_e_warmup = p_and_e[0:warmup_length, :, :] @@ -962,8 +1157,8 @@ def xaj_state( qi0 = np.full(ci.shape, 0.1) qg0 = np.full(cg.shape, 0.1) - #state_variables - # inputs = p_and_e[warmup_length:, :, :] + # state_variables + # inputs = p_and_e[warmup_length:, :, :] inputs = p_and_e[:, :, :] runoff_ims_ = np.full(inputs.shape[:2], 0.0) rss_ = np.full(inputs.shape[:2], 0.0) @@ -1028,7 +1223,7 @@ def xaj_state( for j in range(len(l)): # print(range(len(l))) lag = int(l[j]) - for i in range(0,lag): + for i in range(lag): qs[i, j] = qt[i, j] if i == lag: break @@ -1049,10 +1244,10 @@ def xaj_state( qi = linear_reservoir(ris_[i], ci, qi) qg = linear_reservoir(rgs_[i], cg, qg) qs[i, :] = qs_[i, :, 0] + qi + qg - else: - raise NotImplementedError( - "We don't provide this route method now! Please use 'CSL' or 'MZ'!" - ) + else: + raise NotImplementedError( + "We don't provide this route method now! Please use 'CSL' or 'MZ'!" + ) # seq, batch, feature q_sim = np.expand_dims(qs, axis=2) diff --git a/hydromodel/models/xaj_bmi.py b/hydromodel/models/xaj_bmi.py index 94f1d02..435ab67 100644 --- a/hydromodel/models/xaj_bmi.py +++ b/hydromodel/models/xaj_bmi.py @@ -3,10 +3,11 @@ import numpy as np from hydromodel.models import configuration -from hydromodel.models.xajmodel import xaj_route, xaj_runoff import datetime import logging +from hydromodel.models.xaj import xaj_route, xaj_runoff + logger = logging.getLogger(__name__) PRECISION = 1e-5 diff --git a/test/test_rr_event_iden.py b/test/test_rr_event_iden.py new file mode 100644 index 0000000..0034e38 --- /dev/null +++ b/test/test_rr_event_iden.py @@ -0,0 +1,96 @@ +""" +Author: Wenyu Ouyang +Date: 2023-10-28 09:23:22 +LastEditTime: 2023-10-28 16:19:22 +LastEditors: Wenyu Ouyang +Description: Test for rainfall-runoff event identification +FilePath: \hydro-model-xaj\test\test_rr_event_iden.py +Copyright (c) 2023-2024 Wenyu Ouyang. All rights reserved. +""" +import os +import pandas as pd +import definitions +from hydromodel.utils.dmca_esr import ( + step1_step2_tr_and_fluctuations_timeseries, + step3_core_identification, + step4_end_rain_events, + step5_beginning_rain_events, + step6_checks_on_rain_events, + step7_end_flow_events, + step8_beginning_flow_events, + step9_checks_on_flow_events, + step10_checks_on_overlapping_events, +) + + +def test_rainfall_runoff_event_identify(): + rain = pd.read_csv( + os.path.join(definitions.ROOT_DIR, "hydromodel/example/division_rain.csv") + )["rain"] + flow = pd.read_csv( + os.path.join(definitions.ROOT_DIR, "hydromodel/example/division_flow.csv") + )["flow"] + time = rain.index.to_numpy() + rain = rain.to_numpy() / 24 + flow = flow.to_numpy() / 24 + rain_min = 0.02 + max_window = 100 + ( + Tr, + fluct_rain_Tr, + fluct_flow_Tr, + fluct_bivariate_Tr, + ) = step1_step2_tr_and_fluctuations_timeseries(rain, flow, rain_min, max_window) + beginning_core, end_core = step3_core_identification(fluct_bivariate_Tr) + end_rain = step4_end_rain_events( + beginning_core, end_core, rain, fluct_rain_Tr, rain_min + ) + beginning_rain = step5_beginning_rain_events( + beginning_core, end_rain, rain, fluct_rain_Tr, rain_min + ) + ( + beginning_rain_checked, + end_rain_checked, + beginning_core, + end_core, + ) = step6_checks_on_rain_events( + beginning_rain, end_rain, rain, rain_min, beginning_core, end_core + ) + end_flow = step7_end_flow_events( + end_rain_checked, + beginning_core, + end_core, + rain, + fluct_rain_Tr, + fluct_flow_Tr, + Tr, + ) + beginning_flow = step8_beginning_flow_events( + beginning_rain_checked, + end_rain_checked, + rain, + beginning_core, + fluct_rain_Tr, + fluct_flow_Tr, + ) + beginning_flow_checked, end_flow_checked = step9_checks_on_flow_events( + beginning_rain_checked, + end_rain_checked, + beginning_flow, + end_flow, + fluct_flow_Tr, + ) + ( + BEGINNING_RAIN, + END_RAIN, + BEGINNING_FLOW, + END_FLOW, + ) = step10_checks_on_overlapping_events( + beginning_rain_checked, + end_rain_checked, + beginning_flow_checked, + end_flow_checked, + time, + ) + print(BEGINNING_RAIN, END_RAIN, BEGINNING_FLOW, END_FLOW) + print(len(BEGINNING_RAIN), len(END_RAIN), len(BEGINNING_FLOW), len(END_FLOW)) diff --git a/test/test_session_division.py b/test/test_session_division.py deleted file mode 100644 index cc00831..0000000 --- a/test/test_session_division.py +++ /dev/null @@ -1,44 +0,0 @@ -import os - -import pandas as pd - -import definitions -from hydromodel.utils.dmca_esr import step1_step2_tr_and_fluctuations_timeseries, step3_core_identification, \ - step4_end_rain_events, \ - step5_beginning_rain_events, step6_checks_on_rain_events, step7_end_flow_events, step8_beginning_flow_events, \ - step9_checks_on_flow_events, step10_checks_on_overlapping_events - - -def test_session_division_new(): - rain = pd.read_csv(os.path.join(definitions.ROOT_DIR, 'hydromodel/example/division_rain.csv'))['rain'] - flow = pd.read_csv(os.path.join(definitions.ROOT_DIR, 'hydromodel/example/division_flow.csv'))['flow'] - time = rain.index.to_numpy() - rain = rain.to_numpy() / 24 - flow = flow.to_numpy() / 24 - rain_min = 0.02 - max_window = 100 - Tr, fluct_rain_Tr, fluct_flow_Tr, fluct_bivariate_Tr = step1_step2_tr_and_fluctuations_timeseries(rain, flow, - rain_min, - max_window) - beginning_core, end_core = step3_core_identification(fluct_bivariate_Tr) - end_rain = step4_end_rain_events(beginning_core, end_core, rain, fluct_rain_Tr, rain_min) - beginning_rain = step5_beginning_rain_events(beginning_core, end_rain, rain, fluct_rain_Tr, rain_min) - beginning_rain_checked, end_rain_checked, beginning_core, end_core = step6_checks_on_rain_events(beginning_rain, - end_rain, rain, - rain_min, - beginning_core, - end_core) - end_flow = step7_end_flow_events(end_rain_checked, beginning_core, end_core, rain, fluct_rain_Tr, fluct_flow_Tr, Tr) - beginning_flow = step8_beginning_flow_events(beginning_rain_checked, end_rain_checked, rain, beginning_core, - fluct_rain_Tr, - fluct_flow_Tr) - beginning_flow_checked, end_flow_checked = step9_checks_on_flow_events(beginning_rain_checked, end_rain_checked, - beginning_flow, - end_flow, fluct_flow_Tr) - BEGINNING_RAIN, END_RAIN, BEGINNING_FLOW, END_FLOW = step10_checks_on_overlapping_events(beginning_rain_checked, - end_rain_checked, - beginning_flow_checked, - end_flow_checked, - time) - print(BEGINNING_RAIN, END_RAIN, BEGINNING_FLOW, END_FLOW) - print(len(BEGINNING_RAIN), len(END_RAIN), len(BEGINNING_FLOW), len(END_FLOW)) diff --git a/test/test_xaj_bmi.py b/test/test_xaj_bmi.py index c61808c..0b65331 100644 --- a/test/test_xaj_bmi.py +++ b/test/test_xaj_bmi.py @@ -3,9 +3,6 @@ import definitions from hydromodel.models.configuration import read_config from hydromodel.models.xaj_bmi import xajBmi - -logging.basicConfig(level=logging.INFO) - import pandas as pd import os from pathlib import Path @@ -19,8 +16,8 @@ cross_valid_data, split_train_test, ) -from xaj.calibrate_sceua_xaj_bmi import calibrate_by_sceua -from xaj.calibrate_ga_xaj_bmi import ( +from hydromodel.calibrate.calibrate_sceua_xaj_bmi import calibrate_by_sceua +from hydromodel.calibrate.calibrate_ga_xaj_bmi import ( calibrate_by_ga, show_ga_result, ) @@ -34,9 +31,11 @@ ) from hydromodel.utils import hydro_constant +logging.basicConfig(level=logging.INFO) + def test_bmi(): - ''' + """ model = xajBmi() print(model.get_component_name()) model.initialize("runxaj.yaml") @@ -64,23 +63,23 @@ def test_bmi(): }) results.to_csv('/home/wangjingyi/code/hydro-model-xaj/scripts/xaj.csv') model.finalize() - ''' + """ # 模型率定 config = read_config(os.path.relpath("runxaj.yaml")) - forcing_data = Path(str(definitions.ROOT_DIR) + str(config['forcing_data'])) - train_period = config['train_period'] - test_period = config['test_period'] + forcing_data = Path(str(definitions.ROOT_DIR) + str(config["forcing_data"])) + train_period = config["train_period"] + test_period = config["test_period"] # period = config['period'] - json_file = Path(str(definitions.ROOT_DIR) + str(config['json_file'])) - npy_file = Path(str(definitions.ROOT_DIR) + str(config['npy_file'])) - cv_fold = config['cv_fold'] - warmup_length = config['warmup_length'] + json_file = Path(str(definitions.ROOT_DIR) + str(config["json_file"])) + npy_file = Path(str(definitions.ROOT_DIR) + str(config["npy_file"])) + cv_fold = config["cv_fold"] + warmup_length = config["warmup_length"] # model_info - model_name = config['model_name'] - source_type = config['source_type'] - source_book = config['source_book'] + model_name = config["model_name"] + source_type = config["source_type"] + source_book = config["source_book"] # algorithm - algorithm_name = config['algorithm_name'] + algorithm_name = config["algorithm_name"] if not (cv_fold > 1): # no cross validation @@ -93,7 +92,7 @@ def test_bmi(): split_train_test(json_file, npy_file, train_period, test_period) kfold = [ - int(f_name[len("data_info_fold"): -len("_test.json")]) + int(f_name[len("data_info_fold") : -len("_test.json")]) for f_name in os.listdir(os.path.dirname(forcing_data)) if fnmatch.fnmatch(f_name, "*_fold*_test.json") ] @@ -104,19 +103,21 @@ def test_bmi(): os.path.dirname(forcing_data), f"data_info_fold{str(fold)}_train.json" ) train_data_file = os.path.join( - os.path.dirname(forcing_data), f"basins_lump_p_pe_q_fold{str(fold)}_train.npy" + os.path.dirname(forcing_data), + f"basins_lump_p_pe_q_fold{str(fold)}_train.npy", ) test_data_info_file = os.path.join( os.path.dirname(forcing_data), f"data_info_fold{str(fold)}_test.json" ) test_data_file = os.path.join( - os.path.dirname(forcing_data), f"basins_lump_p_pe_q_fold{str(fold)}_test.npy" + os.path.dirname(forcing_data), + f"basins_lump_p_pe_q_fold{str(fold)}_test.npy", ) if ( - os.path.exists(train_data_info_file) is False - or os.path.exists(train_data_file) is False - or os.path.exists(test_data_info_file) is False - or os.path.exists(test_data_file) is False + os.path.exists(train_data_info_file) is False + or os.path.exists(train_data_file) is False + or os.path.exists(test_data_info_file) is False + or os.path.exists(test_data_file) is False ): raise FileNotFoundError( "The data files are not found, please run datapreprocess4calibrate.py first." @@ -127,14 +128,17 @@ def test_bmi(): data_info_test = hydro_utils.unserialize_json_ordered(test_data_info_file) current_time = datetime.now().strftime("%b%d_%H-%M-%S") # one directory for one model + one hyperparam setting and one basin - save_dir = os.path.join(os.path.dirname(forcing_data), current_time + "_" + socket.gethostname() + "_fold" + str(fold)) + save_dir = os.path.join( + os.path.dirname(forcing_data), + current_time + "_" + socket.gethostname() + "_fold" + str(fold), + ) if algorithm_name == "SCE_UA": - random_seed = config['random_seed'] - rep = config['rep'] - ngs = config['ngs'] - kstop = config['kstop'] - peps = config['peps'] - pcento = config['pcento'] + random_seed = config["random_seed"] + rep = config["rep"] + ngs = config["ngs"] + kstop = config["kstop"] + peps = config["peps"] + pcento = config["pcento"] for i in range(len(data_info_train["basin"])): basin_id = data_info_train["basin"][i] basin_area = data_info_train["area"][i] @@ -148,23 +152,23 @@ def test_bmi(): os.makedirs(spotpy_db_dir) db_name = os.path.join(spotpy_db_dir, "SCEUA_" + model_name) sampler = calibrate_by_sceua( - data_train[:, i: i + 1, 0:2], - data_train[:, i: i + 1, -1:], + data_train[:, i : i + 1, 0:2], + data_train[:, i : i + 1, -1:], db_name, warmup_length=warmup_length, model={ - 'name': model_name, - 'source_type': source_type, - 'source_book': source_book + "name": model_name, + "source_type": source_type, + "source_book": source_book, }, algorithm={ - 'name': algorithm_name, - 'random_seed': random_seed, - 'rep': rep, - 'ngs': ngs, - 'kstop': kstop, - 'peps': peps, - 'pcento': pcento + "name": algorithm_name, + "random_seed": random_seed, + "rep": rep, + "ngs": ngs, + "kstop": kstop, + "peps": peps, + "pcento": pcento, }, ) @@ -182,7 +186,9 @@ def test_bmi(): ) model = xajBmi() - model.initialize(os.path.relpath("runxaj.yaml"), params, data_test[:, i: i + 1, 0:2]) + model.initialize( + os.path.relpath("runxaj.yaml"), params, data_test[:, i : i + 1, 0:2] + ) j = 0 while j <= len(data_info_test["time"]): model.update() @@ -197,7 +203,7 @@ def test_bmi(): ) qobs = hydro_constant.convert_unit( - data_test[warmup_length:, i: i + 1, -1:], + data_test[warmup_length:, i : i + 1, -1:], unit_now="mm/day", unit_final=hydro_constant.unit["streamflow"], basin_area=basin_area, @@ -219,12 +225,12 @@ def test_bmi(): basin_id, test_date, qsim, qobs, save_dir=spotpy_db_dir ) elif algorithm_name == "GA": - random_seed = config['random_seed'] - run_counts = config['run_counts'] - pop_num = config['pop_num'] - cross_prob = config['cross_prob'] - mut_prob = config['mut_prob'] - save_freq = config['save_freq'] + random_seed = config["random_seed"] + run_counts = config["run_counts"] + pop_num = config["pop_num"] + cross_prob = config["cross_prob"] + mut_prob = config["mut_prob"] + save_freq = config["save_freq"] for i in range(len(data_info_train["basin"])): basin_id = data_info_train["basin"][i] basin_area = data_info_train["area"][i] @@ -234,36 +240,36 @@ def test_bmi(): if not os.path.exists(deap_db_dir): os.makedirs(deap_db_dir) calibrate_by_ga( - data_train[:, i: i + 1, 0:2], - data_train[:, i: i + 1, -1:], + data_train[:, i : i + 1, 0:2], + data_train[:, i : i + 1, -1:], deap_db_dir, warmup_length=warmup_length, model={ - 'name': model_name, - 'source_type': source_type, - 'source_book': source_book + "name": model_name, + "source_type": source_type, + "source_book": source_book, }, ga_param={ - 'name': algorithm_name, - 'random_seed': random_seed, - 'run_counts': run_counts, - 'pop_num': pop_num, - 'cross_prob': cross_prob, - 'mut_prob': mut_prob, - 'save_freq': save_freq + "name": algorithm_name, + "random_seed": random_seed, + "run_counts": run_counts, + "pop_num": pop_num, + "cross_prob": cross_prob, + "mut_prob": mut_prob, + "save_freq": save_freq, }, ) show_ga_result( deap_db_dir, warmup_length=warmup_length, basin_id=basin_id, - the_data=data_train[:, i: i + 1, :], + the_data=data_train[:, i : i + 1, :], the_period=data_info_train["time"], basin_area=basin_area, model_info={ - 'name': model_name, - 'source_type': source_type, - 'source_book': source_book + "name": model_name, + "source_type": source_type, + "source_book": source_book, }, train_mode=True, ) @@ -271,13 +277,13 @@ def test_bmi(): deap_db_dir, warmup_length=warmup_length, basin_id=basin_id, - the_data=data_test[:, i: i + 1, :], + the_data=data_test[:, i : i + 1, :], the_period=data_info_test["time"], basin_area=basin_area, model_info={ - 'name': model_name, - 'source_type': source_type, - 'source_book': source_book + "name": model_name, + "source_type": source_type, + "source_book": source_book, }, train_mode=False, ) @@ -285,24 +291,37 @@ def test_bmi(): raise NotImplementedError( "We don't provide this calibrate method! Choose from 'SCE_UA' or 'GA'!" ) - summarize_parameters(save_dir, model_info={ - 'name': model_name, - 'source_type': source_type, - 'source_book': source_book - }) - renormalize_params(save_dir, model_info={ - 'name': model_name, - 'source_type': source_type, - 'source_book': source_book - }) - summarize_metrics(save_dir, model_info={ - 'name': model_name, - 'source_type': source_type, - 'source_book': source_book - }) - save_streamflow(save_dir, model_info={ - 'name': model_name, - 'source_type': source_type, - 'source_book': source_book, - }, fold=fold) + summarize_parameters( + save_dir, + model_info={ + "name": model_name, + "source_type": source_type, + "source_book": source_book, + }, + ) + renormalize_params( + save_dir, + model_info={ + "name": model_name, + "source_type": source_type, + "source_book": source_book, + }, + ) + summarize_metrics( + save_dir, + model_info={ + "name": model_name, + "source_type": source_type, + "source_book": source_book, + }, + ) + save_streamflow( + save_dir, + model_info={ + "name": model_name, + "source_type": source_type, + "source_book": source_book, + }, + fold=fold, + ) print(f"Finish calibrating the {fold}-th fold") From 51f650d6f703a1c6ae8e2ff95e5882162e3caf91 Mon Sep 17 00:00:00 2001 From: ouyangwenyu Date: Sat, 28 Oct 2023 16:43:03 +0800 Subject: [PATCH 16/19] delete some unused lines in xaj --- hydromodel/models/xaj.py | 35 ----------------------------------- hydromodel/models/xaj_bmi.py | 10 +++++----- test/test_xaj_bmi.py | 5 ++--- 3 files changed, 7 insertions(+), 43 deletions(-) diff --git a/hydromodel/models/xaj.py b/hydromodel/models/xaj.py index b780d9b..665fb4b 100644 --- a/hydromodel/models/xaj.py +++ b/hydromodel/models/xaj.py @@ -900,14 +900,6 @@ def xaj_runoff( source_book = kwargs.get("source_book", "HF") # params param_ranges = MODEL_PARAM_DICT[model_name]["param_range"] - if model_name == "xaj": - route_method = "CSL" - elif model_name == "xaj_mz": - route_method = "MZ" - else: - raise NotImplementedError( - "We don't provide this route method now! Please use 'CS' or 'MZ'!" - ) xaj_params = [ (value[1] - value[0]) * params_runoff[:, i] + value[0] for i, (key, value) in enumerate(param_ranges.items()) @@ -991,8 +983,6 @@ def xaj_route( **kwargs, ) -> Union[tuple, np.array]: model_name = kwargs.get("name", "xaj") - source_type = kwargs.get("source_type", "sources") - source_book = kwargs.get("source_book", "HF") # params param_ranges = MODEL_PARAM_DICT[model_name]["param_range"] if model_name == "xaj": @@ -1007,20 +997,6 @@ def xaj_route( (value[1] - value[0]) * params_route[:, i] + value[0] for i, (key, value) in enumerate(param_ranges.items()) ] - k = xaj_params[0] - b = xaj_params[1] - im = xaj_params[2] - um = xaj_params[3] - lm = xaj_params[4] - dm = xaj_params[5] - c = xaj_params[6] - sm = xaj_params[7] - ex = xaj_params[8] - ki_ = xaj_params[9] - kg_ = xaj_params[10] - # ki+kg should be smaller than 1; if not, we scale them - ki = np.where(ki_ + kg_ < 1.0, ki_, (1.0 - PRECISION) / (ki_ + kg_) * ki_) - kg = np.where(ki_ + kg_ < 1.0, kg_, (1.0 - PRECISION) / (ki_ + kg_) * kg_) if route_method == "CSL": cs = xaj_params[11] l = xaj_params[12] @@ -1140,17 +1116,6 @@ def xaj_state( ci = xaj_params[13] cg = xaj_params[14] - # initialize state values - # if warmup_length > 0: - # p_and_e_warmup = p_and_e[0:warmup_length, :, :] - # _q, _e, *w0, s0, fr0, qi0, qg0 = xaj_state( - # p_and_e_warmup, - # params, - # return_state=True, - # warmup_length=0, - # **kwargs, - # ) - # else: w0 = (0.5 * um, 0.5 * lm, 0.5 * dm) s0 = 0.5 * sm fr0 = np.full(ex.shape, 0.1) diff --git a/hydromodel/models/xaj_bmi.py b/hydromodel/models/xaj_bmi.py index 435ab67..e94f620 100644 --- a/hydromodel/models/xaj_bmi.py +++ b/hydromodel/models/xaj_bmi.py @@ -76,7 +76,7 @@ def initialize(self, config_file, params, p_and_e): self.test_start_time_str = test_period[0] self.test_end_time_str = test_period[1] - except: + except Exception: import traceback traceback.print_exc() @@ -294,8 +294,8 @@ def start_Time(self, _start_time_str): self._startTime = datetime.datetime( int(year), int(month), int(day), int(hour), int(minute), int(second) ) - except ValueError: - raise ValueError("Invalid start date format!") + except ValueError as e: + raise ValueError("Invalid start date format!") from e return self._startTime @@ -314,6 +314,6 @@ def end_Time(self, _end_time_str): self._endTime = datetime.datetime( int(year), int(month), int(day), int(hour), int(minute), int(second) ) - except ValueError: - raise ValueError("Invalid start date format!") + except ValueError as e: + raise ValueError("Invalid start date format!") from e return self._endTime diff --git a/test/test_xaj_bmi.py b/test/test_xaj_bmi.py index 0b65331..875a9d7 100644 --- a/test/test_xaj_bmi.py +++ b/test/test_xaj_bmi.py @@ -81,14 +81,13 @@ def test_bmi(): # algorithm algorithm_name = config["algorithm_name"] - if not (cv_fold > 1): + if cv_fold <= 1: # no cross validation periods = np.sort( [train_period[0], train_period[1], test_period[0], test_period[1]] ) - if cv_fold > 1: - cross_valid_data(json_file, npy_file, periods, warmup_length, cv_fold) else: + cross_valid_data(json_file, npy_file, periods, warmup_length, cv_fold) split_train_test(json_file, npy_file, train_period, test_period) kfold = [ From 3caac4a9e2aa14f3db20c9c24264ebd84fcf59e0 Mon Sep 17 00:00:00 2001 From: ouyangwenyu Date: Sat, 28 Oct 2023 17:41:05 +0800 Subject: [PATCH 17/19] add files from cookiecutter template --- .editorconfig | 21 +++++++ .github/ISSUE_TEMPLATE/bug_report.md | 25 ++++++++ .github/ISSUE_TEMPLATE/config.yml | 10 ++++ .github/ISSUE_TEMPLATE/feature_request.md | 18 ++++++ .github/workflows/docs-build.yml | 51 ++++++++++++++++ .github/workflows/docs.yml | 28 +++++++++ .github/workflows/macos.yml | 38 ++++++++++++ .github/workflows/publish-to-pypi.yml | 38 ------------ .github/workflows/pypi.yml | 30 ++++++++++ .github/workflows/ubuntu.yml | 48 +++++++++++++++ .github/workflows/windows.yml | 33 +++++++++++ AUTHORS.rst | 13 +++++ MANIFEST.in | 8 ++- environment-dev.yml | 4 ++ requirements.txt | 6 +- requirements_dev.txt | 14 +++++ setup.cfg | 26 +++++++++ setup.py | 71 ++++++++++++++++++----- 18 files changed, 424 insertions(+), 58 deletions(-) create mode 100644 .editorconfig create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md create mode 100644 .github/ISSUE_TEMPLATE/config.yml create mode 100644 .github/ISSUE_TEMPLATE/feature_request.md create mode 100644 .github/workflows/docs-build.yml create mode 100644 .github/workflows/docs.yml create mode 100644 .github/workflows/macos.yml delete mode 100644 .github/workflows/publish-to-pypi.yml create mode 100644 .github/workflows/pypi.yml create mode 100644 .github/workflows/ubuntu.yml create mode 100644 .github/workflows/windows.yml create mode 100644 AUTHORS.rst create mode 100644 requirements_dev.txt create mode 100644 setup.cfg diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 0000000..d4a2c44 --- /dev/null +++ b/.editorconfig @@ -0,0 +1,21 @@ +# http://editorconfig.org + +root = true + +[*] +indent_style = space +indent_size = 4 +trim_trailing_whitespace = true +insert_final_newline = true +charset = utf-8 +end_of_line = lf + +[*.bat] +indent_style = tab +end_of_line = crlf + +[LICENSE] +insert_final_newline = false + +[Makefile] +indent_style = tab diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..c89fc62 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,25 @@ +--- +name: Bug Report +about: Create a bug report to help us improve +labels: bug +--- + + + +### Environment Information + +- hydromodel version: +- Python version: +- Operating System: + +### Description + +Describe what you were trying to get done. +Tell us what happened, what went wrong, and what you expected to happen. + +### What I Did + +``` +Paste the command(s) you ran and the output. +If there was a crash, please include the traceback here. +``` diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000..de46af5 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,10 @@ +contact_links: + - name: Ask questions + url: https://github.com/OuyangWenyu/hydro-model-xaj/discussions/categories/q-a + about: Please ask and answer questions here. + - name: Ideas + url: https://github.com/OuyangWenyu/hydro-model-xaj/discussions/categories/ideas + about: Please share your ideas here. + - name: Ask questions from the GIS community + url: https://gis.stackexchange.com + about: To get answers from questions in the GIS community, please ask and answer questions here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..cf7acb9 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,18 @@ +--- +name: Feature Request +about: Submit a feature request to help us improve +labels: Feature Request +--- + + + +### Description + +Describe the feature (e.g., new functions/tutorials) you would like to propose. +Tell us what can be achieved with this new feature and what's the expected outcome. + +### Source code + +``` +Paste your source code here if have sample code to share. +``` diff --git a/.github/workflows/docs-build.yml b/.github/workflows/docs-build.yml new file mode 100644 index 0000000..876561c --- /dev/null +++ b/.github/workflows/docs-build.yml @@ -0,0 +1,51 @@ +name: docs-build +on: + pull_request: + branches: + - master + +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + with: + fetch-depth: 0 + - uses: actions/setup-python@v4 + with: + python-version: "3.10" + - name: Install GDAL + run: | + python -m pip install --upgrade pip + pip install --find-links=https://girder.github.io/large_image_wheels --no-cache GDAL pyproj + - name: Test GDAL installation + run: | + python -c "from osgeo import gdal" + gdalinfo --version + - name: Install dependencies + run: | + pip install --no-cache-dir Cython + pip install -r requirements.txt -r requirements_dev.txt -r requirements_docs.txt + pip install . + - name: Discover typos with codespell + run: codespell --skip="*.csv,*.geojson,*.json,*.js,*.html,*cff,*.pdf,./.git" --ignore-words-list="aci,acount,acounts,fallow,hart,hist,nd,ned,ois,wqs" + - name: PKG-TEST + run: | + python -m unittest discover tests/ + - name: Build docs + run: | + mkdocs build + # - name: Deploy to Netlify + # uses: nwtgck/actions-netlify@v2.0 + # with: + # publish-dir: "./site" + # production-branch: master + # github-token: ${{ secrets.GITHUB_TOKEN }} + # deploy-message: "Deploy from GitHub Actions" + # enable-pull-request-comment: true + # enable-commit-comment: false + # overwrites-pull-request-comment: true + # env: + # NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }} + # NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }} + # timeout-minutes: 10 diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..6ea88e5 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,28 @@ +name: docs +on: + push: + branches: + - master +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: 3.9 + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install --user --no-cache-dir Cython + pip install --user -r requirements.txt + pip install . + - name: Discover typos with codespell + run: | + pip install codespell + codespell --skip="*.csv,*.geojson,*.json,*.js,*.html,*cff,./.git" --ignore-words-list="aci,acount,acounts,fallow,hart,hist,nd,ned,ois,wqs,watermask" + - name: PKG-TEST + run: | + python -m unittest discover tests/ + - run: pip install -r requirements_docs.txt + - run: mkdocs gh-deploy --force diff --git a/.github/workflows/macos.yml b/.github/workflows/macos.yml new file mode 100644 index 0000000..4b3ed55 --- /dev/null +++ b/.github/workflows/macos.yml @@ -0,0 +1,38 @@ +on: + push: + branches: + - master + pull_request: + branches: + - master + +name: macOS build +jobs: + test-macOS: + runs-on: ${{ matrix.os }} + name: ${{ matrix.os }} (${{ matrix.python-version}}) + strategy: + fail-fast: false + matrix: + os: ["macOS-latest"] + python-version: ["3.10"] + steps: + - name: Checkout code + uses: actions/checkout@v3 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version}} + - name: Install GDAL + run: | + brew install gdal + - name: Test GDAL installation + run: | + gdalinfo --version + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install --no-cache-dir Cython + pip install -r requirements.txt + pip install . diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml deleted file mode 100644 index 2bc7304..0000000 --- a/.github/workflows/publish-to-pypi.yml +++ /dev/null @@ -1,38 +0,0 @@ -name: Publish Python distributions to PyPI - -on: - push: - workflow_dispatch: - -jobs: - build-n-publish: - name: Build and publish Python distributions to PyPI - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@master - - name: Set up Python 3.9 - uses: actions/setup-python@v1 - with: - python-version: 3.9 - - - name: Install pypa/build - run: >- - python -m - pip install - build - --user - - name: Build a binary wheel and a source tarball - run: >- - python -m - build - --sdist - --wheel - --outdir dist/ - . - - - name: Publish distribution to PyPI - uses: pypa/gh-action-pypi-publish@release/v1 - with: - user: __token__ - password: ${{ secrets.HYDRO_XAJ_TOKEN }} diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml new file mode 100644 index 0000000..2bbb0c0 --- /dev/null +++ b/.github/workflows/pypi.yml @@ -0,0 +1,30 @@ +# This workflows will upload a Python Package using Twine when a release is created +# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries + +name: pypi + +on: + release: + types: [created] + +jobs: + deploy: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: "3.x" + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install setuptools wheel twine + - name: Build and publish + env: + TWINE_USERNAME: ${{ secrets.PYPI_USERS }} + TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + run: | + python setup.py sdist bdist_wheel + twine upload dist/* diff --git a/.github/workflows/ubuntu.yml b/.github/workflows/ubuntu.yml new file mode 100644 index 0000000..6cb7c0c --- /dev/null +++ b/.github/workflows/ubuntu.yml @@ -0,0 +1,48 @@ +on: + push: + branches: + - master + - dev + pull_request: + branches: + - master + - dev + +name: Linux build +jobs: + py-check: + runs-on: ${{ matrix.config.os }} + name: ${{ matrix.config.os }} (${{ matrix.config.py }}) + strategy: + fail-fast: false + matrix: + config: + - { os: ubuntu-latest, py: "3.8" } + - { os: ubuntu-latest, py: "3.9" } + - { os: ubuntu-latest, py: "3.10" } + - { os: ubuntu-latest, py: "3.11" } + steps: + - name: Checkout Code + uses: actions/checkout@v3 + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.config.py }} + - name: Install GDAL + run: | + python -m pip install --upgrade pip + pip install --no-cache-dir Cython + pip install --find-links=https://girder.github.io/large_image_wheels --no-cache GDAL + - name: Test GDAL installation + run: | + python -c "from osgeo import gdal" + gdalinfo --version + - name: Install dependencies + run: | + pip install --user -r requirements.txt + pip install --user -r requirements_dev.txt + pip install . + - name: PKG-TEST + run: | + python -m unittest discover tests/ + diff --git a/.github/workflows/windows.yml b/.github/workflows/windows.yml new file mode 100644 index 0000000..9e9deaa --- /dev/null +++ b/.github/workflows/windows.yml @@ -0,0 +1,33 @@ +on: + push: + branches: + - master + - dev + pull_request: + branches: + - master + - dev + +name: Windows build +jobs: + test-windows: + runs-on: windows-latest + steps: + - uses: actions/checkout@v3 + - name: Install miniconda + uses: conda-incubator/setup-miniconda@v2 + with: + auto-activate-base: true + python-version: "3.10" + - name: Install GDAL + run: conda install -c conda-forge gdal --yes + - name: Test GDAL installation + run: | + python -c "from osgeo import gdal" + gdalinfo --version + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install --no-cache-dir Cython + pip install -r requirements.txt + pip install . diff --git a/AUTHORS.rst b/AUTHORS.rst new file mode 100644 index 0000000..a199192 --- /dev/null +++ b/AUTHORS.rst @@ -0,0 +1,13 @@ +======= +Credits +======= + +Development Lead +---------------- + +* Wenyu Ouyang + +Contributors +------------ + +None yet. Why not be the first? diff --git a/MANIFEST.in b/MANIFEST.in index 57fdc18..89411aa 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1 +1,7 @@ -recursive-include XXX *.csv *.txt # 打包需包含csv、txt为后缀的文件；XXX为包名 \ No newline at end of file +include LICENSE +include README.md +include requirements.txt + +recursive-exclude * __pycache__ +recursive-exclude * *.py[co] + diff --git a/environment-dev.yml b/environment-dev.yml index 288a934..d6be0cb 100644 --- a/environment-dev.yml +++ b/environment-dev.yml @@ -23,4 +23,8 @@ dependencies: # pip - pip - pip: + - setuptools + - wheel + - twine + - bump2version - hydrodataset diff --git a/requirements.txt b/requirements.txt index 74513e9..245c186 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,16 +1,14 @@ -python=3.10 ipykernel numpy numba pandas scikit-learn deap -spotpy=1.5.14 +spotpy==1.5.14 seaborn tqdm pytest hydrodataset bmipy pyyaml -requests -spotpy \ No newline at end of file +requests \ No newline at end of file diff --git a/requirements_dev.txt b/requirements_dev.txt new file mode 100644 index 0000000..c4e98f7 --- /dev/null +++ b/requirements_dev.txt @@ -0,0 +1,14 @@ +black +black[jupyter] +pip +bump2version +wheel +watchdog +flake8 +tox +coverage +Sphinx +twine + +pytest +pytest-runner \ No newline at end of file diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000..2e09fd3 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,26 @@ +[bumpversion] +current_version = 0.0.1 +commit = True +tag = True + +[bumpversion:file:setup.py] +search = version='{current_version}' +replace = version='{new_version}' + +[bumpversion:file:hydromodel/__init__.py] +search = __version__ = '{current_version}' +replace = __version__ = '{new_version}' + +[bdist_wheel] +universal = 1 + +[flake8] +exclude = docs + +[aliases] +# Define setup.py command aliases here +test = pytest + +[tool:pytest] +collect_ignore = ['setup.py'] + diff --git a/setup.py b/setup.py index e143dd7..bf0dedf 100644 --- a/setup.py +++ b/setup.py @@ -1,30 +1,71 @@ +#!/usr/bin/env python """ Author: Wenyu Ouyang Date: 2023-10-28 09:16:46 -LastEditTime: 2023-10-28 09:27:22 +LastEditTime: 2023-10-28 17:36:12 LastEditors: Wenyu Ouyang -Description: setup.py for hydromodel package +Description: The setup script FilePath: \hydro-model-xaj\setup.py Copyright (c) 2023-2024 Wenyu Ouyang. All rights reserved. """ +import io import pathlib +from os import path as op from setuptools import setup, find_packages -readme = pathlib.Path("README.md").read_text() +readme = pathlib.Path("README.md").read_text(encoding='utf-8') +here = op.abspath(op.dirname(__file__)) + +# get the dependencies and installs +with io.open(op.join(here, "requirements.txt"), encoding="utf-8") as f: + all_reqs = f.read().split("\n") + +install_requires = [x.strip() for x in all_reqs if "git+" not in x] +dependency_links = [x.strip().replace("git+", "") for x in all_reqs if "git+" not in x] + +requirements = [] + +setup_requirements = [ + "pytest-runner", +] + +test_requirements = [ + "pytest>=3", +] + setup( - name="hydromodel", # 输入项目名称 - version="0.0.1", # 输入版本号 - keywords=[""], # 输入关键词 - description="", # 输入概述 + author="Wenyu Ouyang", + author_email="wenyuouyang@outlook.com", + python_requires=">=3.9", + classifiers=[ + "Intended Audience :: Developers", + "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", + "Natural Language :: English", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + ], + description="hydrological models starting from XinAnJiang", + entry_points={ + "console_scripts": [ + "hydromodel=hydromodel.cli:main", + ], + }, + install_requires=install_requires, + dependency_links=dependency_links, + license="GNU General Public License v3", long_description=readme, long_description_content_type="text/markdown", - url="https://github.com/iHeadWater/hydro-model-xaj", # 输入项目Github仓库的链接 - author="iHeadWater", # 输入作者名字 - author_email="", # 输入作者邮箱 - license="MIT_license", # 此为声明文件，一般填写 MIT_license - packages=find_packages(), include_package_data=True, - platforms="any", - install_requires=[""], # 输入项目所用的包 - python_requires=">= 3.7 ", # Python版本要求 + keywords="hydromodel", + name="hydromodel", + packages=find_packages(include=["hydromodel", "hydromodel.*"]), + setup_requires=setup_requirements, + test_suite="tests", + tests_require=test_requirements, + url="https://github.com/iHeadWater/hydro-model-xaj", + version="0.0.1", + zip_safe=False, ) From 4f33f707d5619f975e50f57aeafe8f060d9c09ef Mon Sep 17 00:00:00 2001 From: forestbat Date: Wed, 8 Nov 2023 15:19:07 +0800 Subject: [PATCH 18/19] Create publish-to-pypi-token.yml --- .github/workflows/publish-to-pypi-token.yml | 37 +++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 .github/workflows/publish-to-pypi-token.yml diff --git a/.github/workflows/publish-to-pypi-token.yml b/.github/workflows/publish-to-pypi-token.yml new file mode 100644 index 0000000..ef3c9e2 --- /dev/null +++ b/.github/workflows/publish-to-pypi-token.yml @@ -0,0 +1,37 @@ +name: Publish Python distributions to PyPI + +on: + push: + workflow_dispatch: + +jobs: + build-n-publish: + name: Build and publish Python distributions to PyPI + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@master + - name: Set up Python 3.9 + uses: actions/setup-python@v1 + with: + python-version: 3.9 + + - name: Install pypa/build + run: >- + python -m + pip install + build + --user + - name: Build a binary wheel and a source tarball + run: >- + python -m + build + --sdist + --wheel + --outdir dist/ + . + - name: Publish distribution to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + user: __token__ + password: ${{ secrets.HYDRO_XAJ_TOKEN }} From 655628b44fd9eae67597f8ef7f2413dac1fadd69 Mon Sep 17 00:00:00 2001 From: forestbat Date: Wed, 8 Nov 2023 15:20:53 +0800 Subject: [PATCH 19/19] Update setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index bf0dedf..96730f7 100644 --- a/setup.py +++ b/setup.py @@ -66,6 +66,6 @@ test_suite="tests", tests_require=test_requirements, url="https://github.com/iHeadWater/hydro-model-xaj", - version="0.0.1", + version="0.0.2", zip_safe=False, )