From 235bb26f14ff1e261ce08e645bb077da2754dcf0 Mon Sep 17 00:00:00 2001
From: ouyangwenyu <wenyuouyang@outlook.com>
Date: Thu, 21 Mar 2024 22:04:25 +0800
Subject: [PATCH] refactor all folders to start for a complete new version

---
 --algorithm                                   |    0
 environment-dev.yml => env-dev.yml            |   21 +-
 environment.yml                               |   20 -
 hydromodel/app/show_results.ipynb             |  236 -
 hydromodel/cli.py                             |   19 -
 hydromodel/common.py                          |    7 -
 .../{calibrate => datasets}/__init__.py       |    0
 .../{data => datasets}/camels_format_data.py  |    0
 .../{data => datasets}/data_postprocess.py    |    0
 .../{data => datasets}/data_preprocess.py     |    2 +-
 hydromodel/{utils => datasets}/dmca_esr.py    |    0
 hydromodel/{utils => datasets}/units.py       |    0
 hydromodel/example/__init__.py                |    0
 hydromodel/example/daily_flow_27071.txt       | 9131 ----------------
 hydromodel/example/daily_rainfall_27071.txt   | 9131 ----------------
 hydromodel/example/division_flow.csv          | 9132 -----------------
 hydromodel/example/division_rain.csv          | 9132 -----------------
 hydromodel/hydromodel.py                      |    1 -
 hydromodel/models/configuration.py            |   27 -
 hydromodel/models/xaj_bmi.py                  |  261 +-
 hydromodel/models/xaj_river.py                |  223 -
 hydromodel/{data => trainers}/__init__.py     |    0
 .../{calibrate => trainers}/calibrate_ga.py   |    2 +-
 .../calibrate_ga_xaj_bmi.py                   |    2 +-
 .../calibrate_sceua.py                        |    0
 .../calibrate_sceua_xaj_bmi.py                |    0
 {test => hydromodel/trainers}/cchz_diso.py    |    0
 hydromodel/{utils => trainers}/plots.py       |    0
 hydromodel/utils/__init__.py                  |    0
 hydromodel/utils/constant_unit.py             |   63 -
 hydromodel/utils/hydro_utils.py               |  514 -
 hydromodel/visual/hydro_plot.py               |  154 -
 notebooks/show_results.ipynb                  |  182 +-
 requirements.txt                              |   11 +-
 requirements_dev.txt                          |   13 +-
 scripts/calibrate_xaj.py                      |    8 +-
 scripts/datapostprocess4calibrate.py          |    2 +-
 scripts/datapreprocess4calibrate.py           |    2 +-
 {xaj => test}/test-xaj-bmi.py                 |    0
 test/test_data.py                             |    2 +-
 test/test_gr4j.py                             |    4 +-
 test/test_hymod.py                            |    4 +-
 test/test_rr_event_iden.py                    |    2 +-
 test/test_xaj.py                              |    8 +-
 test/test_xaj_bmi.py                          |   10 +-
 xaj/configuration.py                          |   51 -
 xaj/constant_unit.py                          |   63 -
 xaj/params.py                                 |  141 -
 xaj/runxaj.yaml                               |   21 -
 xaj/xaj_bmi.py                                |  274 -
 xaj/xajmodel.py                               | 1060 --
 51 files changed, 202 insertions(+), 39734 deletions(-)
 delete mode 100644 --algorithm
 rename environment-dev.yml => env-dev.yml (55%)
 delete mode 100644 environment.yml
 delete mode 100644 hydromodel/app/show_results.ipynb
 delete mode 100644 hydromodel/cli.py
 delete mode 100644 hydromodel/common.py
 rename hydromodel/{calibrate => datasets}/__init__.py (100%)
 rename hydromodel/{data => datasets}/camels_format_data.py (100%)
 rename hydromodel/{data => datasets}/data_postprocess.py (100%)
 rename hydromodel/{data => datasets}/data_preprocess.py (99%)
 rename hydromodel/{utils => datasets}/dmca_esr.py (100%)
 rename hydromodel/{utils => datasets}/units.py (100%)
 delete mode 100644 hydromodel/example/__init__.py
 delete mode 100644 hydromodel/example/daily_flow_27071.txt
 delete mode 100644 hydromodel/example/daily_rainfall_27071.txt
 delete mode 100644 hydromodel/example/division_flow.csv
 delete mode 100644 hydromodel/example/division_rain.csv
 delete mode 100644 hydromodel/hydromodel.py
 delete mode 100644 hydromodel/models/configuration.py
 delete mode 100644 hydromodel/models/xaj_river.py
 rename hydromodel/{data => trainers}/__init__.py (100%)
 rename hydromodel/{calibrate => trainers}/calibrate_ga.py (99%)
 rename hydromodel/{calibrate => trainers}/calibrate_ga_xaj_bmi.py (99%)
 rename hydromodel/{calibrate => trainers}/calibrate_sceua.py (100%)
 rename hydromodel/{calibrate => trainers}/calibrate_sceua_xaj_bmi.py (100%)
 rename {test => hydromodel/trainers}/cchz_diso.py (100%)
 rename hydromodel/{utils => trainers}/plots.py (100%)
 delete mode 100644 hydromodel/utils/__init__.py
 delete mode 100644 hydromodel/utils/constant_unit.py
 delete mode 100644 hydromodel/utils/hydro_utils.py
 delete mode 100644 hydromodel/visual/hydro_plot.py
 rename {xaj => test}/test-xaj-bmi.py (100%)
 delete mode 100644 xaj/configuration.py
 delete mode 100644 xaj/constant_unit.py
 delete mode 100644 xaj/params.py
 delete mode 100644 xaj/runxaj.yaml
 delete mode 100644 xaj/xaj_bmi.py
 delete mode 100644 xaj/xajmodel.py

diff --git a/--algorithm b/--algorithm
deleted file mode 100644
index e69de29..0000000
diff --git a/environment-dev.yml b/env-dev.yml
similarity index 55%
rename from environment-dev.yml
rename to env-dev.yml
index 90bdb97..2da99b2 100644
--- a/environment-dev.yml
+++ b/env-dev.yml
@@ -1,4 +1,4 @@
-name: xaj-dev
+name: xaj
 channels:
   - pytorch
   - conda-forge
@@ -6,27 +6,16 @@ channels:
 dependencies:
   - python=3.10
   - ipykernel
-  - numpy
   - numba
-  - pandas
   - scikit-learn
   - deap
   - spotpy=1.5.14
-  - seaborn
-  - tqdm
   - yaml
   - bmipy
-  # for dev 
   - pyarrow
-  - hydroerr
-  - xarray
-  - pytorch=1.12.1
-  - torchvision=0.13.1
-  - torchaudio=0.12.1
-  - cudatoolkit=11.3
-  - tensorboard
-  - sphinx
   - pytest
+  # for development
+  - sphinx
   - black
   - flake8
   # pip
@@ -36,7 +25,5 @@ dependencies:
     - wheel
     - twine
     - bump2version
+    - muskingumcunge
     - hydrodataset
-    # config for deep learning
-    - hydra-core
-    - accelerate
diff --git a/environment.yml b/environment.yml
deleted file mode 100644
index da831b9..0000000
--- a/environment.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-name: xaj
-channels:
-  - conda-forge
-  - defaults
-dependencies:
-  - python=3.10
-  - ipykernel
-  - numpy
-  - numba
-  - pandas
-  - scikit-learn
-  - deap
-  - spotpy=1.5.14
-  - seaborn
-  - tqdm
-  - yaml
-  - bmipy
-  - pip
-  - pip:
-    - hydrodataset
diff --git a/hydromodel/app/show_results.ipynb b/hydromodel/app/show_results.ipynb
deleted file mode 100644
index 2948514..0000000
--- a/hydromodel/app/show_results.ipynb
+++ /dev/null
@@ -1,236 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 分析结果\n",
-    "\n",
-    "这是一个用于分析模型率定后测试结果的 Jupyter Notebook。读取各个exp下面的结果文件看看。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Please Check your directory:\n",
-      "ROOT_DIR of the repo:  d:\\code\\hydro-model-xaj\n",
-      "DATASET_DIR of the repo:  d:\\data\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import sys\n",
-    "from pathlib import Path\n",
-    "\n",
-    "sys.path.append(os.path.dirname(Path(os.path.abspath('')).parent))\n",
-    "import definitions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric_mean_file = Path(os.path.join(\"D:/研究生/毕业论文/new毕业论文/预答辩/碧流河水库/模型运行/basins_test_metrics_mean_all_cases.csv\"))\n",
-    "metric_median_file = Path(os.path.join(\"D:/研究生/毕业论文/new毕业论文/预答辩/碧流河水库/模型运行/basins_test_metrics_median_all_cases.csv\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric_mean = pd.read_csv(metric_mean_file, index_col=0)\n",
-    "metric_median = pd.read_csv(metric_median_file, index_col=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Bias</th>\n",
-       "      <th>RMSE</th>\n",
-       "      <th>ubRMSE</th>\n",
-       "      <th>Corr</th>\n",
-       "      <th>R2</th>\n",
-       "      <th>NSE</th>\n",
-       "      <th>KGE</th>\n",
-       "      <th>FHV</th>\n",
-       "      <th>FLV</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>HFsources</th>\n",
-       "      <td>-10.431724</td>\n",
-       "      <td>84.941974</td>\n",
-       "      <td>84.29898</td>\n",
-       "      <td>0.625164</td>\n",
-       "      <td>0.380791</td>\n",
-       "      <td>0.380791</td>\n",
-       "      <td>0.308037</td>\n",
-       "      <td>-30.076788</td>\n",
-       "      <td>-100.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                Bias       RMSE    ubRMSE      Corr        R2       NSE  \\\n",
-       "HFsources -10.431724  84.941974  84.29898  0.625164  0.380791  0.380791   \n",
-       "\n",
-       "                KGE        FHV    FLV  \n",
-       "HFsources  0.308037 -30.076788 -100.0  "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metric_mean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Bias</th>\n",
-       "      <th>RMSE</th>\n",
-       "      <th>ubRMSE</th>\n",
-       "      <th>Corr</th>\n",
-       "      <th>R2</th>\n",
-       "      <th>NSE</th>\n",
-       "      <th>KGE</th>\n",
-       "      <th>FHV</th>\n",
-       "      <th>FLV</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>HFsources</th>\n",
-       "      <td>-10.431724</td>\n",
-       "      <td>84.941974</td>\n",
-       "      <td>84.29898</td>\n",
-       "      <td>0.625164</td>\n",
-       "      <td>0.380791</td>\n",
-       "      <td>0.380791</td>\n",
-       "      <td>0.308037</td>\n",
-       "      <td>-30.076788</td>\n",
-       "      <td>-100.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                Bias       RMSE    ubRMSE      Corr        R2       NSE  \\\n",
-       "HFsources -10.431724  84.941974  84.29898  0.625164  0.380791  0.380791   \n",
-       "\n",
-       "                KGE        FHV    FLV  \n",
-       "HFsources  0.308037 -30.076788 -100.0  "
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metric_median"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.9.7 ('xaj')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "5ff2b0240d3185dc85fb5f0a6365eefe977ea7c2afca8c64838dc9fcf4f02a96"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/hydromodel/cli.py b/hydromodel/cli.py
deleted file mode 100644
index c2cef58..0000000
--- a/hydromodel/cli.py
+++ /dev/null
@@ -1,19 +0,0 @@
-"""Console script for hydromodel."""
-import argparse
-import sys
-
-
-def main():
-    """Console script for hydromodel."""
-    parser = argparse.ArgumentParser()
-    parser.add_argument('_', nargs='*')
-    args = parser.parse_args()
-
-    print("Arguments: " + str(args._))
-    print("Replace this message by putting your code into "
-          "hydromodel.cli.main")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())  # pragma: no cover
diff --git a/hydromodel/common.py b/hydromodel/common.py
deleted file mode 100644
index 6967be7..0000000
--- a/hydromodel/common.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""The common module contains common functions and classes used by the other modules.
-"""
-
-def hello_world():
-    """Prints "Hello World!" to the console.
-    """
-    print("Hello World!")
\ No newline at end of file
diff --git a/hydromodel/calibrate/__init__.py b/hydromodel/datasets/__init__.py
similarity index 100%
rename from hydromodel/calibrate/__init__.py
rename to hydromodel/datasets/__init__.py
diff --git a/hydromodel/data/camels_format_data.py b/hydromodel/datasets/camels_format_data.py
similarity index 100%
rename from hydromodel/data/camels_format_data.py
rename to hydromodel/datasets/camels_format_data.py
diff --git a/hydromodel/data/data_postprocess.py b/hydromodel/datasets/data_postprocess.py
similarity index 100%
rename from hydromodel/data/data_postprocess.py
rename to hydromodel/datasets/data_postprocess.py
diff --git a/hydromodel/data/data_preprocess.py b/hydromodel/datasets/data_preprocess.py
similarity index 99%
rename from hydromodel/data/data_preprocess.py
rename to hydromodel/datasets/data_preprocess.py
index 39befb0..0b46221 100644
--- a/hydromodel/data/data_preprocess.py
+++ b/hydromodel/datasets/data_preprocess.py
@@ -20,7 +20,7 @@
 from hydroutils import hydro_time, hydro_file
 
 sys.path.append(os.path.dirname(Path(os.path.abspath(__file__)).parent.parent))
-from hydromodel.data import camels_format_data
+from hydromodel.datasets import camels_format_data
 
 
 def trans_camels_format_to_xaj_format(
diff --git a/hydromodel/utils/dmca_esr.py b/hydromodel/datasets/dmca_esr.py
similarity index 100%
rename from hydromodel/utils/dmca_esr.py
rename to hydromodel/datasets/dmca_esr.py
diff --git a/hydromodel/utils/units.py b/hydromodel/datasets/units.py
similarity index 100%
rename from hydromodel/utils/units.py
rename to hydromodel/datasets/units.py
diff --git a/hydromodel/example/__init__.py b/hydromodel/example/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hydromodel/example/daily_flow_27071.txt b/hydromodel/example/daily_flow_27071.txt
deleted file mode 100644
index e98b9d9..0000000
--- a/hydromodel/example/daily_flow_27071.txt
+++ /dev/null
@@ -1,9131 +0,0 @@
-1990 1 1 0 0 0 0.571000
-1990 1 2 0 0 0 0.623000
-1990 1 3 0 0 0 1.202000
-1990 1 4 0 0 0 1.342000
-1990 1 5 0 0 0 0.898000
-1990 1 6 0 0 0 0.809000
-1990 1 7 0 0 0 1.246000
-1990 1 8 0 0 0 1.352000
-1990 1 9 0 0 0 1.295000
-1990 1 10 0 0 0 1.079000
-1990 1 11 0 0 0 2.093000
-1990 1 12 0 0 0 2.941000
-1990 1 13 0 0 0 1.445000
-1990 1 14 0 0 0 1.013000
-1990 1 15 0 0 0 0.889000
-1990 1 16 0 0 0 1.128000
-1990 1 17 0 0 0 2.489000
-1990 1 18 0 0 0 1.490000
-1990 1 19 0 0 0 1.161000
-1990 1 20 0 0 0 2.640000
-1990 1 21 0 0 0 1.253000
-1990 1 22 0 0 0 1.134000
-1990 1 23 0 0 0 1.556000
-1990 1 24 0 0 0 2.974000
-1990 1 25 0 0 0 1.834000
-1990 1 26 0 0 0 5.458000
-1990 1 27 0 0 0 2.490000
-1990 1 28 0 0 0 3.318000
-1990 1 29 0 0 0 3.036000
-1990 1 30 0 0 0 6.627000
-1990 1 31 0 0 0 7.136000
-1990 2 1 0 0 0 5.402000
-1990 2 2 0 0 0 4.535000
-1990 2 3 0 0 0 3.582000
-1990 2 4 0 0 0 2.730000
-1990 2 5 0 0 0 4.550000
-1990 2 6 0 0 0 2.556000
-1990 2 7 0 0 0 2.157000
-1990 2 8 0 0 0 5.923000
-1990 2 9 0 0 0 4.910000
-1990 2 10 0 0 0 2.828000
-1990 2 11 0 0 0 2.263000
-1990 2 12 0 0 0 2.398000
-1990 2 13 0 0 0 2.180000
-1990 2 14 0 0 0 2.633000
-1990 2 15 0 0 0 2.563000
-1990 2 16 0 0 0 2.032000
-1990 2 17 0 0 0 1.613000
-1990 2 18 0 0 0 3.662000
-1990 2 19 0 0 0 2.329000
-1990 2 20 0 0 0 6.635000
-1990 2 21 0 0 0 7.500000
-1990 2 22 0 0 0 3.195000
-1990 2 23 0 0 0 1.987000
-1990 2 24 0 0 0 1.600000
-1990 2 25 0 0 0 2.795000
-1990 2 26 0 0 0 3.957000
-1990 2 27 0 0 0 5.038000
-1990 2 28 0 0 0 3.106000
-1990 3 1 0 0 0 2.626000
-1990 3 2 0 0 0 2.191000
-1990 3 3 0 0 0 1.884000
-1990 3 4 0 0 0 2.408000
-1990 3 5 0 0 0 3.148000
-1990 3 6 0 0 0 2.185000
-1990 3 7 0 0 0 1.609000
-1990 3 8 0 0 0 1.420000
-1990 3 9 0 0 0 1.328000
-1990 3 10 0 0 0 1.323000
-1990 3 11 0 0 0 1.217000
-1990 3 12 0 0 0 1.086000
-1990 3 13 0 0 0 0.957000
-1990 3 14 0 0 0 1.127000
-1990 3 15 0 0 0 1.003000
-1990 3 16 0 0 0 0.903000
-1990 3 17 0 0 0 1.054000
-1990 3 18 0 0 0 0.814000
-1990 3 19 0 0 0 0.737000
-1990 3 20 0 0 0 0.691000
-1990 3 21 0 0 0 0.664000
-1990 3 22 0 0 0 0.628000
-1990 3 23 0 0 0 0.600000
-1990 3 24 0 0 0 0.576000
-1990 3 25 0 0 0 0.625000
-1990 3 26 0 0 0 0.610000
-1990 3 27 0 0 0 0.574000
-1990 3 28 0 0 0 0.544000
-1990 3 29 0 0 0 0.512000
-1990 3 30 0 0 0 0.497000
-1990 3 31 0 0 0 0.480000
-1990 4 1 0 0 0 0.466000
-1990 4 2 0 0 0 0.460000
-1990 4 3 0 0 0 0.468000
-1990 4 4 0 0 0 0.462000
-1990 4 5 0 0 0 0.444000
-1990 4 6 0 0 0 0.437000
-1990 4 7 0 0 0 0.558000
-1990 4 8 0 0 0 0.489000
-1990 4 9 0 0 0 0.440000
-1990 4 10 0 0 0 0.432000
-1990 4 11 0 0 0 0.423000
-1990 4 12 0 0 0 0.408000
-1990 4 13 0 0 0 0.408000
-1990 4 14 0 0 0 0.402000
-1990 4 15 0 0 0 0.398000
-1990 4 16 0 0 0 0.443000
-1990 4 17 0 0 0 0.466000
-1990 4 18 0 0 0 0.547000
-1990 4 19 0 0 0 0.601000
-1990 4 20 0 0 0 0.693000
-1990 4 21 0 0 0 0.540000
-1990 4 22 0 0 0 0.532000
-1990 4 23 0 0 0 0.513000
-1990 4 24 0 0 0 0.448000
-1990 4 25 0 0 0 0.408000
-1990 4 26 0 0 0 0.370000
-1990 4 27 0 0 0 0.357000
-1990 4 28 0 0 0 0.442000
-1990 4 29 0 0 0 0.370000
-1990 4 30 0 0 0 0.340000
-1990 5 1 0 0 0 0.325000
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-2004 1 10 0 0 0 4.165000
-2004 1 11 0 0 0 2.506000
-2004 1 12 0 0 0 4.222000
-2004 1 13 0 0 0 4.269000
-2004 1 14 0 0 0 5.266000
-2004 1 15 0 0 0 2.849000
-2004 1 16 0 0 0 3.759000
-2004 1 17 0 0 0 4.259000
-2004 1 18 0 0 0 2.838000
-2004 1 19 0 0 0 2.848000
-2004 1 20 0 0 0 3.654000
-2004 1 21 0 0 0 3.290000
-2004 1 22 0 0 0 2.232000
-2004 1 23 0 0 0 1.512000
-2004 1 24 0 0 0 1.387000
-2004 1 25 0 0 0 1.404000
-2004 1 26 0 0 0 1.124000
-2004 1 27 0 0 0 1.035000
-2004 1 28 0 0 0 1.012000
-2004 1 29 0 0 0 0.903000
-2004 1 30 0 0 0 0.875000
-2004 1 31 0 0 0 3.387000
-2004 2 1 0 0 0 8.980000
-2004 2 2 0 0 0 9.323000
-2004 2 3 0 0 0 6.892000
-2004 2 4 0 0 0 8.355000
-2004 2 5 0 0 0 7.702000
-2004 2 6 0 0 0 4.935000
-2004 2 7 0 0 0 2.838000
-2004 2 8 0 0 0 2.350000
-2004 2 9 0 0 0 2.203000
-2004 2 10 0 0 0 1.531000
-2004 2 11 0 0 0 2.089000
-2004 2 12 0 0 0 1.472000
-2004 2 13 0 0 0 1.186000
-2004 2 14 0 0 0 1.092000
-2004 2 15 0 0 0 1.075000
-2004 2 16 0 0 0 1.014000
-2004 2 17 0 0 0 1.000000
-2004 2 18 0 0 0 1.131000
-2004 2 19 0 0 0 1.024000
-2004 2 20 0 0 0 0.920000
-2004 2 21 0 0 0 0.853000
-2004 2 22 0 0 0 0.793000
-2004 2 23 0 0 0 0.740000
-2004 2 24 0 0 0 0.714000
-2004 2 25 0 0 0 0.748000
-2004 2 26 0 0 0 0.709000
-2004 2 27 0 0 0 0.683000
-2004 2 28 0 0 0 0.697000
-2004 2 29 0 0 0 0.710000
-2004 3 1 0 0 0 0.758000
-2004 3 2 0 0 0 0.766000
-2004 3 3 0 0 0 0.792000
-2004 3 4 0 0 0 1.099000
-2004 3 5 0 0 0 1.485000
-2004 3 6 0 0 0 1.040000
-2004 3 7 0 0 0 0.885000
-2004 3 8 0 0 0 0.852000
-2004 3 9 0 0 0 0.816000
-2004 3 10 0 0 0 0.718000
-2004 3 11 0 0 0 0.664000
-2004 3 12 0 0 0 0.638000
-2004 3 13 0 0 0 0.614000
-2004 3 14 0 0 0 0.599000
-2004 3 15 0 0 0 1.724000
-2004 3 16 0 0 0 1.386000
-2004 3 17 0 0 0 1.465000
-2004 3 18 0 0 0 0.831000
-2004 3 19 0 0 0 0.857000
-2004 3 20 0 0 0 2.372000
-2004 3 21 0 0 0 4.471000
-2004 3 22 0 0 0 3.271000
-2004 3 23 0 0 0 1.670000
-2004 3 24 0 0 0 1.127000
-2004 3 25 0 0 0 0.985000
-2004 3 26 0 0 0 0.944000
-2004 3 27 0 0 0 0.895000
-2004 3 28 0 0 0 0.809000
-2004 3 29 0 0 0 0.725000
-2004 3 30 0 0 0 0.661000
-2004 3 31 0 0 0 0.620000
-2004 4 1 0 0 0 0.590000
-2004 4 2 0 0 0 0.607000
-2004 4 3 0 0 0 0.874000
-2004 4 4 0 0 0 1.901000
-2004 4 5 0 0 0 1.933000
-2004 4 6 0 0 0 1.970000
-2004 4 7 0 0 0 1.187000
-2004 4 8 0 0 0 0.940000
-2004 4 9 0 0 0 0.801000
-2004 4 10 0 0 0 0.716000
-2004 4 11 0 0 0 0.699000
-2004 4 12 0 0 0 0.650000
-2004 4 13 0 0 0 0.600000
-2004 4 14 0 0 0 0.564000
-2004 4 15 0 0 0 0.539000
-2004 4 16 0 0 0 0.559000
-2004 4 17 0 0 0 0.838000
-2004 4 18 0 0 0 1.470000
-2004 4 19 0 0 0 4.930000
-2004 4 20 0 0 0 3.630000
-2004 4 21 0 0 0 2.114000
-2004 4 22 0 0 0 1.310000
-2004 4 23 0 0 0 1.264000
-2004 4 24 0 0 0 0.903000
-2004 4 25 0 0 0 0.760000
-2004 4 26 0 0 0 0.785000
-2004 4 27 0 0 0 0.776000
-2004 4 28 0 0 0 0.917000
-2004 4 29 0 0 0 1.020000
-2004 4 30 0 0 0 0.884000
-2004 5 1 0 0 0 0.732000
-2004 5 2 0 0 0 0.643000
-2004 5 3 0 0 0 0.581000
-2004 5 4 0 0 0 0.567000
-2004 5 5 0 0 0 1.168000
-2004 5 6 0 0 0 0.833000
-2004 5 7 0 0 0 0.870000
-2004 5 8 0 0 0 0.664000
-2004 5 9 0 0 0 0.857000
-2004 5 10 0 0 0 0.716000
-2004 5 11 0 0 0 0.631000
-2004 5 12 0 0 0 0.733000
-2004 5 13 0 0 0 0.577000
-2004 5 14 0 0 0 0.520000
-2004 5 15 0 0 0 0.478000
-2004 5 16 0 0 0 0.450000
-2004 5 17 0 0 0 0.427000
-2004 5 18 0 0 0 0.408000
-2004 5 19 0 0 0 0.389000
-2004 5 20 0 0 0 0.384000
-2004 5 21 0 0 0 0.378000
-2004 5 22 0 0 0 0.372000
-2004 5 23 0 0 0 0.360000
-2004 5 24 0 0 0 0.343000
-2004 5 25 0 0 0 0.336000
-2004 5 26 0 0 0 0.336000
-2004 5 27 0 0 0 0.333000
-2004 5 28 0 0 0 0.312000
-2004 5 29 0 0 0 0.312000
-2004 5 30 0 0 0 0.337000
-2004 5 31 0 0 0 0.361000
-2004 6 1 0 0 0 0.336000
-2004 6 2 0 0 0 0.336000
-2004 6 3 0 0 0 0.323000
-2004 6 4 0 0 0 0.348000
-2004 6 5 0 0 0 0.313000
-2004 6 6 0 0 0 0.308000
-2004 6 7 0 0 0 0.288000
-2004 6 8 0 0 0 0.288000
-2004 6 9 0 0 0 0.279000
-2004 6 10 0 0 0 0.264000
-2004 6 11 0 0 0 0.264000
-2004 6 12 0 0 0 0.264000
-2004 6 13 0 0 0 0.264000
-2004 6 14 0 0 0 0.254000
-2004 6 15 0 0 0 0.240000
-2004 6 16 0 0 0 0.240000
-2004 6 17 0 0 0 0.240000
-2004 6 18 0 0 0 0.257000
-2004 6 19 0 0 0 0.316000
-2004 6 20 0 0 0 0.310000
-2004 6 21 0 0 0 0.333000
-2004 6 22 0 0 0 0.319000
-2004 6 23 0 0 0 0.537000
-2004 6 24 0 0 0 1.964000
-2004 6 25 0 0 0 0.974000
-2004 6 26 0 0 0 0.705000
-2004 6 27 0 0 0 0.503000
-2004 6 28 0 0 0 0.584000
-2004 6 29 0 0 0 0.423000
-2004 6 30 0 0 0 0.423000
-2004 7 1 0 0 0 0.496000
-2004 7 2 0 0 0 0.554000
-2004 7 3 0 0 0 1.112000
-2004 7 4 0 0 0 1.390000
-2004 7 5 0 0 0 1.656000
-2004 7 6 0 0 0 1.270000
-2004 7 7 0 0 0 0.622000
-2004 7 8 0 0 0 0.475000
-2004 7 9 0 0 0 0.573000
-2004 7 10 0 0 0 0.676000
-2004 7 11 0 0 0 0.486000
-2004 7 12 0 0 0 0.410000
-2004 7 13 0 0 0 0.371000
-2004 7 14 0 0 0 0.340000
-2004 7 15 0 0 0 0.317000
-2004 7 16 0 0 0 0.312000
-2004 7 17 0 0 0 0.333000
-2004 7 18 0 0 0 0.455000
-2004 7 19 0 0 0 0.476000
-2004 7 20 0 0 0 0.357000
-2004 7 21 0 0 0 0.394000
-2004 7 22 0 0 0 0.365000
-2004 7 23 0 0 0 0.329000
-2004 7 24 0 0 0 0.289000
-2004 7 25 0 0 0 0.265000
-2004 7 26 0 0 0 0.264000
-2004 7 27 0 0 0 0.283000
-2004 7 28 0 0 0 0.265000
-2004 7 29 0 0 0 0.242000
-2004 7 30 0 0 0 0.240000
-2004 7 31 0 0 0 0.240000
-2004 8 1 0 0 0 0.223000
-2004 8 2 0 0 0 0.216000
-2004 8 3 0 0 0 0.223000
-2004 8 4 0 0 0 0.321000
-2004 8 5 0 0 0 0.902000
-2004 8 6 0 0 0 0.501000
-2004 8 7 0 0 0 0.416000
-2004 8 8 0 0 0 0.339000
-2004 8 9 0 0 0 0.326000
-2004 8 10 0 0 0 3.894000
-2004 8 11 0 0 0 9.167000
-2004 8 12 0 0 0 7.888000
-2004 8 13 0 0 0 6.307000
-2004 8 14 0 0 0 4.086000
-2004 8 15 0 0 0 1.949000
-2004 8 16 0 0 0 1.157000
-2004 8 17 0 0 0 0.949000
-2004 8 18 0 0 0 1.222000
-2004 8 19 0 0 0 2.211000
-2004 8 20 0 0 0 4.284000
-2004 8 21 0 0 0 5.765000
-2004 8 22 0 0 0 2.789000
-2004 8 23 0 0 0 1.473000
-2004 8 24 0 0 0 2.735000
-2004 8 25 0 0 0 3.831000
-2004 8 26 0 0 0 2.969000
-2004 8 27 0 0 0 1.730000
-2004 8 28 0 0 0 1.480000
-2004 8 29 0 0 0 1.016000
-2004 8 30 0 0 0 1.136000
-2004 8 31 0 0 0 1.158000
-2004 9 1 0 0 0 0.815000
-2004 9 2 0 0 0 0.706000
-2004 9 3 0 0 0 0.640000
-2004 9 4 0 0 0 0.588000
-2004 9 5 0 0 0 0.556000
-2004 9 6 0 0 0 0.532000
-2004 9 7 0 0 0 0.509000
-2004 9 8 0 0 0 0.490000
-2004 9 9 0 0 0 0.476000
-2004 9 10 0 0 0 0.456000
-2004 9 11 0 0 0 0.477000
-2004 9 12 0 0 0 0.511000
-2004 9 13 0 0 0 1.760000
-2004 9 14 0 0 0 2.777000
-2004 9 15 0 0 0 1.716000
-2004 9 16 0 0 0 1.194000
-2004 9 17 0 0 0 0.715000
-2004 9 18 0 0 0 0.757000
-2004 9 19 0 0 0 0.812000
-2004 9 20 0 0 0 1.009000
-2004 9 21 0 0 0 3.767000
-2004 9 22 0 0 0 3.178000
-2004 9 23 0 0 0 1.325000
-2004 9 24 0 0 0 0.932000
-2004 9 25 0 0 0 0.725000
-2004 9 26 0 0 0 0.604000
-2004 9 27 0 0 0 0.538000
-2004 9 28 0 0 0 0.521000
-2004 9 29 0 0 0 0.492000
-2004 9 30 0 0 0 0.527000
-2004 10 1 0 0 0 0.546000
-2004 10 2 0 0 0 0.587000
-2004 10 3 0 0 0 1.045000
-2004 10 4 0 0 0 1.757000
-2004 10 5 0 0 0 3.169000
-2004 10 6 0 0 0 2.543000
-2004 10 7 0 0 0 1.932000
-2004 10 8 0 0 0 0.943000
-2004 10 9 0 0 0 0.711000
-2004 10 10 0 0 0 0.605000
-2004 10 11 0 0 0 0.545000
-2004 10 12 0 0 0 0.500000
-2004 10 13 0 0 0 0.532000
-2004 10 14 0 0 0 1.022000
-2004 10 15 0 0 0 1.125000
-2004 10 16 0 0 0 0.898000
-2004 10 17 0 0 0 1.700000
-2004 10 18 0 0 0 2.159000
-2004 10 19 0 0 0 1.214000
-2004 10 20 0 0 0 1.147000
-2004 10 21 0 0 0 4.563000
-2004 10 22 0 0 0 7.167000
-2004 10 23 0 0 0 8.585000
-2004 10 24 0 0 0 9.112000
-2004 10 25 0 0 0 7.125000
-2004 10 26 0 0 0 5.458000
-2004 10 27 0 0 0 3.731000
-2004 10 28 0 0 0 2.601000
-2004 10 29 0 0 0 2.236000
-2004 10 30 0 0 0 2.648000
-2004 10 31 0 0 0 2.289000
-2004 11 1 0 0 0 1.556000
-2004 11 2 0 0 0 1.284000
-2004 11 3 0 0 0 1.171000
-2004 11 4 0 0 0 1.109000
-2004 11 5 0 0 0 1.122000
-2004 11 6 0 0 0 1.035000
-2004 11 7 0 0 0 1.053000
-2004 11 8 0 0 0 1.143000
-2004 11 9 0 0 0 1.044000
-2004 11 10 0 0 0 0.999000
-2004 11 11 0 0 0 0.936000
-2004 11 12 0 0 0 0.849000
-2004 11 13 0 0 0 0.798000
-2004 11 14 0 0 0 0.793000
-2004 11 15 0 0 0 0.728000
-2004 11 16 0 0 0 0.709000
-2004 11 17 0 0 0 0.787000
-2004 11 18 0 0 0 0.960000
-2004 11 19 0 0 0 0.832000
-2004 11 20 0 0 0 0.715000
-2004 11 21 0 0 0 0.636000
-2004 11 22 0 0 0 0.699000
-2004 11 23 0 0 0 1.451000
-2004 11 24 0 0 0 0.916000
-2004 11 25 0 0 0 0.770000
-2004 11 26 0 0 0 0.691000
-2004 11 27 0 0 0 0.745000
-2004 11 28 0 0 0 0.729000
-2004 11 29 0 0 0 0.977000
-2004 11 30 0 0 0 0.766000
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-2004 12 2 0 0 0 0.617000
-2004 12 3 0 0 0 0.580000
-2004 12 4 0 0 0 0.559000
-2004 12 5 0 0 0 0.552000
-2004 12 6 0 0 0 0.532000
-2004 12 7 0 0 0 0.528000
-2004 12 8 0 0 0 0.556000
-2004 12 9 0 0 0 0.532000
-2004 12 10 0 0 0 0.508000
-2004 12 11 0 0 0 0.486000
-2004 12 12 0 0 0 0.480000
-2004 12 13 0 0 0 0.480000
-2004 12 14 0 0 0 0.476000
-2004 12 15 0 0 0 0.892000
-2004 12 16 0 0 0 1.790000
-2004 12 17 0 0 0 1.636000
-2004 12 18 0 0 0 1.673000
-2004 12 19 0 0 0 0.981000
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-2004 12 21 0 0 0 0.617000
-2004 12 22 0 0 0 0.966000
-2004 12 23 0 0 0 2.395000
-2004 12 24 0 0 0 3.338000
-2004 12 25 0 0 0 3.237000
-2004 12 26 0 0 0 1.635000
-2004 12 27 0 0 0 0.947000
-2004 12 28 0 0 0 2.376000
-2004 12 29 0 0 0 2.917000
-2004 12 30 0 0 0 1.239000
-2004 12 31 0 0 0 2.100000
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-2005 1 2 0 0 0 2.589000
-2005 1 3 0 0 0 2.020000
-2005 1 4 0 0 0 1.490000
-2005 1 5 0 0 0 2.324000
-2005 1 6 0 0 0 2.437000
-2005 1 7 0 0 0 2.328000
-2005 1 8 0 0 0 7.873000
-2005 1 9 0 0 0 9.762000
-2005 1 10 0 0 0 7.614000
-2005 1 11 0 0 0 6.265000
-2005 1 12 0 0 0 4.243000
-2005 1 13 0 0 0 3.387000
-2005 1 14 0 0 0 2.562000
-2005 1 15 0 0 0 1.736000
-2005 1 16 0 0 0 1.611000
-2005 1 17 0 0 0 1.537000
-2005 1 18 0 0 0 3.044000
-2005 1 19 0 0 0 2.154000
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-2005 1 23 0 0 0 1.116000
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-2005 1 26 0 0 0 1.411000
-2005 1 27 0 0 0 1.552000
-2005 1 28 0 0 0 1.600000
-2005 1 29 0 0 0 1.480000
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-2005 1 31 0 0 0 1.061000
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-2005 2 2 0 0 0 0.892000
-2005 2 3 0 0 0 0.845000
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-2005 2 5 0 0 0 0.779000
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-2005 2 7 0 0 0 0.777000
-2005 2 8 0 0 0 0.746000
-2005 2 9 0 0 0 0.695000
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-2005 2 11 0 0 0 1.775000
-2005 2 12 0 0 0 1.219000
-2005 2 13 0 0 0 2.369000
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-2005 2 15 0 0 0 0.906000
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-2005 2 26 0 0 0 3.857000
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-2005 3 9 0 0 0 1.709000
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-2005 3 15 0 0 0 1.677000
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-2005 3 17 0 0 0 3.658000
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-2005 3 28 0 0 0 2.570000
-2005 3 29 0 0 0 3.817000
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-2005 4 5 0 0 0 0.742000
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-2005 4 16 0 0 0 4.331000
-2005 4 17 0 0 0 6.099000
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-2005 9 4 0 0 0 0.268000
-2005 9 5 0 0 0 0.245000
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-2005 9 7 0 0 0 0.231000
-2005 9 8 0 0 0 0.217000
-2005 9 9 0 0 0 0.272000
-2005 9 10 0 0 0 2.071000
-2005 9 11 0 0 0 1.145000
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-2005 9 13 0 0 0 0.413000
-2005 9 14 0 0 0 0.342000
-2005 9 15 0 0 0 0.433000
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-2005 9 17 0 0 0 0.883000
-2005 9 18 0 0 0 0.518000
-2005 9 19 0 0 0 0.409000
-2005 9 20 0 0 0 0.356000
-2005 9 21 0 0 0 0.321000
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-2014 4 5 0 0 0 1.139000
-2014 4 6 0 0 0 1.041000
-2014 4 7 0 0 0 1.894000
-2014 4 8 0 0 0 2.099000
-2014 4 9 0 0 0 1.431000
-2014 4 10 0 0 0 0.976000
-2014 4 11 0 0 0 0.838000
-2014 4 12 0 0 0 0.754000
-2014 4 13 0 0 0 0.707000
-2014 4 14 0 0 0 0.664000
-2014 4 15 0 0 0 0.619000
-2014 4 16 0 0 0 0.589000
-2014 4 17 0 0 0 0.573000
-2014 4 18 0 0 0 0.550000
-2014 4 19 0 0 0 0.528000
-2014 4 20 0 0 0 0.518000
-2014 4 21 0 0 0 0.504000
-2014 4 22 0 0 0 0.504000
-2014 4 23 0 0 0 0.540000
-2014 4 24 0 0 0 0.582000
-2014 4 25 0 0 0 0.625000
-2014 4 26 0 0 0 0.706000
-2014 4 27 0 0 0 0.861000
-2014 4 28 0 0 0 0.694000
-2014 4 29 0 0 0 0.607000
-2014 4 30 0 0 0 0.556000
-2014 5 1 0 0 0 0.611000
-2014 5 2 0 0 0 1.076000
-2014 5 3 0 0 0 0.790000
-2014 5 4 0 0 0 0.615000
-2014 5 5 0 0 0 0.561000
-2014 5 6 0 0 0 0.552000
-2014 5 7 0 0 0 0.530000
-2014 5 8 0 0 0 0.642000
-2014 5 9 0 0 0 1.026000
-2014 5 10 0 0 0 1.227000
-2014 5 11 0 0 0 3.377000
-2014 5 12 0 0 0 3.260000
-2014 5 13 0 0 0 3.337000
-2014 5 14 0 0 0 2.790000
-2014 5 15 0 0 0 1.289000
-2014 5 16 0 0 0 0.906000
-2014 5 17 0 0 0 0.772000
-2014 5 18 0 0 0 0.674000
-2014 5 19 0 0 0 0.612000
-2014 5 20 0 0 0 0.567000
-2014 5 21 0 0 0 0.532000
-2014 5 22 0 0 0 0.570000
-2014 5 23 0 0 0 0.789000
-2014 5 24 0 0 0 1.702000
-2014 5 25 0 0 0 3.176000
-2014 5 26 0 0 0 3.215000
-2014 5 27 0 0 0 1.499000
-2014 5 28 0 0 0 1.118000
-2014 5 29 0 0 0 3.465000
-2014 5 30 0 0 0 3.774000
-2014 5 31 0 0 0 1.825000
-2014 6 1 0 0 0 1.078000
-2014 6 2 0 0 0 0.889000
-2014 6 3 0 0 0 0.807000
-2014 6 4 0 0 0 0.931000
-2014 6 5 0 0 0 2.090000
-2014 6 6 0 0 0 1.439000
-2014 6 7 0 0 0 0.949000
-2014 6 8 0 0 0 1.294000
-2014 6 9 0 0 0 1.303000
-2014 6 10 0 0 0 0.946000
-2014 6 11 0 0 0 0.956000
-2014 6 12 0 0 0 0.825000
-2014 6 13 0 0 0 0.671000
-2014 6 14 0 0 0 0.617000
-2014 6 15 0 0 0 0.584000
-2014 6 16 0 0 0 0.559000
-2014 6 17 0 0 0 0.537000
-2014 6 18 0 0 0 0.508000
-2014 6 19 0 0 0 0.481000
-2014 6 20 0 0 0 0.462000
-2014 6 21 0 0 0 0.451000
-2014 6 22 0 0 0 0.432000
-2014 6 23 0 0 0 0.422000
-2014 6 24 0 0 0 0.408000
-2014 6 25 0 0 0 0.405000
-2014 6 26 0 0 0 0.384000
-2014 6 27 0 0 0 0.384000
-2014 6 28 0 0 0 0.384000
-2014 6 29 0 0 0 0.389000
-2014 6 30 0 0 0 0.385000
-2014 7 1 0 0 0 0.360000
-2014 7 2 0 0 0 0.360000
-2014 7 3 0 0 0 0.348000
-2014 7 4 0 0 0 0.343000
-2014 7 5 0 0 0 0.441000
-2014 7 6 0 0 0 0.504000
-2014 7 7 0 0 0 0.448000
-2014 7 8 0 0 0 0.388000
-2014 7 9 0 0 0 0.374000
-2014 7 10 0 0 0 0.404000
-2014 7 11 0 0 0 0.361000
-2014 7 12 0 0 0 0.336000
-2014 7 13 0 0 0 0.340000
-2014 7 14 0 0 0 0.346000
-2014 7 15 0 0 0 0.335000
-2014 7 16 0 0 0 0.358000
-2014 7 17 0 0 0 0.363000
-2014 7 18 0 0 0 0.336000
-2014 7 19 0 0 0 0.353000
-2014 7 20 0 0 0 0.390000
-2014 7 21 0 0 0 0.422000
-2014 7 22 0 0 0 0.366000
-2014 7 23 0 0 0 0.326000
-2014 7 24 0 0 0 0.307000
-2014 7 25 0 0 0 0.288000
-2014 7 26 0 0 0 0.288000
-2014 7 27 0 0 0 0.291000
-2014 7 28 0 0 0 0.294000
-2014 7 29 0 0 0 0.288000
-2014 7 30 0 0 0 0.266000
-2014 7 31 0 0 0 0.264000
-2014 8 1 0 0 0 0.269000
-2014 8 2 0 0 0 0.352000
-2014 8 3 0 0 0 0.525000
-2014 8 4 0 0 0 0.637000
-2014 8 5 0 0 0 0.689000
-2014 8 6 0 0 0 0.466000
-2014 8 7 0 0 0 0.692000
-2014 8 8 0 0 0 0.542000
-2014 8 9 0 0 0 0.606000
-2014 8 10 0 0 0 0.590000
-2014 8 11 0 0 0 0.784000
-2014 8 12 0 0 0 1.085000
-2014 8 13 0 0 0 0.639000
-2014 8 14 0 0 0 0.552000
-2014 8 15 0 0 0 0.703000
-2014 8 16 0 0 0 0.762000
-2014 8 17 0 0 0 0.511000
-2014 8 18 0 0 0 0.455000
-2014 8 19 0 0 0 0.450000
-2014 8 20 0 0 0 0.399000
-2014 8 21 0 0 0 0.355000
-2014 8 22 0 0 0 0.336000
-2014 8 23 0 0 0 0.342000
-2014 8 24 0 0 0 0.345000
-2014 8 25 0 0 0 0.336000
-2014 8 26 0 0 0 0.322000
-2014 8 27 0 0 0 0.308000
-2014 8 28 0 0 0 0.304000
-2014 8 29 0 0 0 0.288000
-2014 8 30 0 0 0 0.369000
-2014 8 31 0 0 0 0.467000
-2014 9 1 0 0 0 0.361000
-2014 9 2 0 0 0 0.321000
-2014 9 3 0 0 0 0.296000
-2014 9 4 0 0 0 0.288000
-2014 9 5 0 0 0 0.285000
-2014 9 6 0 0 0 0.291000
-2014 9 7 0 0 0 0.312000
-2014 9 8 0 0 0 0.312000
-2014 9 9 0 0 0 0.288000
-2014 9 10 0 0 0 0.281000
-2014 9 11 0 0 0 0.264000
-2014 9 12 0 0 0 0.264000
-2014 9 13 0 0 0 0.264000
-2014 9 14 0 0 0 0.260000
-2014 9 15 0 0 0 0.255000
-2014 9 16 0 0 0 0.264000
-2014 9 17 0 0 0 0.256000
-2014 9 18 0 0 0 0.240000
-2014 9 19 0 0 0 0.240000
-2014 9 20 0 0 0 0.240000
-2014 9 21 0 0 0 0.240000
-2014 9 22 0 0 0 0.240000
-2014 9 23 0 0 0 0.240000
-2014 9 24 0 0 0 0.240000
-2014 9 25 0 0 0 0.240000
-2014 9 26 0 0 0 0.240000
-2014 9 27 0 0 0 0.218000
-2014 9 28 0 0 0 0.216000
-2014 9 29 0 0 0 0.217000
-2014 9 30 0 0 0 0.238000
-2014 10 1 0 0 0 0.216000
-2014 10 2 0 0 0 0.216000
-2014 10 3 0 0 0 0.216000
-2014 10 4 0 0 0 0.216000
-2014 10 5 0 0 0 0.216000
-2014 10 6 0 0 0 0.252000
-2014 10 7 0 0 0 0.653000
-2014 10 8 0 0 0 0.625000
-2014 10 9 0 0 0 0.553000
-2014 10 10 0 0 0 0.716000
-2014 10 11 0 0 0 0.924000
-2014 10 12 0 0 0 0.765000
-2014 10 13 0 0 0 0.680000
-2014 10 14 0 0 0 0.678000
-2014 10 15 0 0 0 0.862000
-2014 10 16 0 0 0 0.881000
-2014 10 17 0 0 0 1.595000
-2014 10 18 0 0 0 1.127000
-2014 10 19 0 0 0 1.216000
-2014 10 20 0 0 0 1.351000
-2014 10 21 0 0 0 1.314000
-2014 10 22 0 0 0 1.815000
-2014 10 23 0 0 0 0.898000
-2014 10 24 0 0 0 0.679000
-2014 10 25 0 0 0 0.887000
-2014 10 26 0 0 0 0.664000
-2014 10 27 0 0 0 0.644000
-2014 10 28 0 0 0 1.309000
-2014 10 29 0 0 0 1.823000
-2014 10 30 0 0 0 1.416000
-2014 10 31 0 0 0 1.180000
-2014 11 1 0 0 0 0.963000
-2014 11 2 0 0 0 0.782000
-2014 11 3 0 0 0 1.340000
-2014 11 4 0 0 0 0.956000
-2014 11 5 0 0 0 0.840000
-2014 11 6 0 0 0 0.688000
-2014 11 7 0 0 0 NaN
-2014 11 8 0 0 0 1.224000
-2014 11 9 0 0 0 1.273000
-2014 11 10 0 0 0 0.775000
-2014 11 11 0 0 0 0.491077
-2014 11 12 0 0 0 1.548000
-2014 11 13 0 0 0 2.676000
-2014 11 14 0 0 0 1.389000
-2014 11 15 0 0 0 4.402000
-2014 11 16 0 0 0 2.867000
-2014 11 17 0 0 0 1.552000
-2014 11 18 0 0 0 3.577000
-2014 11 19 0 0 0 2.037000
-2014 11 20 0 0 0 1.045000
-2014 11 21 0 0 0 0.825000
-2014 11 22 0 0 0 1.468000
-2014 11 23 0 0 0 3.826000
-2014 11 24 0 0 0 1.936000
-2014 11 25 0 0 0 1.038000
-2014 11 26 0 0 0 0.829000
-2014 11 27 0 0 0 1.014000
-2014 11 28 0 0 0 1.215000
-2014 11 29 0 0 0 1.108000
-2014 11 30 0 0 0 1.050000
-2014 12 1 0 0 0 0.905000
-2014 12 2 0 0 0 0.797000
-2014 12 3 0 0 0 0.750000
-2014 12 4 0 0 0 0.898667
-2014 12 5 0 0 0 0.856000
-2014 12 6 0 0 0 0.791000
-2014 12 7 0 0 0 0.763000
-2014 12 8 0 0 0 1.466000
-2014 12 9 0 0 0 0.888000
-2014 12 10 0 0 0 2.303000
-2014 12 11 0 0 0 3.324000
-2014 12 12 0 0 0 2.344000
-2014 12 13 0 0 0 1.318000
-2014 12 14 0 0 0 0.979000
-2014 12 15 0 0 0 3.572000
-2014 12 16 0 0 0 2.116000
-2014 12 17 0 0 0 1.373000
-2014 12 18 0 0 0 2.004000
-2014 12 19 0 0 0 3.478000
-2014 12 20 0 0 0 2.638000
-2014 12 21 0 0 0 1.940000
-2014 12 22 0 0 0 2.031000
-2014 12 23 0 0 0 6.076000
-2014 12 24 0 0 0 6.408000
-2014 12 25 0 0 0 3.720000
-2014 12 26 0 0 0 1.518000
-2014 12 27 0 0 0 1.192000
-2014 12 28 0 0 0 1.074000
-2014 12 29 0 0 0 0.906000
-2014 12 30 0 0 0 0.799000
-2014 12 31 0 0 0 0.754000
diff --git a/hydromodel/example/daily_rainfall_27071.txt b/hydromodel/example/daily_rainfall_27071.txt
deleted file mode 100644
index 8091120..0000000
--- a/hydromodel/example/daily_rainfall_27071.txt
+++ /dev/null
@@ -1,9131 +0,0 @@
-1990 1 1 0 0 0 6.017000
-1990 1 2 0 0 0 4.929000
-1990 1 3 0 0 0 0.380000
-1990 1 4 0 0 0 0.073000
-1990 1 5 0 0 0 0.534000
-1990 1 6 0 0 0 4.487000
-1990 1 7 0 0 0 3.600000
-1990 1 8 0 0 0 0.345000
-1990 1 9 0 0 0 0.454000
-1990 1 10 0 0 0 2.990000
-1990 1 11 0 0 0 3.307000
-1990 1 12 0 0 0 0.688000
-1990 1 13 0 0 0 0.027000
-1990 1 14 0 0 0 0.323000
-1990 1 15 0 0 0 1.320000
-1990 1 16 0 0 0 3.852000
-1990 1 17 0 0 0 1.494000
-1990 1 18 0 0 0 0.601000
-1990 1 19 0 0 0 6.466000
-1990 1 20 0 0 0 0.263000
-1990 1 21 0 0 0 0.861000
-1990 1 22 0 0 0 2.448000
-1990 1 23 0 0 0 12.039000
-1990 1 24 0 0 0 3.589000
-1990 1 25 0 0 0 14.421000
-1990 1 26 0 0 0 3.048000
-1990 1 27 0 0 0 18.185000
-1990 1 28 0 0 0 12.320000
-1990 1 29 0 0 0 8.048000
-1990 1 30 0 0 0 4.279000
-1990 1 31 0 0 0 6.610000
-1990 2 1 0 0 0 7.564000
-1990 2 2 0 0 0 4.336000
-1990 2 3 0 0 0 0.297000
-1990 2 4 0 0 0 7.134000
-1990 2 5 0 0 0 0.021000
-1990 2 6 0 0 0 0.066000
-1990 2 7 0 0 0 24.037000
-1990 2 8 0 0 0 3.587000
-1990 2 9 0 0 0 1.143000
-1990 2 10 0 0 0 3.123000
-1990 2 11 0 0 0 5.208000
-1990 2 12 0 0 0 4.105000
-1990 2 13 0 0 0 5.910000
-1990 2 14 0 0 0 2.599000
-1990 2 15 0 0 0 1.390000
-1990 2 16 0 0 0 0.152000
-1990 2 17 0 0 0 3.199000
-1990 2 18 0 0 0 0.579000
-1990 2 19 0 0 0 16.399000
-1990 2 20 0 0 0 3.844000
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-1990 2 22 0 0 0 0.003000
-1990 2 23 0 0 0 0.504000
-1990 2 24 0 0 0 5.090000
-1990 2 25 0 0 0 6.077000
-1990 2 26 0 0 0 14.087000
-1990 2 27 0 0 0 7.309000
-1990 2 28 0 0 0 3.936000
-1990 3 1 0 0 0 3.583000
-1990 3 2 0 0 0 0.299000
-1990 3 3 0 0 0 0.191000
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-1990 3 5 0 0 0 0.092000
-1990 3 6 0 0 0 0.250000
-1990 3 7 0 0 0 0.060000
-1990 3 8 0 0 0 1.591000
-1990 3 9 0 0 0 0.867000
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-1990 3 13 0 0 0 5.436000
-1990 3 14 0 0 0 0.016000
-1990 3 15 0 0 0 0.636000
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-1990 3 20 0 0 0 0.102000
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-1990 3 22 0 0 0 0.037000
-1990 3 23 0 0 0 0.131000
-1990 3 24 0 0 0 2.248000
-1990 3 25 0 0 0 0.466000
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-1990 3 27 0 0 0 0.049000
-1990 3 28 0 0 0 0.010000
-1990 3 29 0 0 0 0.799000
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-1990 4 1 0 0 0 0.002000
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-1990 4 5 0 0 0 1.014000
-1990 4 6 0 0 0 0.821000
-1990 4 7 0 0 0 0.350000
-1990 4 8 0 0 0 0.000000
-1990 4 9 0 0 0 0.009000
-1990 4 10 0 0 0 0.149000
-1990 4 11 0 0 0 0.007000
-1990 4 12 0 0 0 0.307000
-1990 4 13 0 0 0 0.302000
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-1990 4 16 0 0 0 1.476000
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-1990 6 1 0 0 0 0.142000
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-1990 6 3 0 0 0 0.594000
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-1990 6 5 0 0 0 2.344000
-1990 6 6 0 0 0 4.364000
-1990 6 7 0 0 0 0.191000
-1990 6 8 0 0 0 4.216000
-1990 6 9 0 0 0 4.977000
-1990 6 10 0 0 0 0.026000
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-1990 6 12 0 0 0 1.092000
-1990 6 13 0 0 0 0.000000
-1990 6 14 0 0 0 0.000000
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-1990 6 22 0 0 0 24.967000
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-1990 7 1 0 0 0 3.191000
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-1990 7 5 0 0 0 2.452000
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-1990 7 7 0 0 0 7.426000
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-1990 8 14 0 0 0 0.838000
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-1990 12 7 0 0 0 27.638000
-1990 12 8 0 0 0 32.307000
-1990 12 9 0 0 0 4.643000
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-1990 12 12 0 0 0 0.054000
-1990 12 13 0 0 0 0.298000
-1990 12 14 0 0 0 0.000000
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diff --git a/hydromodel/example/division_flow.csv b/hydromodel/example/division_flow.csv
deleted file mode 100644
index 23dddb9..0000000
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diff --git a/hydromodel/example/division_rain.csv b/hydromodel/example/division_rain.csv
deleted file mode 100644
index 84e0f7d..0000000
--- a/hydromodel/example/division_rain.csv
+++ /dev/null
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\ No newline at end of file
diff --git a/hydromodel/hydromodel.py b/hydromodel/hydromodel.py
deleted file mode 100644
index dd0b80e..0000000
--- a/hydromodel/hydromodel.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Main module."""
diff --git a/hydromodel/models/configuration.py b/hydromodel/models/configuration.py
deleted file mode 100644
index 3e5b8d2..0000000
--- a/hydromodel/models/configuration.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import yaml
-import numpy as np
-from hydromodel.models.xaj import xaj
-
-
-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(
-        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/hydromodel/models/xaj_bmi.py b/hydromodel/models/xaj_bmi.py
index e94f620..2ee3fa9 100644
--- a/hydromodel/models/xaj_bmi.py
+++ b/hydromodel/models/xaj_bmi.py
@@ -2,134 +2,89 @@
 from bmipy import Bmi
 import numpy as np
 
-from hydromodel.models import configuration
-import datetime
-import logging
+from xaj.xajmodel import xaj_route, xaj_runoff
+from xaj.constant_unit import convert_unit,unit
 
-from hydromodel.models.xaj import xaj_route, xaj_runoff
+from grpc4bmi.constants import GRPC_MAX_MESSAGE_LENGTH
+import datetime 
+import pandas as pd
+import logging
+from xaj.configuration import configuration
 
 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"}
 
-    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 Bmi model that is ready for initialization."""
+        """Create a BmiHeat model that is ready for initialization."""
         self.time_step = 0
 
-    def initialize(self, config_file, params, p_and_e):
+    def initialize(self, config_file):
         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
-            )
-
+            forcing_data = pd.read_csv(config['forcing_file'])
+            p_and_e_df, p_and_e = configuration.extract_forcing(forcing_data)
+            p_and_e_warmup = p_and_e[0:config['warmup_length'],:,:]
+            params=np.tile([0.5], (1, 15))
+            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'])
+        
+            self._start_time_str,self._end_time_str, self._time_units = configuration.get_time_config(config) 
+            
             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.warmup_length = config['warmup_length']
+            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]
+            self.basin_area = config['basin_area']
 
-        except Exception:
+        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,
+        # p_and_e_sim = self.p_and_e[self.warmup_length+1:self.time_step+self.warmup_length+1]
+        p_and_e_sim = self.p_and_e[1:self.time_step+1]
+        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,
+                )
+        if self.time_step+self.warmup_length+1 >= self.p_and_e.shape[0]:
+            q_sim,es = xaj_route(p_and_e_sim,
+                            params_route=self.params,
+                            model_name = "xaj",
+                            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.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[:, :, :]
+            self.q_sim = q_sim
+            self.es =  es
 
     def update_until(self, time):
         while self.get_current_time() + 0.001 < time:
@@ -150,7 +105,7 @@ def get_output_item_count(self) -> int:
 
     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
 
@@ -158,7 +113,7 @@ 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]
@@ -171,28 +126,20 @@ def get_var_nbytes(self, name: str) -> int:
 
     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_start_time(self):
+        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":
-            time_step = datetime.timedelta(days=self.time_step)
-        return self.start_Time(self._start_time_str) + 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_end_time(self):
+        return self.end_Time(self._end_time_str) 
 
     def get_time_units(self) -> str:
         return self._time_units
@@ -200,29 +147,35 @@ def get_time_units(self) -> str:
     def get_time_step(self) -> float:
         return 1
 
-    def get_value(self, name: str) -> None:
+    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":
+        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
     ) -> None:
         val = self.get_value_ptr(name)
         val.flat[inds] = src
-
+        
     # Grid information
     def get_grid_rank(self, grid: int) -> int:
         raise NotImplementedError()
@@ -275,45 +228,47 @@ 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(" ")
+            if " " in _start_time_str:    
+                date, time = _start_time_str.split(" ")    
             else:
-                date = _start_time_str
-                time = None
+                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),
+                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 as e:
-            raise ValueError("Invalid start date format!") from e
+                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(" ")
+            if " " in _end_time_str:    
+                date, time = _end_time_str.split(" ")    
             else:
-                date = _end_time_str
-                time = None
+                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 as e:
-            raise ValueError("Invalid start date format!") from e
+                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/hydromodel/models/xaj_river.py b/hydromodel/models/xaj_river.py
deleted file mode 100644
index 4977b3a..0000000
--- a/hydromodel/models/xaj_river.py
+++ /dev/null
@@ -1,223 +0,0 @@
-"""TODO: river routing process"""
-import numpy as np
-
-
-def network_route(runoffs, route_params):
-    """
-    河网汇流计算，新安江模型里一般采用线性水库或滞后演算法。这里使用滞后演算法
-
-    Parameters
-    ----------
-    runoffs
-        坡面汇流计算结果，数组
-    route_params
-        模型参数
-
-    Returns
-    -------
-    np.array
-        汇流计算结果——流量过程线
-    """
-    # 取整后面才能计算
-    t = int(route_params["L"])
-    cr = route_params["CR"]
-    # 初始化
-    qr = runoffs
-    q = np.zeros(runoffs.size)
-    if t <= 0:
-        for i in range(runoffs.size):
-            if i == 0:
-                q[0] = (1 - cr) * qr[0]
-            else:
-                q[i] = cr * q[i - 1] + (1 - cr) * qr[i]
-    else:
-        for i in range(runoffs.size):
-            if i == 0:
-                q[0] = 0
-            elif i < t:
-                q[i] = cr * q[i - 1]
-            else:
-                q[i] = cr * q[i - 1] + (1 - cr) * qr[i - t]
-    return q
-
-
-def river_route(config, route_params, qf):
-    """
-    河道汇流计算，新安江模型一般采用马斯京根法
-
-    Parameters
-    ----------
-    config
-        计算设置条件
-    route_params
-        模型参数
-    qf
-        河网汇流计算结果，数组
-
-    Returns
-    -------
-    np.array
-        汇流计算结果——流量过程线
-    """
-    ke = route_params["KE"]
-    xe = route_params["XE"]
-    time_interval = config["time_interval/h"]
-
-    q = np.zeros(qf.size)
-
-    c0 = (0.5 * time_interval - ke * xe) / (0.5 * time_interval + ke - ke * xe)
-    c1 = (0.5 * time_interval + ke * xe) / (0.5 * time_interval + ke - ke * xe)
-    c2 = 1 - c0 - c1
-
-    q[0] = qf[0]
-    if c0 >= 0 and c2 >= 0:
-        for i in range(1, q.size):
-            q[i] = c0 * qf[i] + c1 * qf[i - 1] + c2 * q[i - 1]
-    else:
-        # 当马斯京根不适用时，暂未处理
-        q = qf
-    return q
-
-
-# -------------------------------------------Unfinished auxiliary function---------------------------------------------
-def uh_recognise(runoffs, flood_data):
-    """
-    时段单位线的识别，先以最小二乘法为主。
-
-    Parameters
-    ----------
-    runoffs
-        各场次洪水对应的各时段净雨，np.array组成的list
-    flood_data
-        多组出口断面流量过程线，np.array组成的list
-
-    Returns
-    -------
-    np.array
-        时段单位线，每个数据的单位为m^3/s
-    """
-    # 最小二乘法计算针对每场次洪水得到一条单位线，多场次洪水，按照书上的意思，可以取平均。如果曲线之间差异较大，需要进行分类，目前先求平均。
-    uh_s = []
-    uh_sum = np.array([])
-    for i in range(len(runoffs)):
-        ht = []
-        # q表示实际径流
-        q = flood_data[i]
-        l = len(flood_data[i])
-        m = len(runoffs[i])
-        n = l - m + 1
-        for j in range(n):
-            # numpy默认为行向量
-            h_row = np.zeros(l)
-            for k in range(j, j + m):
-                h_row[k] = runoffs[i][k - j]
-            ht.append(h_row)
-        h = np.transpose(ht)
-        ht_h = np.dot(ht, h)
-        ht_q = np.dot(ht, q)
-        # 求得一条单位线
-        uh_temp = np.linalg.solve(ht_h, ht_q)
-        # 每场次洪水均有一条单位线
-        uh_s.append(uh_temp)
-        # 求和，因为单位线长度可能不一致，所以需要进行补0对齐
-        if i == 0:
-            uh_sum = uh_temp
-        else:
-            # 当维度不同的向量要对齐时，需在不足处补0
-            length_zero = max(uh_sum.size, uh_temp.size) - min(
-                uh_sum.size, uh_temp.size
-            )
-            zeros_need = np.zeros(length_zero)
-            if uh_sum.size > uh_temp.size:
-                arr_new = np.hstack([uh_temp, zeros_need])
-                uh_sum = uh_sum + arr_new
-            else:
-                arr_new = np.hstack([uh_sum, zeros_need])
-                uh_sum = uh_temp + arr_new
-    # 广播运算
-    uh = uh_sum / len(runoffs)
-    return uh
-
-
-def split_flow(floods_data, auto=None):
-    """
-    分割流量过程，有时候，会手动进行分割，所以可能不需要调用次函数
-
-    Parameters
-    ----------
-    floods_data
-        多组观测的出口断面流量过程线，np.array组成的list
-    auto
-        是程序自动计算退水曲线还是已经手动计算好了
-
-    Returns
-    -------
-    list[np.array]
-        去掉前期洪水尚未退完的部分水量和非本次降雨补给的流量过程线
-    """
-    # TODO: 求退水曲线
-    # 首先取出各个洪水过程退水段
-    floods = []
-    for flood_data in floods_data:
-        temp = flood_data.loc[:, "flood_quant"]
-        floods.append(np.array(temp))
-    # 然后取出退水段各点，组成向量
-
-    # 最小二乘法计算
-
-    # 利用计算好的退水曲线分割观测的流量过程线
-
-    if auto is None:
-        return floods
-    return floods
-
-
-def divide_source(floods, auto=None):
-    """
-    从观测的场次洪水径流中分割地表地下径流
-
-    Parameters
-    ----------
-    floods
-        多组观测的已经去掉前期洪水尚未退完的部分水量和非本次降雨补给的出口断面流量过程线，np.array组成的list
-    auto
-        是程序自动计算分割流量还是已经手动计算好了
-
-    Returns
-    -------
-    list[np.array]
-        rs_s,rg_s: 地表径流流量过程线和地下径流过程线
-    """
-    # TODO: 分割径流
-    if auto is None:
-        return floods
-    return floods
-
-
-def iuh_recognise(
-    runoffs, flood_data, linear_reservoir=None, linear_canal=None, isochrone=None
-):
-    """
-    瞬时单位线的识别，目前以计算Nash单位线为主
-
-    Parameters
-    ----------
-    runoffs
-        多组各时段净雨（地表），矩阵表示
-    flood_data
-        多组出口断面流量过程线，矩阵表示
-    linear_reservoir
-
-    linear_canal
-        线性渠个数
-    isochrone
-        等流时线个数
-
-    Returns
-    -------
-    tuple[float, float]
-        n,k:nash单位线两参数
-    """
-    # TODO: 这个函数没想好怎么写，能很好地把串并联结构和三个基础模型搭配在一起，目前以n个线性水库串联为主
-
-    return
diff --git a/hydromodel/data/__init__.py b/hydromodel/trainers/__init__.py
similarity index 100%
rename from hydromodel/data/__init__.py
rename to hydromodel/trainers/__init__.py
diff --git a/hydromodel/calibrate/calibrate_ga.py b/hydromodel/trainers/calibrate_ga.py
similarity index 99%
rename from hydromodel/calibrate/calibrate_ga.py
rename to hydromodel/trainers/calibrate_ga.py
index b9b8179..4b1faed 100644
--- a/hydromodel/calibrate/calibrate_ga.py
+++ b/hydromodel/trainers/calibrate_ga.py
@@ -25,7 +25,7 @@
 import definitions
 from hydromodel.models.model_config import MODEL_PARAM_DICT
 from hydromodel.utils import units
-from hydromodel.utils.plots import plot_sim_and_obs, plot_train_iteration
+from hydromodel.trainers.plots import plot_sim_and_obs, plot_train_iteration
 from hydromodel.models.gr4j import gr4j
 from hydromodel.models.hymod import hymod
 from hydromodel.models.xaj import xaj
diff --git a/hydromodel/calibrate/calibrate_ga_xaj_bmi.py b/hydromodel/trainers/calibrate_ga_xaj_bmi.py
similarity index 99%
rename from hydromodel/calibrate/calibrate_ga_xaj_bmi.py
rename to hydromodel/trainers/calibrate_ga_xaj_bmi.py
index ab04e06..bef49d6 100644
--- a/hydromodel/calibrate/calibrate_ga_xaj_bmi.py
+++ b/hydromodel/trainers/calibrate_ga_xaj_bmi.py
@@ -17,7 +17,7 @@
 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.plots import plot_sim_and_obs, plot_train_iteration
+from hydromodel.trainers.plots import plot_sim_and_obs, plot_train_iteration
 from hydromodel.models.xaj_bmi import xajBmi
 from hydromodel.utils import units
 
diff --git a/hydromodel/calibrate/calibrate_sceua.py b/hydromodel/trainers/calibrate_sceua.py
similarity index 100%
rename from hydromodel/calibrate/calibrate_sceua.py
rename to hydromodel/trainers/calibrate_sceua.py
diff --git a/hydromodel/calibrate/calibrate_sceua_xaj_bmi.py b/hydromodel/trainers/calibrate_sceua_xaj_bmi.py
similarity index 100%
rename from hydromodel/calibrate/calibrate_sceua_xaj_bmi.py
rename to hydromodel/trainers/calibrate_sceua_xaj_bmi.py
diff --git a/test/cchz_diso.py b/hydromodel/trainers/cchz_diso.py
similarity index 100%
rename from test/cchz_diso.py
rename to hydromodel/trainers/cchz_diso.py
diff --git a/hydromodel/utils/plots.py b/hydromodel/trainers/plots.py
similarity index 100%
rename from hydromodel/utils/plots.py
rename to hydromodel/trainers/plots.py
diff --git a/hydromodel/utils/__init__.py b/hydromodel/utils/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hydromodel/utils/constant_unit.py b/hydromodel/utils/constant_unit.py
deleted file mode 100644
index f09eef7..0000000
--- a/hydromodel/utils/constant_unit.py
+++ /dev/null
@@ -1,63 +0,0 @@
-"""
-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/hydromodel/utils/hydro_utils.py b/hydromodel/utils/hydro_utils.py
deleted file mode 100644
index bd402b2..0000000
--- a/hydromodel/utils/hydro_utils.py
+++ /dev/null
@@ -1,514 +0,0 @@
-import json
-import os
-import re
-import zipfile
-import datetime as dt, datetime
-from typing import List
-import pickle
-from collections import OrderedDict
-import numpy as np
-import urllib
-from urllib import parse
-
-import requests
-import matplotlib.pyplot as plt
-from itertools import combinations
-
-import threading
-import functools
-
-import tqdm
-
-import logging
-
-
-# -----------------------------------------------logger setting----------------------------------------------------
-def get_hydro_logger(log_level_param):
-    logger = logging.getLogger(__name__)
-    # StreamHandler
-    stream_handler = logging.StreamHandler()  # console stream output
-    stream_handler.setLevel(level=log_level_param)
-    logger.addHandler(stream_handler)
-    return logger
-
-
-log_level = logging.INFO
-hydro_logger = get_hydro_logger(log_level)
-
-
-# ------------------------------------------------progress bar----------------------------------------------------
-def provide_progress_bar(
-    function, estimated_time, tstep=0.2, tqdm_kwargs={}, args=[], kwargs={}
-):
-    """
-    Tqdm wrapper for a long-running function
-
-    Parameters
-    ----------
-    function
-        function to run
-    estimated_time
-        how long you expect the function to take
-    tstep
-        time delta (seconds) for progress bar updates
-    tqdm_kwargs
-        kwargs to construct the progress bar
-    args
-        args to pass to the function
-    kwargs
-        keyword args to pass to the function
-
-    Returns
-    -------
-    function
-    """
-    ret = [None]  # Mutable var so the function can store its return value
-
-    def myrunner(function, ret, *args, **kwargs):
-        ret[0] = function(*args, **kwargs)
-
-    thread = threading.Thread(
-        target=myrunner, args=(function, ret) + tuple(args), kwargs=kwargs
-    )
-    pbar = tqdm.tqdm(total=estimated_time, **tqdm_kwargs)
-
-    thread.start()
-    while thread.is_alive():
-        thread.join(timeout=tstep)
-        pbar.update(tstep)
-    pbar.close()
-    return ret[0]
-
-
-def progress_wrapped(estimated_time, tstep=0.2, tqdm_kwargs={}):
-    """Decorate a function to add a progress bar"""
-
-    def real_decorator(function):
-        @functools.wraps(function)
-        def wrapper(*args, **kwargs):
-            return provide_progress_bar(
-                function,
-                estimated_time=estimated_time,
-                tstep=tstep,
-                tqdm_kwargs=tqdm_kwargs,
-                args=args,
-                kwargs=kwargs,
-            )
-
-        return wrapper
-
-    return real_decorator
-
-
-def setup_log(tag="VOC_TOPICS"):
-    # create logger
-    logger = logging.getLogger(tag)
-    # logger.handlers = []
-    logger.propagate = False
-    logger.setLevel(logging.DEBUG)
-    # create console handler and set level to debug
-    ch = logging.StreamHandler()
-    ch.setLevel(logging.DEBUG)
-    # create formatter
-    formatter = logging.Formatter(
-        "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-    )
-    # add formatter to ch
-    ch.setFormatter(formatter)
-    # add ch to logger
-    # logger.handlers = []
-    logger.addHandler(ch)
-    return logger
-
-
-# -------------------------------------------------plot utils-------------------------------------------------------
-def save_or_show_plot(file_nm: str, save: bool, save_path=""):
-    if save:
-        plt.savefig(os.path.join(save_path, file_nm))
-    else:
-        plt.show()
-
-
-# ----------------------------------------------------data tools--------------------------------------------------------
-def unzip_file(dataset_zip, path_unzip):
-    """extract a zip file"""
-    with zipfile.ZipFile(dataset_zip, "r") as zip_temp:
-        zip_temp.extractall(path_unzip)
-
-
-def unzip_nested_zip(dataset_zip, path_unzip):
-    """Extract a zip file including any nested zip files"""
-    with zipfile.ZipFile(dataset_zip, "r") as zfile:
-        zfile.extractall(path=path_unzip)
-    for root, dirs, files in os.walk(path_unzip):
-        for filename in files:
-            if re.search(r"\.zip$", filename):
-                file_spec = os.path.join(root, filename)
-                new_dir = os.path.join(root, filename[0:-4])
-                unzip_nested_zip(file_spec, new_dir)
-
-
-def zip_file_name_from_url(data_url, data_dir):
-    data_url_str = data_url.split("/")
-    filename = parse.unquote(data_url_str[-1])
-    zipfile_path = os.path.join(data_dir, filename)
-    unzip_dir = os.path.join(data_dir, filename[0:-4])
-    return zipfile_path, unzip_dir
-
-
-def is_there_file(zipfile_path, unzip_dir):
-    """if a file has existed"""
-    if os.path.isfile(zipfile_path):
-        if os.path.isdir(unzip_dir):
-            return True
-        unzip_nested_zip(zipfile_path, unzip_dir)
-        return True
-
-
-def download_one_zip(data_url, data_dir):
-    """download one zip file from url as data_file"""
-    zipfile_path, unzip_dir = zip_file_name_from_url(data_url, data_dir)
-    if not is_there_file(zipfile_path, unzip_dir):
-        if not os.path.isdir(unzip_dir):
-            os.mkdir(unzip_dir)
-        r = requests.get(data_url, stream=True)
-        with open(zipfile_path, "wb") as py_file:
-            for chunk in r.iter_content(chunk_size=1024):  # 1024 bytes
-                if chunk:
-                    py_file.write(chunk)
-        unzip_nested_zip(zipfile_path, unzip_dir), download_small_file
-
-
-def download_small_zip(data_url, data_dir):
-    """download zip file and unzip"""
-    zipfile_path, unzip_dir = zip_file_name_from_url(data_url, data_dir)
-    if not is_there_file(zipfile_path, unzip_dir):
-        if not os.path.isdir(unzip_dir):
-            os.mkdir(unzip_dir)
-        zipfile_path, _ = urllib.request.urlretrieve(data_url, zipfile_path)
-        unzip_nested_zip(zipfile_path, unzip_dir)
-
-
-def download_small_file(data_url, temp_file):
-    """download data from url to the temp_file"""
-    r = requests.get(data_url)
-    with open(temp_file, "w") as f:
-        f.write(r.text)
-
-
-def download_excel(data_url, temp_file):
-    """download a excel file according to url"""
-    if not os.path.isfile(temp_file):
-        urllib.request.urlretrieve(data_url, temp_file)
-
-
-def download_a_file_from_google_drive(drive, dir_id, download_dir):
-    file_list = drive.ListFile(
-        {"q": "'" + dir_id + "' in parents and trashed=false"}
-    ).GetList()
-    for file in file_list:
-        print("title: %s, id: %s" % (file["title"], file["id"]))
-        file_dl = drive.CreateFile({"id": file["id"]})
-        print("mimetype is %s" % file_dl["mimeType"])
-        if file_dl["mimeType"] == "application/vnd.google-apps.folder":
-            download_dir_sub = os.path.join(download_dir, file_dl["title"])
-            if not os.path.isdir(download_dir_sub):
-                os.makedirs(download_dir_sub)
-            download_a_file_from_google_drive(drive, file_dl["id"], download_dir_sub)
-        else:
-            # download
-            temp_file = os.path.join(download_dir, file_dl["title"])
-            if os.path.isfile(temp_file):
-                print("file has been downloaded")
-                continue
-            file_dl.GetContentFile(os.path.join(download_dir, file_dl["title"]))
-            print("Downloading file finished")
-
-
-class NumpyArrayEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, np.ndarray):
-            return obj.tolist()
-        return json.JSONEncoder.default(self, obj)
-
-
-def serialize_json_np(my_dict, my_file):
-    with open(my_file, "w") as FP:
-        json.dump(my_dict, FP, cls=NumpyArrayEncoder)
-
-
-def serialize_json(my_dict, my_file):
-    with open(my_file, "w") as FP:
-        json.dump(my_dict, FP, indent=4)
-
-
-def unserialize_json_ordered(my_file):
-    # m_file = os.path.join(my_file, 'master.json')
-    with open(my_file, "r") as fp:
-        m_dict = json.load(fp, object_pairs_hook=OrderedDict)
-    return m_dict
-
-
-def unserialize_json(my_file):
-    with open(my_file, "r") as fp: #自动分配和释放资源
-        my_object = json.load(fp)
-    return my_object
-
-
-def serialize_pickle(my_object, my_file):
-    f = open(my_file, "wb")
-    pickle.dump(my_object, f)
-    f.close()
-
-
-def unserialize_pickle(my_file):
-    f = open(my_file, "rb")
-    my_object = pickle.load(f)
-    f.close()
-    return my_object
-
-
-def serialize_numpy(my_array, my_file):
-    np.save(my_file, my_array)
-
-
-def unserialize_numpy(my_file):
-    y = np.load(my_file, allow_pickle=True)  #读取npy文件
-    return y
-
-
-# -------------------------------------------------time & date tools--------------------------------------------------
-def t2dt(t, hr=False):
-    t_out = None
-    if type(t) is int:
-        if 30000000 > t > 10000000:
-            t = dt.datetime.strptime(str(t), "%Y%m%d").date()
-            t_out = t if hr is False else t.datetime()
-
-    if type(t) is dt.date:
-        t_out = t if hr is False else t.datetime()
-
-    if type(t) is dt.datetime:
-        t_out = t.date() if hr is False else t
-
-    if t_out is None:
-        raise Exception("hydroDL.utils.t2dt failed")
-    return t_out
-
-
-def t_range2_array(t_range, *, step=np.timedelta64(1, "D")):
-    sd = t2dt(t_range[0])
-    ed = t2dt(t_range[1])
-    tArray = np.arange(sd, ed, step)
-    return tArray
-
-
-def t_range_days(t_range, *, step=np.timedelta64(1, "h")):
-    sd = dt.datetime.strptime(t_range[0], "%Y-%m-%d %H:%M:%S")
-    ed = dt.datetime.strptime(t_range[1], "%Y-%m-%d %H:%M:%S")
-    t_array = np.arange(sd, ed, step)
-    return t_array
-
-
-def t_days_lst2range(t_array):
-    if type(t_array[0]) == np.datetime64:
-        t0 = t_array[0].astype(datetime.datetime)
-        t1 = t_array[-1].astype(datetime.datetime)
-    else:
-        t0 = t_array[0]
-        t1 = t_array[-1]
-    sd = t0.strftime("%Y-%m-%d")
-    ed = t1.strftime("%Y-%m-%d")
-    return [sd, ed]
-
-
-def t_range_years(t_range):
-    """t_range is a left-closed and right-open interval, if t_range[1] is not Jan.1 then end_year should be included"""
-    start_year = int(t_range[0].split("-")[0])
-    end_year = int(t_range[1].split("-")[0])
-    end_month = int(t_range[1].split("-")[1])
-    end_day = int(t_range[1].split("-")[2])
-    if end_month == 1 and end_day == 1:
-        year_range_list = np.arange(start_year, end_year)
-    else:
-        year_range_list = np.arange(start_year, end_year + 1)
-    return year_range_list
-
-
-def get_year(a_time):
-    if isinstance(a_time, datetime.date):
-        return a_time.year
-    elif isinstance(a_time, np.datetime64):
-        return a_time.astype("datetime64[Y]").astype(int) + 1970
-    else:
-        return int(a_time[0:4])
-
-
-def intersect(t_lst1, t_lst2):
-    C, ind1, ind2 = np.intersect1d(t_lst1, t_lst2, return_indices=True)
-    return ind1, ind2
-
-
-def date_to_julian(a_time):
-    if type(a_time) == str:
-        fmt = "%Y-%m-%d"
-        dt = datetime.datetime.strptime(a_time, fmt)
-    else:
-        dt = a_time
-    tt = dt.timetuple()
-    julian_date = tt.tm_yday
-    return julian_date
-
-
-def t_range_to_julian(t_range):
-    t_array = t_range_days(t_range)
-    t_array_str = np.datetime_as_string(t_array)
-    julian_dates = [date_to_julian(a_time[0:10]) for a_time in t_array_str]
-    return julian_dates
-
-
-# --------------------------------------------------MATH CALCULATION---------------------------------------------------
-def subset_of_dict(dict, chosen_keys):
-    """make a new dict from key-values of chosen keys in a list"""
-    return {key: value for key, value in dict.items() if key in chosen_keys}
-
-
-def pair_comb(combine_attrs):
-    if len(combine_attrs) == 1:
-        values = list(combine_attrs[0].values())[0]
-        key = list(combine_attrs[0].keys())[0]
-        results = []
-        for value in values:
-            d = dict()
-            d[key] = value
-            results.append(d)
-        return results
-    items_all = list()
-    for dict_item in combine_attrs:
-        list_temp = list(dict_item.values())[0]
-        items_all = items_all + list_temp
-    all_combs = list(combinations(items_all, 2))
-
-    def is_in_same_item(item1, item2):
-        for dict_item in combine_attrs:
-            list_now = list(dict_item.values())[0]
-            if item1 in list_now and item2 in list_now:
-                return True
-        return False
-
-    def which_dict(item):
-        for dict_item in combine_attrs:
-            list_now = list(dict_item.values())[0]
-            if item in list_now:
-                return list(dict_item.keys())[0]
-
-    combs = [comb for comb in all_combs if not is_in_same_item(comb[0], comb[1])]
-    combs_dict = [
-        {which_dict(comb[0]): comb[0], which_dict(comb[1]): comb[1]} for comb in combs
-    ]
-    return combs_dict
-
-
-def flat_data(x):
-    xArrayTemp = x.flatten()
-    xArray = xArrayTemp[~np.isnan(xArrayTemp)]
-    xSort = np.sort(xArray)
-    return xSort
-
-
-def interpNan(x, mode="linear"):
-    if len(x.shape) == 1:
-        ngrid = 1
-        nt = x.shape[0]
-    else:
-        ngrid, nt = x.shape
-    for k in range(ngrid):
-        xx = x[k, :]
-        xx = interpNan1d(xx, mode)
-    return x
-
-
-def interpNan1d(x, mode="linear"):
-    i0 = np.where(np.isnan(x))[0]
-    i1 = np.where(~np.isnan(x))[0]
-    if len(i1) > 0:
-        if mode == "linear":
-            x[i0] = np.interp(i0, i1, x[i1])
-        if mode == "pre":
-            x0 = x[i1[0]]
-            for k in range(len(x)):
-                if k in i0:
-                    x[k] = x0
-                else:
-                    x0 = x[k]
-
-    return x
-
-
-def is_any_elem_in_a_lst(lst1, lst2, return_index=False, include=False):
-    do_exist = False
-    idx_lst = []
-    for j in range(len(lst1)):
-        if include:
-            for lst2_elem in lst2:
-                if lst1[j] in lst2_elem:
-                    idx_lst.append(j)
-                    do_exist = True
-        else:
-            if lst1[j] in lst2:
-                idx_lst.append(j)
-                do_exist = True
-    if return_index:
-        return do_exist, idx_lst
-    return do_exist
-
-
-def random_choice_no_return(arr, num_lst):
-    """sampling without replacement multi-times, and the num of each time is in num_lst"""
-    num_lst_arr = np.array(num_lst)
-    num_sum = num_lst_arr.sum()
-    if type(arr) == list:
-        arr = np.array(arr)
-    assert num_sum <= arr.size
-    results = []
-    arr_residue = np.arange(arr.size)
-    for num in num_lst_arr:
-        idx_chosen = np.random.choice(arr_residue.size, num, replace=False)
-        chosen_idx_in_arr = np.sort(arr_residue[idx_chosen])
-        results.append(arr[chosen_idx_in_arr])
-        arr_residue = np.delete(arr_residue, idx_chosen)
-    return results
-
-
-def find_integer_factors_close_to_square_root(integer):
-    start = int(np.sqrt(integer))
-    factor = integer / start
-    while not is_integer(factor):
-        start += 1
-        factor = integer / start
-    return int(factor), start
-
-
-def is_integer(number):
-    if int(number) == number:
-        return True
-    else:
-        return False
-
-
-def random_index(ngrid, nt, dim_subset):
-    batch_size, rho = dim_subset
-    i_grid = np.random.randint(0, ngrid, [batch_size])
-    i_t = np.random.randint(0, nt - rho, [batch_size])
-    return i_grid, i_t
-
-
-def flatten_list_function(input_list: List):
-    return [item for sublist in input_list for item in sublist]
-
-
-def nanlog(x):
-    if x != x:
-        return np.nan
-    else:
-        return np.log(x)
diff --git a/hydromodel/visual/hydro_plot.py b/hydromodel/visual/hydro_plot.py
deleted file mode 100644
index a96ae3d..0000000
--- a/hydromodel/visual/hydro_plot.py
+++ /dev/null
@@ -1,154 +0,0 @@
-"""
-Author: Wenyu Ouyang
-Date: 2022-12-02 15:15:20
-LastEditTime: 2022-12-02 15:17:08
-LastEditors: Wenyu Ouyang
-Description: 
-FilePath: \hydro-model-xaj\hydromodel\visual\hydro_plot.py
-Copyright (c) 2021-2022 Wenyu Ouyang. All rights reserved.
-"""
-from matplotlib import pyplot as plt
-import numpy as np
-import matplotlib.dates as mdates
-from hydromodel.utils import hydro_constant
-
-
-def plot_rainfall_runoff(
-    t,
-    p,
-    qs,
-    fig_size=(8, 6),
-    c_lst="rbkgcmy",
-    leg_lst=None,
-    dash_lines=None,
-    title=None,
-    xlabel=None,
-    ylabel=None,
-    linewidth=1,
-):
-    fig, ax = plt.subplots(figsize=fig_size)
-    if dash_lines is not None:
-        assert type(dash_lines) == list
-    else:
-        dash_lines = np.full(len(qs), False).tolist()
-    for k in range(len(qs)):
-        tt = t[k] if type(t) is list else t
-        q = qs[k]
-        leg_str = None
-        if leg_lst is not None:
-            leg_str = leg_lst[k]
-        (line_i,) = ax.plot(tt, q, color=c_lst[k], label=leg_str, linewidth=linewidth)
-        if dash_lines[k]:
-            line_i.set_dashes([2, 2, 10, 2])
-
-    ax.set_ylim(ax.get_ylim()[0], ax.get_ylim()[1] * 1.2)
-    # Create second axes, in order to get the bars from the top you can multiply by -1
-    ax2 = ax.twinx()
-    ax2.bar(tt, -p, color="b")
-
-    # Now need to fix the axis labels
-    max_pre = max(p)
-    ax2.set_ylim(-max_pre * 5, 0)
-    y2_ticks = np.arange(0, max_pre, 20)
-    y2_ticklabels = [str(i) for i in y2_ticks]
-    ax2.set_yticks(-1 * y2_ticks)
-    ax2.set_yticklabels(y2_ticklabels, fontsize=16)
-    # ax2.set_yticklabels([lab.get_text()[1:] for lab in ax2.get_yticklabels()])
-    if title is not None:
-        ax.set_title(title, loc="center", fontdict={"fontsize": 17})
-    if ylabel is not None:
-        ax.set_ylabel(ylabel, fontsize=18)
-    if xlabel is not None:
-        ax.set_xlabel(xlabel, fontsize=18)
-    ax2.set_ylabel("降水（mm/day）", fontsize=8, loc="top")
-    # ax2.set_ylabel("precipitation (mm/day)", fontsize=12, loc='top')
-    # https://github.com/matplotlib/matplotlib/issues/12318
-    ax.tick_params(axis="x", labelsize=16)
-    ax.tick_params(axis="y", labelsize=16)
-    ax.legend(bbox_to_anchor=(0.01, 0.9), loc="upper left", fontsize=16)
-    ax.grid()
-
-
-def plot_sim_and_obs(
-    date,
-    sim,
-    obs,
-    prcp,
-    save_fig,
-    xlabel="Date(∆t=1hour)",
-    ylabel="Streamflow(" + hydro_constant.unit["streamflow"] + ")",
-
-):
-    # matplotlib.use("Agg")
-    fig = plt.figure(figsize=(9,6),dpi=500)
-    ax = fig.subplots()
-    ax.plot(
-        date,
-        sim,
-        color="blue",
-        linestyle="-",
-        linewidth=1,
-        label="Simulation",
-    )
-    ax.plot(
-        date,
-        obs,
-        # "r.",
-        color="black",
-        linestyle="-",
-        linewidth=1,
-        label="Observation",
-    )
-    ylim = np.max(np.vstack((obs, sim)))
-    ax.set_ylim(0, ylim*1.3) 
-    ax.xaxis.set_major_formatter(mdates.DateFormatter("%y-%m-%d"))
-    ax.set_xlabel(xlabel)
-    ax.set_ylabel(ylabel)
-    plt.legend(loc="upper right")
-    
-    sim = np.array(sim)
-    obs = np.array(obs)
-
-    numerator = 0
-    denominator = 0
-    meangauge = 0
-    count = 0
-    for i in range(len(obs)):
-        if (obs[i]>=0):
-            numerator+=pow(abs(sim[i])-obs[i],2)
-            meangauge+=obs[i]
-            count+=1
-    meangauge=meangauge/count
-    for i in range(len(obs)):
-        if (obs[i]>=0):
-            denominator+=pow(obs[i]-meangauge,2)
-    NSE= 1-numerator/denominator
-    plt.text(0.9, 0.6, 'NSE=%.2f' % NSE, 
-         horizontalalignment='center',  
-         verticalalignment='center',
-         transform = ax.transAxes,
-         fontsize=10)
-    # plt.xticks(date) 
-    ax2 = ax.twinx()
-    ax2.bar(date,prcp, label='Precipitation', color='royalblue',alpha=0.9,width=0.05)
-    ax2.set_ylabel('Precipitation(mm)')
-    plt.yticks(fontproperties = 'Times New Roman', size = 10)
-    prcp_max = np.max(prcp)
-    ax2.set_ylim(0, prcp_max*4)
-    ax2.invert_yaxis()  #y轴反向
-    ax2.legend(loc='upper left')
-    plt.tight_layout()  # 自动调整子图参数,使之填充整个图像区域
-    plt.savefig(save_fig, bbox_inches="tight")
-    plt.close()
-
-
-def plot_train_iteration(likelihood, save_fig):
-    # matplotlib.use("Agg")
-    fig = plt.figure(figsize=(9, 6))  #绘制单个子图
-    ax = fig.subplots()
-    ax.plot(likelihood)
-    ax.set_ylabel("RMSE")
-    ax.set_xlabel("Iteration")
-    plt.savefig(save_fig, bbox_inches="tight")
-    # plt.cla()
-    plt.close()
diff --git a/notebooks/show_results.ipynb b/notebooks/show_results.ipynb
index 7e4f487..2948514 100644
--- a/notebooks/show_results.ipynb
+++ b/notebooks/show_results.ipynb
@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -12,26 +11,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Please Check your directory:\n",
+      "ROOT_DIR of the repo:  d:\\code\\hydro-model-xaj\n",
+      "DATASET_DIR of the repo:  d:\\data\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import sys\n",
     "from pathlib import Path\n",
     "\n",
-    "# Get the current directory of the notebook\n",
-    "notebook_dir = os.path.abspath(\"\")\n",
-    "# Get the parent directory of the notebook directory\n",
-    "project_dir = os.path.dirname(notebook_dir)\n",
-    "# import the module using a relative path\n",
-    "sys.path.append(project_dir)\n",
-    "import definitions\n"
+    "sys.path.append(os.path.dirname(Path(os.path.abspath('')).parent))\n",
+    "import definitions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,36 +45,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "metric_mean_file = Path(os.path.join(definitions.ROOT_DIR, \"hydromodel\", \"example\", \"exp201\", \"basins_test_metrics_mean_all_cases.csv\"))\n",
-    "metric_median_file = Path(os.path.join(definitions.ROOT_DIR, \"hydromodel\", \"example\", \"exp201\", \"basins_test_metrics_median_all_cases.csv\"))"
+    "metric_mean_file = Path(os.path.join(\"D:/研究生/毕业论文/new毕业论文/预答辩/碧流河水库/模型运行/basins_test_metrics_mean_all_cases.csv\"))\n",
+    "metric_median_file = Path(os.path.join(\"D:/研究生/毕业论文/new毕业论文/预答辩/碧流河水库/模型运行/basins_test_metrics_median_all_cases.csv\"))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: '/mnt/sdc/owen/code/hydro-model-xaj/hydromodel/example/exp201/basins_test_metrics_mean_all_cases.csv'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m metric_mean \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_csv(metric_mean_file, index_col\u001b[39m=\u001b[39;49m\u001b[39m0\u001b[39;49m)\n\u001b[1;32m      2\u001b[0m metric_median \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_csv(metric_median_file, index_col\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m)\n",
-      "File \u001b[0;32m/mnt/data/owen411/program/miniconda3/envs/xaj-dev/lib/python3.10/site-packages/pandas/io/parsers/readers.py:912\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    899\u001b[0m kwds_defaults \u001b[39m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    900\u001b[0m     dialect,\n\u001b[1;32m    901\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    908\u001b[0m     dtype_backend\u001b[39m=\u001b[39mdtype_backend,\n\u001b[1;32m    909\u001b[0m )\n\u001b[1;32m    910\u001b[0m kwds\u001b[39m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 912\u001b[0m \u001b[39mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
-      "File \u001b[0;32m/mnt/data/owen411/program/miniconda3/envs/xaj-dev/lib/python3.10/site-packages/pandas/io/parsers/readers.py:577\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    574\u001b[0m _validate_names(kwds\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mnames\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m))\n\u001b[1;32m    576\u001b[0m \u001b[39m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 577\u001b[0m parser \u001b[39m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m    579\u001b[0m \u001b[39mif\u001b[39;00m chunksize \u001b[39mor\u001b[39;00m iterator:\n\u001b[1;32m    580\u001b[0m     \u001b[39mreturn\u001b[39;00m parser\n",
-      "File \u001b[0;32m/mnt/data/owen411/program/miniconda3/envs/xaj-dev/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1407\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1404\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptions[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m kwds[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m   1406\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles: IOHandles \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m-> 1407\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_engine(f, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mengine)\n",
-      "File \u001b[0;32m/mnt/data/owen411/program/miniconda3/envs/xaj-dev/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1661\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1659\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m mode:\n\u001b[1;32m   1660\u001b[0m         mode \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m-> 1661\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39m=\u001b[39m get_handle(\n\u001b[1;32m   1662\u001b[0m     f,\n\u001b[1;32m   1663\u001b[0m     mode,\n\u001b[1;32m   1664\u001b[0m     encoding\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m   1665\u001b[0m     compression\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mcompression\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m   1666\u001b[0m     memory_map\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mmemory_map\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mFalse\u001b[39;49;00m),\n\u001b[1;32m   1667\u001b[0m     is_text\u001b[39m=\u001b[39;49mis_text,\n\u001b[1;32m   1668\u001b[0m     errors\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding_errors\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mstrict\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[1;32m   1669\u001b[0m     storage_options\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mstorage_options\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m   1670\u001b[0m )\n\u001b[1;32m   1671\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   1672\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles\u001b[39m.\u001b[39mhandle\n",
-      "File \u001b[0;32m/mnt/data/owen411/program/miniconda3/envs/xaj-dev/lib/python3.10/site-packages/pandas/io/common.py:859\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    854\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(handle, \u001b[39mstr\u001b[39m):\n\u001b[1;32m    855\u001b[0m     \u001b[39m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    856\u001b[0m     \u001b[39m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    857\u001b[0m     \u001b[39mif\u001b[39;00m ioargs\u001b[39m.\u001b[39mencoding \u001b[39mand\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m ioargs\u001b[39m.\u001b[39mmode:\n\u001b[1;32m    858\u001b[0m         \u001b[39m# Encoding\u001b[39;00m\n\u001b[0;32m--> 859\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(\n\u001b[1;32m    860\u001b[0m             handle,\n\u001b[1;32m    861\u001b[0m             ioargs\u001b[39m.\u001b[39;49mmode,\n\u001b[1;32m    862\u001b[0m             encoding\u001b[39m=\u001b[39;49mioargs\u001b[39m.\u001b[39;49mencoding,\n\u001b[1;32m    863\u001b[0m             errors\u001b[39m=\u001b[39;49merrors,\n\u001b[1;32m    864\u001b[0m             newline\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    865\u001b[0m         )\n\u001b[1;32m    866\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    867\u001b[0m         \u001b[39m# Binary mode\u001b[39;00m\n\u001b[1;32m    868\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(handle, ioargs\u001b[39m.\u001b[39mmode)\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/mnt/sdc/owen/code/hydro-model-xaj/hydromodel/example/exp201/basins_test_metrics_mean_all_cases.csv'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "metric_mean = pd.read_csv(metric_mean_file, index_col=0)\n",
     "metric_median = pd.read_csv(metric_median_file, index_col=0)"
@@ -78,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -115,58 +102,30 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>-38.040823</td>\n",
-       "      <td>140.371584</td>\n",
-       "      <td>133.078035</td>\n",
-       "      <td>0.718456</td>\n",
-       "      <td>0.402706</td>\n",
-       "      <td>0.402706</td>\n",
-       "      <td>0.466833</td>\n",
-       "      <td>-26.523422</td>\n",
-       "      <td>5.316441</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>HFsources</th>\n",
-       "      <td>-38.019304</td>\n",
-       "      <td>142.458621</td>\n",
-       "      <td>135.027414</td>\n",
-       "      <td>0.712506</td>\n",
-       "      <td>0.375980</td>\n",
-       "      <td>0.375980</td>\n",
-       "      <td>0.473212</td>\n",
-       "      <td>-21.426497</td>\n",
-       "      <td>5.874046</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>xajHFsources</th>\n",
-       "      <td>-31.410343</td>\n",
-       "      <td>179.334546</td>\n",
-       "      <td>174.828694</td>\n",
-       "      <td>0.495601</td>\n",
-       "      <td>0.178166</td>\n",
-       "      <td>0.178166</td>\n",
-       "      <td>0.241897</td>\n",
-       "      <td>-52.154434</td>\n",
-       "      <td>55.063198</td>\n",
+       "      <td>-10.431724</td>\n",
+       "      <td>84.941974</td>\n",
+       "      <td>84.29898</td>\n",
+       "      <td>0.625164</td>\n",
+       "      <td>0.380791</td>\n",
+       "      <td>0.380791</td>\n",
+       "      <td>0.308037</td>\n",
+       "      <td>-30.076788</td>\n",
+       "      <td>-100.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                   Bias        RMSE      ubRMSE      Corr        R2       NSE  \\\n",
-       "NaN          -38.040823  140.371584  133.078035  0.718456  0.402706  0.402706   \n",
-       "HFsources    -38.019304  142.458621  135.027414  0.712506  0.375980  0.375980   \n",
-       "xajHFsources -31.410343  179.334546  174.828694  0.495601  0.178166  0.178166   \n",
+       "                Bias       RMSE    ubRMSE      Corr        R2       NSE  \\\n",
+       "HFsources -10.431724  84.941974  84.29898  0.625164  0.380791  0.380791   \n",
        "\n",
-       "                   KGE        FHV        FLV  \n",
-       "NaN           0.466833 -26.523422   5.316441  \n",
-       "HFsources     0.473212 -21.426497   5.874046  \n",
-       "xajHFsources  0.241897 -52.154434  55.063198  "
+       "                KGE        FHV    FLV  \n",
+       "HFsources  0.308037 -30.076788 -100.0  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -177,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -214,58 +173,30 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>-23.563702</td>\n",
-       "      <td>103.561371</td>\n",
-       "      <td>100.163566</td>\n",
-       "      <td>0.698029</td>\n",
-       "      <td>0.435731</td>\n",
-       "      <td>0.435731</td>\n",
-       "      <td>0.517797</td>\n",
-       "      <td>-26.467879</td>\n",
-       "      <td>-27.434441</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>HFsources</th>\n",
-       "      <td>-24.489225</td>\n",
-       "      <td>104.603251</td>\n",
-       "      <td>101.225733</td>\n",
-       "      <td>0.695736</td>\n",
-       "      <td>0.414580</td>\n",
-       "      <td>0.414580</td>\n",
-       "      <td>0.531209</td>\n",
-       "      <td>-21.144583</td>\n",
-       "      <td>-26.480053</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>xajHFsources</th>\n",
-       "      <td>-16.771830</td>\n",
-       "      <td>125.237613</td>\n",
-       "      <td>123.441410</td>\n",
-       "      <td>0.483835</td>\n",
-       "      <td>0.186539</td>\n",
-       "      <td>0.186539</td>\n",
-       "      <td>0.219498</td>\n",
-       "      <td>-53.051419</td>\n",
-       "      <td>-1.451178</td>\n",
+       "      <td>-10.431724</td>\n",
+       "      <td>84.941974</td>\n",
+       "      <td>84.29898</td>\n",
+       "      <td>0.625164</td>\n",
+       "      <td>0.380791</td>\n",
+       "      <td>0.380791</td>\n",
+       "      <td>0.308037</td>\n",
+       "      <td>-30.076788</td>\n",
+       "      <td>-100.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                   Bias        RMSE      ubRMSE      Corr        R2       NSE  \\\n",
-       "NaN          -23.563702  103.561371  100.163566  0.698029  0.435731  0.435731   \n",
-       "HFsources    -24.489225  104.603251  101.225733  0.695736  0.414580  0.414580   \n",
-       "xajHFsources -16.771830  125.237613  123.441410  0.483835  0.186539  0.186539   \n",
+       "                Bias       RMSE    ubRMSE      Corr        R2       NSE  \\\n",
+       "HFsources -10.431724  84.941974  84.29898  0.625164  0.380791  0.380791   \n",
        "\n",
-       "                   KGE        FHV        FLV  \n",
-       "NaN           0.517797 -26.467879 -27.434441  \n",
-       "HFsources     0.531209 -21.144583 -26.480053  \n",
-       "xajHFsources  0.219498 -53.051419  -1.451178  "
+       "                KGE        FHV    FLV  \n",
+       "HFsources  0.308037 -30.076788 -100.0  "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -273,20 +204,13 @@
    "source": [
     "metric_median"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "xaj-dev",
+   "display_name": "Python 3.9.7 ('xaj')",
    "language": "python",
-   "name": "xaj-dev"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -298,7 +222,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.10.12"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/requirements.txt b/requirements.txt
index 245c186..f0de31a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,14 +1,13 @@
 ipykernel
-numpy
 numba
-pandas
 scikit-learn
 deap
 spotpy==1.5.14
-seaborn
-tqdm
 pytest
-hydrodataset
 bmipy
 pyyaml
-requests
\ No newline at end of file
+requests
+
+muskingumcunge
+
+hydrodataset
diff --git a/requirements_dev.txt b/requirements_dev.txt
index 5f559cb..522f725 100644
--- a/requirements_dev.txt
+++ b/requirements_dev.txt
@@ -14,13 +14,16 @@ pytest
 pytest-runner
 
 # for hydromodel
-numpy
+ipykernel
 numba
-pandas
 scikit-learn
 deap
 spotpy==1.5.14
-seaborn
-tqdm
+pytest
+bmipy
+pyyaml
+requests
+
+muskingumcunge
+
 hydrodataset
-hydroutils
diff --git a/scripts/calibrate_xaj.py b/scripts/calibrate_xaj.py
index eb7deb9..fa2ba4c 100644
--- a/scripts/calibrate_xaj.py
+++ b/scripts/calibrate_xaj.py
@@ -12,17 +12,17 @@
 
 sys.path.append(os.path.dirname(Path(os.path.abspath(__file__)).parent))
 import definitions
-from hydromodel.calibrate.calibrate_sceua import calibrate_by_sceua
-from hydromodel.data.data_postprocess import (
+from hydromodel.trainers.calibrate_sceua import calibrate_by_sceua
+from hydromodel.datasets.data_postprocess import (
     renormalize_params,
     read_save_sceua_calibrated_params,
     save_streamflow,
     summarize_metrics,
     summarize_parameters,
 )
-from hydromodel.utils.plots import show_calibrate_result, show_test_result
+from hydromodel.trainers.plots import show_calibrate_result, show_test_result
 from hydromodel.models.xaj import xaj
-from hydromodel.calibrate.calibrate_ga import calibrate_by_ga, show_ga_result
+from hydromodel.trainers.calibrate_ga import calibrate_by_ga, show_ga_result
 from hydromodel.utils import units
 
 
diff --git a/scripts/datapostprocess4calibrate.py b/scripts/datapostprocess4calibrate.py
index 4c6e08f..7ec964e 100644
--- a/scripts/datapostprocess4calibrate.py
+++ b/scripts/datapostprocess4calibrate.py
@@ -20,7 +20,7 @@
 
 sys.path.append(os.path.dirname(Path(os.path.abspath(__file__)).parent))
 import definitions
-from hydromodel.data.data_postprocess import read_and_save_et_ouputs
+from hydromodel.datasets.data_postprocess import read_and_save_et_ouputs
 
 
 def statistics(args):
diff --git a/scripts/datapreprocess4calibrate.py b/scripts/datapreprocess4calibrate.py
index 296473c..590896c 100644
--- a/scripts/datapreprocess4calibrate.py
+++ b/scripts/datapreprocess4calibrate.py
@@ -16,7 +16,7 @@
 
 sys.path.append(os.path.dirname(Path(os.path.abspath(__file__)).parent))
 import definitions
-from hydromodel.data.data_preprocess import (
+from hydromodel.datasets.data_preprocess import (
     trans_camels_format_to_xaj_format,
     cross_valid_data,
     split_train_test,
diff --git a/xaj/test-xaj-bmi.py b/test/test-xaj-bmi.py
similarity index 100%
rename from xaj/test-xaj-bmi.py
rename to test/test-xaj-bmi.py
diff --git a/test/test_data.py b/test/test_data.py
index 40331a6..2af99b3 100644
--- a/test/test_data.py
+++ b/test/test_data.py
@@ -23,7 +23,7 @@
 
 import definitions
 from hydromodel.utils import hydro_utils
-from hydromodel.data.data_preprocess import (
+from hydromodel.datasets.data_preprocess import (
     cross_valid_data,
     split_train_test,
 )
diff --git a/test/test_gr4j.py b/test/test_gr4j.py
index bb35c67..93c7175 100644
--- a/test/test_gr4j.py
+++ b/test/test_gr4j.py
@@ -15,9 +15,9 @@
 import spotpy
 from matplotlib import pyplot as plt
 import definitions
-from hydromodel.calibrate.calibrate_sceua import calibrate_by_sceua, SpotSetup
+from hydromodel.trainers.calibrate_sceua import calibrate_by_sceua, SpotSetup
 from hydromodel.models.gr4j import gr4j
-from hydromodel.utils.plots import show_calibrate_result
+from hydromodel.trainers.plots import show_calibrate_result
 
 
 @pytest.fixture()
diff --git a/test/test_hymod.py b/test/test_hymod.py
index 89da327..845f54f 100644
--- a/test/test_hymod.py
+++ b/test/test_hymod.py
@@ -16,9 +16,9 @@
 import spotpy
 
 import definitions
-from hydromodel.calibrate.calibrate_sceua import calibrate_by_sceua, SpotSetup
+from hydromodel.trainers.calibrate_sceua import calibrate_by_sceua, SpotSetup
 from hydromodel.models.hymod import hymod
-from hydromodel.utils.plots import show_calibrate_result
+from hydromodel.trainers.plots import show_calibrate_result
 
 
 @pytest.fixture()
diff --git a/test/test_rr_event_iden.py b/test/test_rr_event_iden.py
index bb5eb5e..46d9ec1 100644
--- a/test/test_rr_event_iden.py
+++ b/test/test_rr_event_iden.py
@@ -11,7 +11,7 @@
 import os
 import pandas as pd
 import definitions
-from hydromodel.utils.dmca_esr import rainfall_runoff_event_identify
+from hydromodel.datasets.dmca_esr import rainfall_runoff_event_identify
 
 
 def test_rainfall_runoff_event_identify():
diff --git a/test/test_xaj.py b/test/test_xaj.py
index 84dbf77..045e693 100644
--- a/test/test_xaj.py
+++ b/test/test_xaj.py
@@ -7,11 +7,11 @@
 from hydroutils import hydro_time
 
 import definitions
-from hydromodel.calibrate.calibrate_sceua import calibrate_by_sceua
-from hydromodel.calibrate.calibrate_ga import calibrate_by_ga
-from hydromodel.data.data_postprocess import read_save_sceua_calibrated_params
+from hydromodel.trainers.calibrate_sceua import calibrate_by_sceua
+from hydromodel.trainers.calibrate_ga import calibrate_by_ga
+from hydromodel.datasets.data_postprocess import read_save_sceua_calibrated_params
 from hydromodel.utils import units
-from hydromodel.utils.plots import show_calibrate_result, show_test_result
+from hydromodel.trainers.plots import show_calibrate_result, show_test_result
 from hydromodel.models.xaj import xaj, uh_gamma, uh_conv
 
 
diff --git a/test/test_xaj_bmi.py b/test/test_xaj_bmi.py
index b5ec937..0b8ba47 100644
--- a/test/test_xaj_bmi.py
+++ b/test/test_xaj_bmi.py
@@ -14,17 +14,17 @@
 from hydroutils import hydro_file
 
 from hydromodel.utils import units
-from hydromodel.data.data_preprocess import (
+from hydromodel.datasets.data_preprocess import (
     cross_valid_data,
     split_train_test,
 )
-from hydromodel.calibrate.calibrate_sceua_xaj_bmi import calibrate_by_sceua
-from hydromodel.calibrate.calibrate_ga_xaj_bmi import (
+from hydromodel.trainers.calibrate_sceua_xaj_bmi import calibrate_by_sceua
+from hydromodel.trainers.calibrate_ga_xaj_bmi import (
     calibrate_by_ga,
     show_ga_result,
 )
-from hydromodel.utils.plots import show_calibrate_result, show_test_result
-from hydromodel.data.data_postprocess import (
+from hydromodel.trainers.plots import show_calibrate_result, show_test_result
+from hydromodel.datasets.data_postprocess import (
     renormalize_params,
     read_save_sceua_calibrated_params,
     save_streamflow,
diff --git a/xaj/configuration.py b/xaj/configuration.py
deleted file mode 100644
index be41d42..0000000
--- a/xaj/configuration.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import yaml
-from hydromodel.data.data_preprocess import split_train_test, cross_valid_data
-from pathlib import Path
-import numpy as np
-from xaj.xajmodel import xaj_state as xaj_state
-
-class configuration():
-    
-    def read_config(config_file):
-        with open(config_file) as f:
-            config = yaml.safe_load(f)
-        return config
-    
-    def process_inputs(forcing_file, json_file, npy_file, config):
-        
-
-        train_period = config['train_period']  
-        test_period = config['test_period']
-        period = config['period']
-        json_file = Path(json_file)
-        npy_file = Path(npy_file)
-        split_train_test(json_file, npy_file, train_period, test_period)
-
-        warmup_length = config['warmup_length']  
-        cv_fold = config['cv_fold']  
-        cross_valid_data(json_file, npy_file, period, warmup_length, cv_fold) 
-        
-    def extract_forcing(forcing_data):
-        # p_and_e_df = forcing_data[["rainfall[mm]", "TURC [mm d-1]"]]
-        p_and_e_df = forcing_data[["pre", "turc"]]
-        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):
-
-        q_sim, es, *w0, w1, w2,s0, fr0, qi0, qg0 = xaj_state(              
-                                        p_and_e_warmup,              
-                                        params_state,           
-                                        warmup_length= warmup_length,
-                                        source_book="HF",
-                                        source_type="sources",              
-                                        return_state=True,            
-                                        )
-        return  q_sim,es,*w0, w1, w2, s0, fr0, qi0, qg0
-        print(*w0, w1, w2, s0, fr0, qi0, qg0)
-    def get_time_config(config):
-        start_time_str = config['start_time_str'] 
-        end_time_str = config['end_time_str']
-        time_units = config['time_units']
-        return start_time_str, end_time_str, time_units
-    
diff --git a/xaj/constant_unit.py b/xaj/constant_unit.py
deleted file mode 100644
index f09eef7..0000000
--- a/xaj/constant_unit.py
+++ /dev/null
@@ -1,63 +0,0 @@
-"""
-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/params.py b/xaj/params.py
deleted file mode 100644
index 43b654e..0000000
--- a/xaj/params.py
+++ /dev/null
@@ -1,141 +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, 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, 100.0],
-        #         "EX": [1.0, 1.5],
-        #         "KI": [0.0, 0.7],
-        #         "KG": [0.0, 0.7],
-        #         "CS": [0.0, 1.0],
-        #         "L": [1.0, 10.0],  # unit is day
-        #         "CI": [0.0, 0.9],
-        #         "CG": [0.98, 0.998],
-        #     }
-        # ),
-        "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/runxaj.yaml b/xaj/runxaj.yaml
deleted file mode 100644
index dba9186..0000000
--- a/xaj/runxaj.yaml
+++ /dev/null
@@ -1,21 +0,0 @@
-# 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"
-  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_file: "/home/wangjingyi/code/hydro-model-xaj/hydromodel/example/hymod_input.csv"
-  forcing_file: "/home/wangjingyi/code/hydro-model-xaj/scripts/尼尔基降雨.csv" 
-  warmup_length: 360
-#   json_file: "/home/wangjingyi/code/hydro-model-xaj/hydromodel/example/01013500_lump_p_pe_q.json"
-#   npy_file: "/home/wangjingyi/code/hydro-model-xaj/hydromodel/example/01013500_lump_p_pe_q.npy"
-
-#   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: 2
diff --git a/xaj/xaj_bmi.py b/xaj/xaj_bmi.py
deleted file mode 100644
index 2ee3fa9..0000000
--- a/xaj/xaj_bmi.py
+++ /dev/null
@@ -1,274 +0,0 @@
-from typing import Tuple
-from bmipy import Bmi
-import numpy as np
-
-from xaj.xajmodel import xaj_route, xaj_runoff
-from xaj.constant_unit import convert_unit,unit
-
-from grpc4bmi.constants import GRPC_MAX_MESSAGE_LENGTH
-import datetime 
-import pandas as pd
-import logging
-from xaj.configuration import configuration
-
-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):
-        try:
-            logger.info("xaj: initialize_model")
-            config = configuration.read_config(config_file)
-            forcing_data = pd.read_csv(config['forcing_file'])
-            p_and_e_df, p_and_e = configuration.extract_forcing(forcing_data)
-            p_and_e_warmup = p_and_e[0:config['warmup_length'],:,:]
-            params=np.tile([0.5], (1, 15))
-            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'])
-        
-            self._start_time_str,self._end_time_str, self._time_units = configuration.get_time_config(config) 
-            
-            self.params = params
-            self.warmup_length = config['warmup_length']
-            self.p_and_e_df = p_and_e_df
-            self.p_and_e = p_and_e
-            self.config = config
-            self.basin_area = config['basin_area']
-
-        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+1:self.time_step+self.warmup_length+1]
-        p_and_e_sim = self.p_and_e[1:self.time_step+1]
-        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,
-                )
-        if self.time_step+self.warmup_length+1 >= self.p_and_e.shape[0]:
-            q_sim,es = xaj_route(p_and_e_sim,
-                            params_route=self.params,
-                            model_name = "xaj",
-                            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):
-        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):
-        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
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