{ "cells": [ { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] }, { "ename": "ImportError", "evalue": "Traceback (most recent call last):\n File \"/home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow.py\", line 58, in \n from tensorflow.python.pywrap_tensorflow_internal import *\n File \"/home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py\", line 28, in \n _pywrap_tensorflow_internal = swig_import_helper()\n File \"/home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py\", line 24, in swig_import_helper\n _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)\n File \"/home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/imp.py\", line 242, in load_module\n return load_dynamic(name, filename, file)\n File \"/home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/imp.py\", line 342, in load_dynamic\n return _load(spec)\nImportError: /home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/site-packages/tensorflow/python/../../_solib_k8/_U@mkl_Ulinux_S_S_Cmkl_Ulibs_Ulinux___Uexternal_Smkl_Ulinux_Slib/libmklml_intel.so: symbol __kmpc_omp_task_with_deps, version VERSION not defined in file libiomp5.so with link time reference\n\n\nFailed to load the native TensorFlow runtime.\n\nSee https://www.tensorflow.org/install/errors\n\nfor some common reasons and solutions. Include the entire stack trace\nabove this error message when asking for help.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpywrap_tensorflow_internal\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 59\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpywrap_tensorflow_internal\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m__version__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_mod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0m_pywrap_tensorflow_internal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mswig_import_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 29\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mswig_import_helper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py\u001b[0m in \u001b[0;36mswig_import_helper\u001b[0;34m()\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0m_mod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_pywrap_tensorflow_internal'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpathname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescription\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 25\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/imp.py\u001b[0m in \u001b[0;36mload_module\u001b[0;34m(name, file, filename, details)\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mload_dynamic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 243\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mtype_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mPKG_DIRECTORY\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/imp.py\u001b[0m in \u001b[0;36mload_dynamic\u001b[0;34m(name, path, file)\u001b[0m\n\u001b[1;32m 341\u001b[0m name=name, loader=loader, origin=path)\n\u001b[0;32m--> 342\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mImportError\u001b[0m: /home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/site-packages/tensorflow/python/../../_solib_k8/_U@mkl_Ulinux_S_S_Cmkl_Ulibs_Ulinux___Uexternal_Smkl_Ulinux_Slib/libmklml_intel.so: symbol __kmpc_omp_task_with_deps, version VERSION not defined in file libiomp5.so with link time reference", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;31m#from sklearn.linear_model import Ridge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 34\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDropout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/site-packages/keras/__init__.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mactivations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mapplications\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/site-packages/keras/utils/__init__.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdata_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mio_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconv_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# Globally-importable utils.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/site-packages/keras/utils/conv_utils.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoves\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m 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\u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mops\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf_ops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmoving_averages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/site-packages/tensorflow/__init__.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;31m# pylint: disable=g-bad-import-order\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0;32mfrom\u001b[0m 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\"/home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py\", line 28, in \n _pywrap_tensorflow_internal = swig_import_helper()\n File \"/home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py\", line 24, in swig_import_helper\n _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)\n File \"/home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/imp.py\", line 242, in load_module\n return load_dynamic(name, filename, file)\n File \"/home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/imp.py\", line 342, in load_dynamic\n return _load(spec)\nImportError: /home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/site-packages/tensorflow/python/../../_solib_k8/_U@mkl_Ulinux_S_S_Cmkl_Ulibs_Ulinux___Uexternal_Smkl_Ulinux_Slib/libmklml_intel.so: symbol __kmpc_omp_task_with_deps, version VERSION not defined in file libiomp5.so with link time reference\n\n\nFailed to load the native TensorFlow runtime.\n\nSee https://www.tensorflow.org/install/errors\n\nfor some common reasons and solutions. Include the entire stack trace\nabove this error message when asking for help." ] } ], "source": [ "import numpy as np\n", "import datetime as dt\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "import dask\n", "dask.config.set(scheduler='synchronous')\n", "#from dask.distributed import Client, LocalCluster\n", "#cluster = LocalCluster(processes=True)\n", "#client = Client(cluster) # memory_limit='16GB', \n", "\n", "import xarray as xr\n", "from dask.diagnostics import ProgressBar\n", "\n", "import numpy as np\n", "import datetime as dt\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "\n", "#import joblib\n", "from sklearn.pipeline import Pipeline\n", "#from dask_ml.preprocessing import StandardScaler\n", "#from dask_ml.decomposition import PCA\n", "\n", "#from dask_ml.xgboost import XGBRegressor\n", "#from dask_ml.linear_model import LogisticRegression\n", "#from dask_ml.linear_model import LinearRegression\n", "#from sklearn.linear_model import Ridge\n", "\n", "import keras\n", "from keras.layers.core import Dropout" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sys\n", "print(sys.executable)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def shift_time(ds, value):\n", " ds.coords['time'].values = pd.to_datetime(ds.coords['time'].values) + value\n", " return ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# select data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "static = xr.open_dataset('../data/danube/era5_slt_z_slor_lsm_stationary_field.nc')\n", "print(static)\n", "glofas = xr.open_dataset('../data/danube/glofas_reanalysis_danube_1981-2002.nc')\n", "glofas = glofas.rename({'lat': 'latitude', 'lon': 'longitude'}) # to have the same name like in era5\n", "glofas = shift_time(glofas, -dt.timedelta(days=1))\n", "z_glofas = static['z'].isel(time=0)/9.81 # converting to m approx.\n", "z_glofas = z_glofas.interp(latitude=glofas.latitude,\n", " longitude=glofas.longitude)\n", "print(z_glofas)\n", "dis = glofas['dis']\n", "print(dis)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(26,6))\n", "i = 48.45\n", "j = 13.75\n", "z_point = z_glofas.sel(latitude=slice(i, i-0.01)).sel(longitude=slice(j, j+0.01))\n", "\n", "z_glofas.plot()\n", "\n", "# river mask\n", "river = dis.min('time') > 5\n", "river.name = 'river mask [0/1]'\n", "dis.mean('time').where(river).plot(cmap='RdBu')\n", "#dis.mean('time').plot(cmap='RdBu')\n", "\n", "ax = plt.gca()\n", "p = plt.scatter(j, i, s=1500, marker='o', lw=10, facecolor='none', color='cyan')\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(26,6))\n", "i = 48.45\n", "j = 13.75\n", "z_point = z_glofas.sel(latitude=slice(i, i-0.01)).sel(longitude=slice(j, j+0.01))\n", "\n", "z_glofas.where(z_glofas.values >= z_point.values).plot()\n", "dis.mean('time').where(river).plot(cmap='RdBu')\n", "\n", "ax = plt.gca()\n", "p = plt.scatter(j, i, s=1500, marker='o', lw=10, facecolor='none', color='cyan')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(26,6))\n", "i = 48.45\n", "j = 13.75\n", "z_point = z_glofas.sel(latitude=slice(i, i-0.01)).sel(longitude=slice(j, j+0.01))\n", "\n", "z_glofas.where(z_glofas.values >= z_point.values-150).plot()\n", "dis.mean('time').where(river).plot(cmap='RdBu')\n", "\n", "ax = plt.gca()\n", "p = plt.scatter(j, i, s=1500, marker='o', lw=10, facecolor='none', color='cyan')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dis_point = dis.where(z_point).squeeze()\n", "dis_point" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def correlate(da_3d, da_timeseries, timelag=False):\n", " a = da_3d - da_3d.mean('time')\n", " b = da_timeseries - da_timeseries.mean('time')\n", " N = len(b.coords['time'])\n", " if timelag:\n", " b = b.drop('time')\n", " a = a.drop('time')\n", "# out = b.dot(a)/a.std('time')/b.std()/N\n", " out = np.zeros(a.isel(time=1).shape)*np.nan\n", " i = 0\n", " for lat in a.latitude:\n", " j = 0\n", " for lon in a.longitude:\n", " a_iter = a.sel(latitude=lat, longitude=lon)\n", " out[i,j] = np.corrcoef(a_iter, b)[0,1]\n", " j += 1\n", " i += 1\n", " out = xr.DataArray(out, dims=['latitude', 'longitude'], coords=[a.latitude, a.longitude])\n", " out.name = 'correlation coefficient'\n", " return out" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "plt.figure(figsize=(26,6))\n", "i = 48.45\n", "j = 13.75\n", "z_point = z_glofas.sel(latitude=slice(i, i-0.01)).sel(longitude=slice(j, j+0.01))\n", "\n", "z_glofas.where(z_glofas.values >= z_point.values-150).plot()\n", "#dis.mean('time').where(river).plot(cmap='RdBu')\n", "\n", "ax = plt.gca()\n", "p = plt.scatter(j, i, s=1500, marker='o', lw=10, facecolor='none', color='cyan')\n", "\n", "\n", "a = correlate(dis.where(river), dis_point)\n", "a.plot(cmap='rainbow')\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def correlate(da_3d, da_timeseries, timelag=False):\n", " a = da_3d - da_3d.mean('time')\n", "# a = da_3d\n", " b = da_timeseries - da_timeseries.mean('time')\n", "# b = da_timeseries\n", " N = len(b.coords['time'])\n", " if timelag:\n", " b = b.drop('time')\n", " a = a.drop('time')\n", "# out = b.dot(a)/a.std('time')/b.std()/N\n", " out = np.zeros(a.isel(time=1).shape)*np.nan\n", " i = 0\n", " for lat in a.latitude:\n", " j = 0\n", " for lon in a.longitude:\n", " a_iter = a.sel(latitude=lat, longitude=lon)\n", " out[i,j] = np.corrcoef(a_iter, b)[0,1]\n", " j += 1\n", " i += 1\n", " out = xr.DataArray(out, dims=['latitude', 'longitude'], coords=[a.latitude, a.longitude])\n", " out.name = 'correlation coefficient'\n", " return out" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'z_point' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mdis_box\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mriver\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlag\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mlag\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# dis_box with data from future timesteps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mdis_point_lag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz_point\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlag\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mdis_box\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mriver\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlag\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'z_point' is not defined" ] } ], "source": [ "lags = [-1, 1]\n", "\n", "timelag_corrs = np.full((len(lags), dis.latitude.shape[0], dis.longitude.shape[0]), np.nan)\n", "for t, lag in enumerate(lags):\n", " if lag > 0: # dis_box with data from previous timesteps\n", " dis_point_lag = dis.where(z_point).squeeze()[lag:]\n", " dis_box = dis.where(river)[:-lag]\n", " elif lag < 0: # dis_box with data from future timesteps\n", " dis_point_lag = dis.where(z_point).squeeze()[:lag]\n", " dis_box = dis.where(river)[-lag:]\n", " \n", " timelag_corrs[t,:,:] = correlate(dis_box, dis_point_lag, timelag=True)\n", "\n", "\n", "lag_influencing = timelag_corrs[1,:,:] > timelag_corrs[0,:,:]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/site-packages/ipykernel_launcher.py:13: RuntimeWarning: invalid value encountered in greater\n", " del sys.path[0]\n", "/home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/site-packages/ipykernel_launcher.py:17: RuntimeWarning: invalid value encountered in less\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(26,6))\n", "plt.imshow(timelag_corrs[0,:,:])\n", "plt.colorbar()\n", "\n", "plt.figure(figsize=(26,6))\n", "plt.imshow(timelag_corrs[1,:,:])\n", "plt.colorbar()\n", "\n", "plt.figure(figsize=(26,6))\n", "plt.imshow(timelag_corrs[0,:,:] - timelag_corrs[1,:,:])\n", "plt.colorbar()\n", "\n", "lag_influencing = timelag_corrs[1,:,:] > timelag_corrs[0,:,:]\n", "plt.figure(figsize=(26,6))\n", "plt.imshow(lag_influencing)\n", "\n", "lag_influencing = timelag_corrs[1,:,:] < timelag_corrs[0,:,:]\n", "plt.figure(figsize=(26,6))\n", "plt.imshow(lag_influencing)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[False, False, False, ..., False, False, False],\n", " [False, False, False, ..., False, False, False],\n", " [False, False, False, ..., False, False, False],\n", " ...,\n", " [False, False, False, ..., False, False, False],\n", " [False, False, False, ..., False, False, False],\n", " [False, False, False, ..., False, False, False]])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lag_influencing" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# investigate single constraints" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "ename": "MemoryError", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mMemoryError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m 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**kwargs)\n\u001b[0m\u001b[1;32m 35\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 36\u001b[0m def wrapped_func(self, dim=None, axis=None, # type: ignore\n", "\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mreduce\u001b[0;34m(self, func, dim, axis, keep_attrs, keepdims, **kwargs)\u001b[0m\n\u001b[1;32m 1891\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1892\u001b[0m var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims,\n\u001b[0;32m-> 1893\u001b[0;31m **kwargs)\n\u001b[0m\u001b[1;32m 1894\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_replace_maybe_drop_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1895\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36mreduce\u001b[0;34m(self, func, dim, axis, keep_attrs, keepdims, allow_lazy, **kwargs)\u001b[0m\n\u001b[1;32m 1385\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1386\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_axis_num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1387\u001b[0;31m \u001b[0minput_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_lazy\u001b[0m \u001b[0;32melse\u001b[0m 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"\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/site-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m 514\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_indexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 516\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 517\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 518\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 537\u001b[0m \"\"\"\n\u001b[0;32m--> 538\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/site-packages/xarray/coding/variables.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/ml_flood/lib/python3.7/site-packages/xarray/coding/variables.py\u001b[0m in \u001b[0;36m_apply_mask\u001b[0;34m(data, encoded_fill_values, decoded_fill_value, dtype)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mencoded_fill_values\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0mcondition\u001b[0m \u001b[0;34m|=\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mfv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcondition\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecoded_fill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mMemoryError\u001b[0m: " ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(26,6))\n", "dis_box_mean = dis.mean('time')\n", "dis_box_mean.where(dis_box_mean > 50).plot()\n", "\n", "mask_box_mean_greater = (~np.isnan(dis_box_mean.where(dis_box_mean > 50))).astype(bool)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(26,6))\n", "z_river = z_glofas.where(river)\n", "z_river.where(z_river > z_point.squeeze()-100).plot()\n", "\n", "mask_river_altitude_greater = (~np.isnan(z_river.where(z_river > z_point.squeeze()-100))).astype(bool)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(26,6))\n", "(dis_box_mean > pct*dis_box_mean.sel(latitude=dis_point.latitude, longitude=dis_point.longitude)).plot()\n", "\n", "mask_mean_greater_pct_point = (dis_box_mean > pct*dis_box_mean.sel(latitude=dis_point.latitude, longitude=dis_point.longitude)).astype(bool)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(26,6))\n", "plt.imshow(lag_influencing > 0.9)\n", "\n", "mask_lag_greater = (lag_influencing > 0.9).astype(bool)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pct = 0.2\n", "# select feature gridpoints\n", "plt.figure(figsize=(26,6))\n", "influencer = (dis_box_mean > pct*dis_box_mean.sel(latitude=dis_point.latitude, longitude=dis_point.longitude)) \\\n", " &(river==1) & (lag_influencing > 0.5) \\\n", " &(z_glofas.where(z_glofas >= z_point.squeeze()-50)).astype(bool)\n", " \n", "influencer.name = 'gridpoints influencing discharge [0/1]'\n", "influencer.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(26,6))\n", "z_glofas.where(z_glofas > z_point.squeeze()-100).plot()\n", "\n", "mask_z_greater = (~np.isnan(z_glofas.where(z_glofas > z_point.squeeze()-100))).astype(bool)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'mask_river_altitude_greater' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmask_box_mean_greater\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmask_river_altitude_greater\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mmask_mean_greater_pct_point\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mmask_lag_greater\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmask_z_greater\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'mask_river_altitude_greater' is not defined" ] } ], "source": [ "mask_box_mean_greater\n", "mask_river_altitude_greater\n", "mask_mean_greater_pct_point\n", "mask_lag_greater\n", "mask_z_greater" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(26,6))\n", "dis_box_mean.where(mask_box_mean_greater).plot()\n", "plt.figure(figsize=(26,6))\n", "dis_box_mean.where(mask_river_altitude_greater).plot()\n", "plt.figure(figsize=(26,6))\n", "dis_box_mean.where(mask_mean_greater_pct_point).plot()\n", "plt.figure(figsize=(26,6))\n", "dis_box_mean.where(mask_lag_greater).plot()\n", "plt.figure(figsize=(26,6))\n", "dis_box_mean.where(mask_z_greater).plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(26,6))\n", "dis_box_mean.where(mask_box_mean_greater & mask_river_altitude_greater \\\n", " &mask_lag_greater & mask_z_greater & mask_mean_greater_pct_point).plot()\n", "\n", "p = plt.scatter(j, i, s=1500, marker='o', lw=10, facecolor='none', color='cyan')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(26,6))\n", "z_river = z_glofas.where(river)\n", "z_river.where(z_river > z_point.squeeze()-100).plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dis_point" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "from rasterio import features\n", "from affine import Affine\n", "\n", "def filter_data_inside_single_basin(da, kw_basins='Danube'):\n", " def transform_from_latlon(lat, lon):\n", " lat = np.asarray(lat)\n", " lon = np.asarray(lon)\n", " trans = Affine.translation(lon[0], lat[0])\n", " scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\n", " return trans * scale\n", "\n", " def rasterize(shapes, coords, fill=np.nan, **kwargs):\n", " \"\"\"Rasterize a list of (geometry, fill_value) tuples onto the given\n", " xray coordinates. This only works for 1d latitude and longitude\n", " arrays.\n", " \"\"\"\n", " transform = transform_from_latlon(coords['latitude'], coords['longitude'])\n", " out_shape = (len(coords['latitude']), len(coords['longitude']))\n", " raster = features.rasterize(shapes, out_shape=out_shape,\n", " fill=fill, transform=transform,\n", " dtype=float, **kwargs)\n", " return xr.DataArray(raster, coords=coords, dims=('latitude', 'longitude'))\n", " \n", " # this shapefile is from natural earth data\n", " # http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-1-states-provinces/\n", " shp2 = '/raid/home/srvx7/lehre/users/a1303583/ipython/ml_flood/data/drainage_basins/Major_Basins_of_the_World.shp'\n", " basins = geopandas.read_file(shp2)\n", "# print(basins)\n", " single_basin = basins.query(\"NAME == '\"+kw_basins+\"'\").reset_index(drop=True)\n", "# print(single_basin)\n", " shapes = [(shape, n) for n, shape in enumerate(single_basin.geometry)]\n", "\n", " da['basins'] = rasterize(shapes, da.coords)\n", " return da.where(da.basins == 0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "z_danube = filter_data_inside_single_basin(z_glofas, kw_basins='Danube')\n", "mask_danube_basin = ~np.isnan(z_danube).astype(bool)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# spatial feature selection" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(26,6))\n", "z_danube.plot()\n", "p = plt.scatter(j, i, s=1500, marker='o', lw=10, facecolor='none', color='cyan')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "z_downstream = z_glofas.where(z_glofas.longitude <= j)\n", "mask_downstream = ~np.isnan(z_downstream).astype(bool)\n", "\n", "plt.figure(figsize=(26,6))\n", "z_glofas.where(mask_downstream).plot()\n", "p = plt.scatter(j, i, s=1500, marker='o', lw=10, facecolor='none', color='cyan')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "z_downstream = z_glofas.where(z_glofas >= z_point.squeeze())\n", "z_downstream = z_downstream.where(z_glofas.longitude >= j)\n", "mask_downstream = ~np.isnan(z_downstream).astype(bool)\n", "\n", "plt.figure(figsize=(26,6))\n", "z_glofas.where(mask_downstream).plot()\n", "p = plt.scatter(j, i, s=1500, marker='o', lw=10, facecolor='none', color='cyan')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(26,6))\n", "dis_box_mean.where(mask_box_mean_greater).plot()\n", "\n", "p = plt.scatter(j, i, s=1500, marker='o', lw=10, facecolor='none', color='cyan')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(26,6))\n", "z_danube.plot()\n", "dis_box_mean.where(mask_box_mean_greater & mask_danube_basin & mask_downstream).plot(cmap='rainbow')\n", "\n", "p = plt.scatter(j, i, s=1500, marker='o', lw=10, facecolor='none', color='cyan')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dis_point.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# USA exemplary " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Dimensions: (latitude: 101, longitude: 221, time: 744)\n", "Coordinates:\n", " * longitude (longitude) float32 -125.0 -124.75 -124.5 ... -70.5 -70.25 -70.0\n", " * latitude (latitude) float32 50.0 49.75 49.5 49.25 ... 25.5 25.25 25.0\n", " * time (time) datetime64[ns] 2017-01-01 ... 2017-01-31T23:00:00\n", "Data variables:\n", " slor (time, latitude, longitude) float32 ...\n", " lsm (time, latitude, longitude) float32 ...\n", " slt (time, latitude, longitude) float32 ...\n", " z (time, latitude, longitude) float32 ...\n", "Attributes:\n", " Conventions: CF-1.6\n", " history: 2019-05-29 15:32:37 GMT by grib_to_netcdf-2.10.0: /opt/ecmw...\n", "\n", "array([[ 1.033807e+02, 1.295250e+02, 1.556693e+02, ..., 4.951622e+02,\n", " 4.973871e+02, 4.996119e+02],\n", " [ 1.442023e+02, 1.641796e+02, 1.841568e+02, ..., 4.795543e+02,\n", " 4.817030e+02, 4.838518e+02],\n", " [ 1.850239e+02, 1.988342e+02, 2.126444e+02, ..., 4.639464e+02,\n", " 4.660190e+02, 4.680916e+02],\n", " ...,\n", " [ 1.189793e-01, -1.559314e-01, -4.308422e-01, ..., -3.778829e-01,\n", " -6.951612e-01, -1.012439e+00],\n", " [ 1.824191e-01, -5.018794e-02, -2.827950e-01, ..., -1.664437e-01,\n", " -3.991144e-01, -6.317851e-01],\n", " [ 2.458588e-01, 5.555556e-02, -1.347477e-01, ..., 4.499553e-02,\n", " -1.030677e-01, -2.511309e-01]])\n", "Coordinates:\n", " time datetime64[ns] 2017-01-01\n", " * latitude (latitude) float64 49.95 49.85 49.75 49.65 ... 25.25 25.15 25.05\n", " * longitude (longitude) float64 -124.9 -124.8 -124.7 ... -70.25 -70.15 -70.05\n", "\n", "[1104812500 values with dtype=float32]\n", "Coordinates:\n", " * longitude (longitude) float64 -124.9 -124.8 -124.7 ... -70.25 -70.15 -70.05\n", " * latitude (latitude) float64 49.95 49.85 49.75 49.65 ... 25.25 25.15 25.05\n", " * time (time) datetime64[ns] 1981-01-01 1981-01-02 ... 2002-12-31\n", "Attributes:\n", " long_name: discharge\n", " units: m3/s\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "static = xr.open_dataset('../data/usa/era5_slt_z_slor_lsm_stationary_field.nc')\n", "print(static)\n", "alti = static['z'].isel(time=0)/9.81\n", "glofas = xr.open_dataset('../data/usa/glofas_reanalysis_usa_1981-2002.nc')\n", "glofas = glofas.rename({'lat': 'latitude', 'lon': 'longitude'}) # to have the same name like in era5\n", "glofas = shift_time(glofas, -dt.timedelta(days=1))\n", "z_glofas = static['z'].isel(time=0)/9.81 # converting to m approx.\n", "z_glofas = z_glofas.interp(latitude=glofas.latitude,\n", " longitude=glofas.longitude)\n", "print(z_glofas)\n", "dis = glofas['dis']\n", "print(dis)\n", "alti.where(alti > 0).plot.imshow()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "array(20.876736, dtype=float32)\n", "Coordinates:\n", " longitude float32 -92.0\n", " latitude float32 31.0\n", " time datetime64[ns] 2017-01-01" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "j = -92\n", "i = 31\n", "point = (i, j)\n", "alti.sel(latitude=point[0], longitude=point[1])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "from rasterio import features\n", "from affine import Affine\n", "\n", "def filter_data_inside_single_basin(da, kw_basins='Danube'):\n", " def transform_from_latlon(lat, lon):\n", " lat = np.asarray(lat)\n", " lon = np.asarray(lon)\n", " trans = Affine.translation(lon[0], lat[0])\n", " scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\n", " return trans * scale\n", "\n", " def rasterize(shapes, coords, fill=np.nan, **kwargs):\n", " \"\"\"Rasterize a list of (geometry, fill_value) tuples onto the given\n", " xray coordinates. This only works for 1d latitude and longitude\n", " arrays.\n", " \"\"\"\n", " transform = transform_from_latlon(coords['latitude'], coords['longitude'])\n", " out_shape = (len(coords['latitude']), len(coords['longitude']))\n", " raster = features.rasterize(shapes, out_shape=out_shape,\n", " fill=fill, transform=transform,\n", " dtype=float, **kwargs)\n", " return xr.DataArray(raster, coords=coords, dims=('latitude', 'longitude'))\n", " \n", " # this shapefile is from natural earth data\n", " # http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-1-states-provinces/\n", " shp2 = '/raid/home/srvx7/lehre/users/a1303583/ipython/ml_flood/data/drainage_basins/Major_Basins_of_the_World.shp'\n", " basins = geopandas.read_file(shp2)\n", "# print(basins)\n", " single_basin = basins.query(\"NAME == '\"+kw_basins+\"'\").reset_index(drop=True)\n", "# print(single_basin)\n", " shapes = [(shape, n) for n, shape in enumerate(single_basin.geometry)]\n", "\n", " da['basins'] = rasterize(shapes, da.coords)\n", " return da.where(da.basins == 0)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "z_mississippi = filter_data_inside_single_basin(z_glofas, kw_basins='Mississippi')\n", "mask_mississippi_basin = ~np.isnan(z_mississippi).astype(bool)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15,6))\n", "z_mississippi.plot(cmap='viridis')\n", "p = plt.scatter(j, i, s=1500, marker='o', lw=10, facecolor='none', color='cyan')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "z_downstream = z_glofas.where(z_glofas.latitude >= i)\n", "mask_downstream = ~np.isnan(z_downstream).astype(bool)\n", "\n", "plt.figure(figsize=(15,6))\n", "z_glofas.where(mask_downstream).plot(cmap='viridis')\n", "p = plt.scatter(j, i, s=1500, marker='o', lw=10, facecolor='none', color='cyan')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/srvx11/lehre/users/a1303583/.conda/envs/ml_flood/lib/python3.7/site-packages/xarray/core/nanops.py:160: RuntimeWarning: Mean of empty slice\n", " return np.nanmean(a, axis=axis, dtype=dtype)\n" ] } ], "source": [ "dis_usa = glofas['dis']\n", "dis_usa_mean = dis_usa[:1000].mean('time')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15,6))\n", "#dis_usa_mean.where(dis_usa_mean > 100).plot(cmap='RdBu')\n", "mask_box_mean_greater = (~np.isnan(dis_usa_mean.where(dis_usa_mean > 5))).astype(bool)\n", "\n", "dis_usa_mean.where(mask_box_mean_greater).plot(cmap='viridis')\n", "\n", "p = plt.scatter(j, i, s=500, marker='o', lw=3, facecolor='none', color='cyan')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15,6))\n", "dis_usa_mean.where(mask_box_mean_greater & mask_mississippi_basin & mask_downstream).plot()\n", "\n", "p = plt.scatter(j, i, s=1500, marker='o', lw=10, facecolor='none', color='cyan')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def add_shifted_predictors(ds, shifts, variables='all'):\n", " \"\"\"Adds additional variables to an array which are shifted in time.\n", " \n", " Parameters\n", " ----------\n", " ds : xr.Dataset\n", " shifts : list of integers\n", " variables : str or list\n", " \"\"\"\n", " if variables == 'all': \n", " variables = ds.data_vars\n", " \n", " for var in variables:\n", " for i in shifts:\n", " if i == 0: continue # makes no sense to shift by zero\n", " newvar = var+'-'+str(i)\n", " ds[newvar] = ds[var].shift(time=i)\n", " return ds" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "ename": "MemoryError", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mMemoryError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mshifts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mX_dis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_shifted_predictors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mglofas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshifts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'all'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mX_dis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_dis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dis'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# we actually want to predict (t) with (t-1, t-2, t-3)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0my_dis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mglofas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dis'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m\u001b[0m in \u001b[0;36madd_shifted_predictors\u001b[0;34m(ds, shifts, variables)\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mcontinue\u001b[0m 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\u001b[0;34m|=\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mfv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcondition\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecoded_fill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mMemoryError\u001b[0m: " ] } ], "source": [ "shifts = range(1,4)\n", "X_dis = add_shifted_predictors(glofas, shifts, variables='all')\n", "X_dis = X_dis.drop('dis') # we actually want to predict (t) with (t-1, t-2, t-3)\n", "y_dis = glofas['dis']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "def preprocess_reshape(X_dis, y_dis, i, j):\n", " \"\"\"Reshape, merge predictor/predictand in time, drop nans.\"\"\"\n", " X_dis = X_dis.to_array(dim='time_feature') \n", " X_dis = X_dis.stack(features=['latitude', 'longitude', 'time_feature'])\n", " Xar = X_dis.dropna('features', how='all')\n", " \n", " yar = y_dis[:,i,j]\n", " yar = yar.drop(['latitude', 'longitude'])\n", " yar.coords['features'] = 'dis'\n", " \n", " Xy = xr.concat([Xar, yar], dim='features')\n", " Xyt = Xy.dropna('time', how='any') # drop them as we cannot train on nan values\n", " time = Xyt.time\n", " \n", " Xda = Xyt[:,:-1]\n", " yda = Xyt[:,-1]\n", " return Xda, yda, time" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# space subset, dimensionality reduction\n", "#X_dis = X_dis.where(upstream)\n", "\n", "# time subset\n", "X_dis = X_dis.where(noprecip)\n", "y_dis = y_dis.where(noprecip)\n", " \n", "Xda, yda, time = preprocess_reshape(X_dis, y_dis, i,j)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2392, 78)\n", "train shapes: (1095, 78) (1095,)\n", "valid shapes: (365, 78) (365,)\n" ] } ], "source": [ "N_train = 365*3\n", "N_valid = 365\n", "\n", "X_train = Xda[:N_train,:]\n", "y_train = yda[:N_train]\n", "X_valid = Xda[N_train:N_train+N_valid,:]\n", "y_valid = yda[N_train:N_train+N_valid]\n", "\n", "print(Xda.shape)\n", "print('train shapes:', X_train.shape, y_train.shape)\n", "print('valid shapes:', X_valid.shape, y_valid.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "def add_time(vector, time, name=None):\n", " \"\"\"Converts arrays to xarrays with a time coordinate.\"\"\"\n", " return xr.DataArray(vector, dims=('time'), coords={'time': time}, name=name)\n", "\n", "class KerasDenseNN(object):\n", " def __init__(self, **kwargs):\n", " model = keras.models.Sequential()\n", " self.cfg = kwargs\n", " \n", " model.add(keras.layers.BatchNormalization())\n", " \n", " model.add(keras.layers.Dense(8,\n", " kernel_initializer='normal', \n", " bias_initializer='zeros',\n", " activation='relu')) #('sigmoid'))\n", " #model.add(Dropout(self.cfg.get('dropout')))\n", " #model.add(keras.layers.Dense(32))\n", " #model.add(keras.layers.Activation('sigmoid'))\n", " #model.add(Dropout(self.cfg.get('dropout')))\n", " #model.add(keras.layers.Dense(16))\n", " #model.add(keras.layers.Activation('sigmoid'))\n", " #model.add(Dropout(self.cfg.get('dropout')))\n", " #model.add(keras.layers.Dense(8))\n", " #model.add(keras.layers.Activation('sigmoid'))\n", " #model.add(Dropout(self.cfg.get('dropout')))\n", " model.add(keras.layers.Dense(1, activation='linear'))\n", " # bias_initializer=keras.initializers.Constant(value=9000)))\n", " \n", " #ha = self.cfg.get('hidden_activation')\n", "\n", " #for N_nodes in self.cfg.get('N_hidden_nodes'):\n", " # \n", " # model.add(hidden)\n", " # model.add(ha.copy())\n", " # \n", " # if self.cfg.get('dropout'):\n", " # model.add(Dropout(self.cfg.get('dropout')))#\n", "\n", " #outputlayer = keras.layers.Dense(1, activation='linear')\n", "\n", " #optimizer_name, options_dict = self.cfg.get('optimizer')\n", " #optimizer = getattr(keras.optimizers, optimizer_name)(**options_dict)\n", " #optimizer = keras.optimizers.SGD(lr=0.01)\n", " rmsprop = keras.optimizers.RMSprop(lr=.1)\n", " sgd = keras.optimizers.SGD(lr=0.1, decay=1e-6, momentum=0.5, nesterov=True)\n", "\n", " model.compile(loss=self.cfg.get('loss'), \n", " optimizer=rmsprop)\n", " self.model = model\n", "\n", " self.callbacks = [keras.callbacks.EarlyStopping(monitor='val_loss',\n", " min_delta=1, patience=100, verbose=0, mode='auto',\n", " baseline=None, restore_best_weights=True),]\n", "\n", " def predict(self, Xda, name=None):\n", " a = self.model.predict(Xda.values).squeeze()\n", " return add_time(a, Xda.time, name=name)\n", "\n", " def fit(self, Xda, yda, **kwargs):\n", " return self.model.fit(Xda.values, yda.values.reshape(-1,1),\n", " epochs=self.cfg.get('epochs', None),\n", " batch_size=self.cfg.get('batch_size', None),\n", " callbacks=self.callbacks,\n", " verbose=0,\n", " **kwargs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "mlp_kws = dict(optimizer=('sgd', dict(lr=1)),\n", " loss='mean_squared_error',\n", " #N_hidden_nodes=(4,4),\n", " #hidden_activation=keras.layers.Activation('sigmoid'), #keras.layers.ReLU(), #-LeakyReLU(alpha=0.3), #'relu',\n", " #output_activation='linear',\n", " #bias_initializer='random_uniform',\n", " batch_size=128,\n", " dropout=0., #.25,\n", " epochs=1000,\n", " )\n", "\n", "\n", "linear_kws = dict(C=.1, n_jobs=-1, max_iter=10000, verbose=True)\n", "\n", "\n", "if False:\n", " pipe = Pipeline([('scaler', StandardScaler()),\n", " ('pca', PCA(n_components=4)),\n", " ('model', LinearRegression(**linear_kws)),],\n", " verbose=True)\n", "if True:\n", " pipe = Pipeline([#('scaler', StandardScaler()),\n", " #('pca', PCA(n_components=2)),\n", " ('model', KerasDenseNN(**mlp_kws)),],\n", " verbose=False)\n", " " ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Pipeline(memory=None,\n", " steps=[('model', <__main__.KerasDenseNN object at 0x7f5b722e1da0>)],\n", " verbose=False)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipe" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": true }, "outputs": [], "source": [ "history = pipe.fit(X_train, y_train,\n", " model__validation_data=(X_valid, #.values, \n", " y_valid)) #.values.reshape(-1,1)))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "batch_normalization_1 (Batch (None, 78) 312 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 8) 632 \n", "_________________________________________________________________\n", "dense_2 (Dense) (None, 1) 9 \n", "=================================================================\n", "Total params: 953\n", "Trainable params: 797\n", "Non-trainable params: 156\n", "_________________________________________________________________\n" ] } ], "source": [ "keras.utils.print_summary(pipe.named_steps['model'].model)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "h = history.named_steps['model'].model.history\n", "\n", "# Plot training & validation loss values\n", "plt.plot(h.history['loss'], label='loss')\n", "plt.plot(h.history['val_loss'], label='val_loss')\n", "plt.title('Model loss')\n", "plt.ylabel('Loss')\n", "plt.xlabel('Epoch')\n", "plt.legend() #['Train', 'Test'], loc='upper left')\n", "plt.gca().set_yscale('log')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Test it on the same data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "y_train_pred = pipe.predict(X_train, name='dis-fcst-train')\n", "y_valid_pred = pipe.predict(X_valid, name='dis-fcst-valid')" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(365, 78)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_valid.values.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 42, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "(723181.0, 726629.0)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(24,5))\n", "minpred = X_train.min('features')\n", "maxpred = X_train.max('features')\n", "\n", "minpred.plot(ax=ax, linestyle='--', label='predictor-min')\n", "maxpred.plot(ax=ax, linestyle='--', label='predictor-max')\n", "\n", "\n", "dis[:,i,j].to_pandas().plot(ax=ax, label='dis-reanalysis')\n", "y_train_pred.plot(ax=ax, marker='.', lw=0)\n", "\n", "plt.legend()\n", "plt.gca().set_xlim(dt.datetime(1981,1,1), y_train_pred.time.values[-1]) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(726630.0, 727717.0)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(24,5))\n", "minpred = add_time(Xda.min(axis=1), time)\n", "maxpred = add_time(Xda.max(axis=1), time)\n", "\n", "minpred.plot(ax=ax, linestyle='--', label='predictor-min')\n", "maxpred.plot(ax=ax, linestyle='--', label='predictor-max')\n", "\n", "dis[:,i,j].to_pandas().plot(ax=ax, label='dis-reanalysis')\n", "y_valid_pred.plot(ax=ax, marker='.', lw=0)\n", "\n", "plt.legend()\n", "plt.gca().set_xlim(y_valid_pred.time.values[0], y_valid_pred.time.values[-1]) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }