"
]
},
{
"cell_type": "markdown",
"id": "59b94b7a",
"metadata": {},
"source": [
"CUWALID is an integrated model used to obtain actionable forecasts for hydrological components in drylands. It consists of a main hydrological model (DRYP) which allows to estimate the partioning of the water balance. This hydrological model is driven by two major climate inputs **precipitation** and **potential evapotranspiration (PET)**. The driving climate variables are obtained based on stochastic models integrated in the system. **STORM** is the precipitation model and **stoPET** is the PET model that generates the required driving climate variables. Here, we disscuss the stoPET model and how it works in the CUWALID system."
]
},
{
"cell_type": "markdown",
"id": "57621d77",
"metadata": {},
"source": [
"## 1. stoPET\n",
"stoPET is a stochastic PET generator over the globe (55N - 55S). The model consists of two scripts **run_stoPET.py** and **stoPET_v1.py** where the first one is used to provide the necessary inputs and run the model. The second one is the script containing the functions to generate the PET values. For training purposes, the function in the run_stoPET.py is provided below. Details of the model description can be found in the following paper stoPET paper and you can download the model from this link.\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "5246cd48",
"metadata": {},
"outputs": [],
"source": [
"# This is for showing plots \n",
"# interactively in Jupiter notebook\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "1a7f4264",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import numpy as np\n",
"from stoPET_v1 import *\n",
"from netCDF4 import Dataset\n",
"from mpl_toolkits.basemap import Basemap"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "1ba5f3c2",
"metadata": {},
"outputs": [],
"source": [
"def run_stoPET():\n",
" ## ----- CHANGE THE INPUT VARIABLES HERE -----##\n",
" datapath = 'stopet_parameter_files/'\n",
" outputpath = 'results/'\n",
" runtype = 'single' #'regional' #\n",
" startyear = 2000\n",
" endyear = 2010\n",
"\n",
" # Single point stoPET run\n",
" latval = 1.73\n",
" lonval = 40.09\n",
"\n",
" # Regional stoPET run\n",
" latval_min = -5.5\n",
" latval_max = -4.5 #5.5\n",
" lonval_min = 33.0\n",
" lonval_max = 34.5 #42.0\n",
" locname = 'Wajir' #'Turkana1' #\n",
"\n",
" number_ensm = 2\n",
" tempAdj = 3\n",
" deltat = 1.5\n",
" udpi_pet = 5\n",
"\n",
" ## ------ NO CHANGES BELLOW THIS -------------##\n",
" if runtype == 'single':\n",
" for ens_num in np.arange(0,number_ensm):\n",
" stoPET_wrapper_singlepoint(startyear, endyear, \n",
" latval, lonval, locname,\n",
" ens_num,datapath, outputpath, \n",
" tempAdj, deltat, udpi_pet)\n",
" elif runtype == 'regional':\n",
" for ens_num in np.arange(0,number_ensm):\n",
" stoPET_wrapper_regional(startyear, endyear, \n",
" latval_min, latval_max, \n",
" lonval_min, lonval_max,\n",
" locname, ens_num, datapath, \n",
" outputpath, \n",
" tempAdj, deltat, udpi_pet)\n",
" else:\n",
" raise ValueError('runtype only takes single and regional ... please check!')\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "cd4d04f9",
"metadata": {},
"source": [
"### 1.1 Changing input parameters\n",
"The above function is used to run the stoPET model and generate the required PET values. There are input parameters that are required to run it. The user can change these parameters in this function. stoPET can run in two types: one is a **single point run** and two is a **regional run**. If a single point run is selected, the user will need to provide the **lat/lon** of the specific location and the model will choose the nearest grid point to generate the PET value. If a regional run is selected, the user will need to provide the **four corners of a rectangle** that contain the region of interest. The user also needs to provide the **start year** and **end year** for the data and a **local name** to differentiate the region or location.\n",
"\n",
"### 1.2 Adjusting for temperature increase\n",
"If the user wants to adjust the PET values to account for increasing temperature, they can provide one of the three mothods included in the model **(tempAdj)**. This is an integer number with values 1, 2, or 3. Each of these numbers represents what method to use for the model to account for temperature adjustment on future PET. Method 1 = 1, Method 2 = 2, Method 3 = 3 (Refer to the stoPET paper for the description of each method).\n",
"\n",
" 1. Method 1: user-defined percentage step change in annual PET\n",
" 2. Method 2: step change in PET based on a user-defined change in atmospheric temperature\n",
" 3. Method 3: progressive change in PET based on the historical trend in hPET\n",
"\n",
"If the user chooses Method 1, the value used to increase the PET given as a percentage **(udpi_pet)**, will be used. If Method 2 is chosen, the value of the increased temperature given as **(deltat)** will be used. If Method 3 is chosen, **(deltat)** will be used and the **(udpi_pet)** will be ignored. \n",
"\n",
"The user also needs to provide how many ensembles of run are needed **(number_ensm)**. Once the user provides the required input values, the model can be executed using the following function."
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "3994891e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stoPET running ...\n",
"2000\n",
"2001\n",
"2002\n",
"2003\n",
"2004\n",
"2005\n",
"2006\n",
"2007\n",
"2008\n",
"2009\n",
"2010\n",
"stoPET finished successfully.\n",
"stoPET running ...\n",
"2000\n",
"2001\n",
"2002\n",
"2003\n",
"2004\n",
"2005\n",
"2006\n",
"2007\n",
"2008\n",
"2009\n",
"2010\n",
"stoPET finished successfully.\n"
]
}
],
"source": [
"run_stoPET()"
]
},
{
"cell_type": "markdown",
"id": "d1a6422f",
"metadata": {},
"source": [
"As seen in the output value the model generated eleven years (2000 - 2010) single point PET. stoPET ran twice as the number of ensembles given is two."
]
},
{
"cell_type": "markdown",
"id": "4e52e3dd",
"metadata": {},
"source": [
"### 1.3 Output\n",
"Once we run the model, the outputs will be saved in the output folder user provided. The output of these functions will be written in the output directory provided, where the model creates a new named directory `outputpath+locname+_E + number_ensm +_stoPET/`. \n",
" \n",
"For the single point run, stoPET generates text files `year_latval+_+lonval+_+tempAdj+stoPET.txt` and `year_latval+_+lonval+_+tempAdj+AdjstoPET.txt`. The first file is the PET generated without accounting for any adjustment for temperature. The second file is the adjusted PET based on the user’s choice (tempAdj). If one just wants to avoid any temperature change adjustment in the model, just use the first file output and ignore the second file.\n",
"\n",
"**Example:\n",
"1994_3.8_36.6_3_stoPET.txt and 1994_3.8_36.6_3_AdjstoPET.txt**\n",
"\n",
"stoPET output are hourly PET values of the year has 8760 hours for normal years and 8784 for leap years. Hence, in procesing the values, notice these array length for each year."
]
},
{
"cell_type": "markdown",
"id": "b1019d87",
"metadata": {},
"source": [
"### 1.4 Post processing and visualization\n",
"Here we have a basic script to analyse the generated PET data and visulize it through simple plots. The script is named as **post_processing_stopet.py** and it consists of functions that helps to process the hourly data and plot the values. The following functions are available currently but you can add any additional functions you would like to have.\n",
"\n",
" 1. leap_remove(timeseries) \n",
" 2. running_mean(timeseries, n)\n",
" 3. aggregate_data(timeseries, period)\n",
" 4. timeseries_plot(data, xlabel, ylabel, title, plotpath, fname)\n",
" 5. comparison_timeseries_plot(data_1, data_2, label_1, label_2, xlabel, ylabel, title, plotpath, fname)\n",
" 6. comparison_density_plot(data_1, data_2, label_1, label_2, xlabel, ylabel, title, plotpath, fname)\n",
" 7. plot_spatial(data, lats, lons, cmap , title, cbar_label, climin, climax, plotpath, figfname)\n",
"\n",
"You can get the details of the function input parapemters by using the folowing help function after importing the script.\n",
"**help(script.function)** \n",
"\n",
"**Example: help(leap_remove)**\n",
"\n",
"**Note:**\n",
"If a user wants multi year values comparison one must write a simple script to append the time series of each year before using the plotting function. "
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "2a559132",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function leap_remove in module post_processing_stopet:\n",
"\n",
"leap_remove(timeseries)\n",
" This function removes leap days from a time series \n",
" param timeseries: array containing daily time series\n",
" param datastartyear: start year of the input data\n",
" output data: time series with the leap days removed.\n",
"\n"
]
}
],
"source": [
"from post_processing_stopet import *\n",
"help(leap_remove)"
]
},
{
"cell_type": "markdown",
"id": "86445204",
"metadata": {},
"source": [
"# Kenya 1996 data doesn't exist"
]
},
{
"cell_type": "markdown",
"id": "b133aabb",
"metadata": {},
"source": [
"**Example:** let us remove the leap year data from 1996 timeseries\n",
"first read the 1996 PET data and check the array length"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "1a42983f",
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "results/Kenya_E0_StoPET/1996_3.8_36.6_3_stoPET.txt not found.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[34], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenfromtxt\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresults/Kenya_E0_StoPET/1996_3.8_36.6_3_stoPET.txt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(data))\n\u001b[1;32m 3\u001b[0m new_data \u001b[38;5;241m=\u001b[39m leap_remove(data)\n",
"File \u001b[0;32m~/anaconda3/envs/IS2/lib/python3.8/site-packages/numpy/lib/npyio.py:1977\u001b[0m, in \u001b[0;36mgenfromtxt\u001b[0;34m(fname, dtype, comments, delimiter, skip_header, skip_footer, converters, missing_values, filling_values, usecols, names, excludelist, deletechars, replace_space, autostrip, case_sensitive, defaultfmt, unpack, usemask, loose, invalid_raise, max_rows, encoding, ndmin, like)\u001b[0m\n\u001b[1;32m 1975\u001b[0m fname \u001b[38;5;241m=\u001b[39m os_fspath(fname)\n\u001b[1;32m 1976\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fname, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m-> 1977\u001b[0m fid \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_datasource\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1978\u001b[0m fid_ctx \u001b[38;5;241m=\u001b[39m contextlib\u001b[38;5;241m.\u001b[39mclosing(fid)\n\u001b[1;32m 1979\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
"File \u001b[0;32m~/anaconda3/envs/IS2/lib/python3.8/site-packages/numpy/lib/_datasource.py:193\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(path, mode, destpath, encoding, newline)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 157\u001b[0m \u001b[38;5;124;03mOpen `path` with `mode` and return the file object.\u001b[39;00m\n\u001b[1;32m 158\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 189\u001b[0m \n\u001b[1;32m 190\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 192\u001b[0m ds \u001b[38;5;241m=\u001b[39m DataSource(destpath)\n\u001b[0;32m--> 193\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnewline\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/envs/IS2/lib/python3.8/site-packages/numpy/lib/_datasource.py:533\u001b[0m, in \u001b[0;36mDataSource.open\u001b[0;34m(self, path, mode, encoding, newline)\u001b[0m\n\u001b[1;32m 530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _file_openers[ext](found, mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[1;32m 531\u001b[0m encoding\u001b[38;5;241m=\u001b[39mencoding, newline\u001b[38;5;241m=\u001b[39mnewline)\n\u001b[1;32m 532\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 533\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: results/Kenya_E0_StoPET/1996_3.8_36.6_3_stoPET.txt not found."
]
}
],
"source": [
"data = np.genfromtxt('results/Kenya_E0_StoPET/1996_3.8_36.6_3_stoPET.txt')\n",
"print(len(data))\n",
"new_data = leap_remove(data)\n",
"print(len(new_data))"
]
},
{
"cell_type": "markdown",
"id": "1a793fc8",
"metadata": {},
"source": [
"As seen above the origional data has a length of 8784 which is 366 days of hourly data. After we use the remove leap year function the new data has a length of 8760 which is 365 days of hourly values."
]
},
{
"cell_type": "markdown",
"id": "1ca69319",
"metadata": {},
"source": [
"### Example 1 \n",
"Now lets do a simple exercise based on a 10 year data for a single location in Eastern Kenya (Wajir).\n",
"\n",
" * lat = 1.73\n",
" * lon = 40.09\n",
" * start year = 2000\n",
" * end year = 2010\n",
" * two ensembles\n",
" * using Method 3 for temperature adjustment\n",
" * temperature increase of 1.5 degrees\n",
"\n",
"Adjust the **run_stoPET.py** acordingly and run to generate the PET data"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "c89e8583",
"metadata": {},
"outputs": [],
"source": [
"def run_stoPET():\n",
" ## ----- CHANGE THE INPUT VARIABLES HERE -----##\n",
" datapath = 'stopet_parameter_files/'\n",
" outputpath = 'results/'\n",
" runtype = 'single' #'regional' #\n",
" startyear = 2000\n",
" endyear = 2010\n",
"\n",
" # Single point stoPET run\n",
" latval = 1.73\n",
" lonval = 40.09\n",
"\n",
" # Regional stoPET run\n",
" latval_min = -5.5\n",
" latval_max = -4.5 #5.5\n",
" lonval_min = 33.0\n",
" lonval_max = 34.5 #42.0\n",
" locname = 'Wajir' #'Turkana1' #\n",
"\n",
" number_ensm = 2\n",
" tempAdj = 3\n",
" deltat = 1.5\n",
" udpi_pet = 5\n",
"\n",
" ## ------ NO CHANGES BELLOW THIS -------------##\n",
" if runtype == 'single':\n",
" for ens_num in np.arange(0,number_ensm):\n",
" stoPET_wrapper_singlepoint(startyear, endyear, \n",
" latval, lonval, locname,\n",
" ens_num,datapath, outputpath, \n",
" tempAdj, deltat, udpi_pet)\n",
" elif runtype == 'regional':\n",
" for ens_num in np.arange(0,number_ensm):\n",
" stoPET_wrapper_regional(startyear, endyear, \n",
" latval_min, latval_max, \n",
" lonval_min, lonval_max,\n",
" locname, ens_num, datapath, \n",
" outputpath, \n",
" tempAdj, deltat, udpi_pet)\n",
" else:\n",
" raise ValueError('runtype only takes single and regional ... please check!')\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "cf0e5879",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stoPET running ...\n",
"2000\n",
"2001\n",
"2002\n",
"2003\n",
"2004\n",
"2005\n",
"2006\n",
"2007\n",
"2008\n",
"2009\n",
"2010\n",
"stoPET finished successfully.\n",
"stoPET running ...\n",
"2000\n",
"2001\n",
"2002\n",
"2003\n",
"2004\n",
"2005\n",
"2006\n",
"2007\n",
"2008\n",
"2009\n",
"2010\n",
"stoPET finished successfully.\n"
]
}
],
"source": [
"run_stoPET()"
]
},
{
"cell_type": "markdown",
"id": "b822c4b7",
"metadata": {},
"source": [
"Now let us make the visulization of the daily time series of PET for the first year (2000). Notice 2000 is a leap year so first we need to remove the leap year values since it won't be necessary for now"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "8ccb20a6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"8760\n",
"8736\n"
]
}
],
"source": [
"# read the PET data for the year without temperature adjustment\n",
"data_1 = np.genfromtxt('results/Wajir_E0_StoPET/2000_1.73_40.09_3_stoPET.txt')\n",
"# read the PET data for the year with adjusted temeperature\n",
"data_2 = np.genfromtxt('results/Wajir_E0_StoPET/2009_1.73_40.09_3_AdjstoPET.txt')\n",
"# lets remove the leap year day data\n",
"data_1 = leap_remove(data_1)\n",
"data_2 = leap_remove(data_2)\n",
"# check the length of the array\n",
"print(len(data_1))\n",
"print(len(data_2))"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "8c795713",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# This is just to show the plots in the document the function \n",
"# already saved the plot in the plots folder.\n",
"fig=plt.figure()\n",
"plt.plot(data,'k')\n",
"plt.ylabel(ylabel) \n",
"plt.xlabel(xlabel) \n",
"plt.title(title,fontweight='bold') \n",
"plt.tight_layout() "
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "a0d6fa10",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Now lets plot the timeseres for the monthly PET values of the year 2000.\n",
"data = data_1_month\n",
"xlabel = 'Month'\n",
"ylabel = 'PET ($\\mathbf{mm\\,month^{-1}}$)' \n",
"title = 'Monthly PET value' \n",
"plotpath = './plots/' \n",
"fname = 'Wajir_2000_monthly_stoPET.png'\n",
"timeseries_plot(data, xlabel, ylabel, title, plotpath, fname)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "7b85d2f4",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# This is just to show the plots in the document the function \n",
"# already saved the plot in the plots folder.\n",
"fig=plt.figure()\n",
"plt.plot(data,'k')\n",
"plt.ylabel(ylabel) \n",
"plt.xlabel(xlabel) \n",
"plt.title(title,fontweight='bold') \n",
"plt.tight_layout() "
]
},
{
"cell_type": "markdown",
"id": "df8f9923",
"metadata": {},
"source": [
"Now lets plot the annual PET values and compare the temperature adjusted PET with non adjusted PET. Remember stoPET provide one file for each year so we need to loop through each year data and concatenate the values before plotting."
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "0021a009",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2000\n",
"2001\n",
"2002\n",
"2003\n",
"2004\n",
"2005\n",
"2006\n",
"2007\n",
"2008\n",
"2009\n",
"2010\n"
]
}
],
"source": [
"# first lets make the year list\n",
"years = np.arange(2000,2011)\n",
"# create empty array\n",
"data_1_year = [] # this is non adjusted PET\n",
"data_2_year = [] # this is the adjusted PET\n",
"# make a loop and estimate the annual PET value for both data\n",
"for i in range (0, len(years)):\n",
" year = years[i]\n",
" data_1 = np.genfromtxt('results/Wajir_E0_StoPET/%s_1.73_40.09_3_stoPET.txt'\n",
" %year)\n",
" data_2 = np.genfromtxt('results/Wajir_E0_StoPET/%s_1.73_40.09_3_AdjstoPET.txt'\n",
" %year)\n",
" # make the annual aggregate\n",
" data_1_val = aggregate_data(data_1, 'year')\n",
" data_2_val = aggregate_data(data_2, 'year')\n",
" # append the data to the empty array\n",
" data_1_year = np.append(data_1_year, data_1_val)\n",
" data_2_year = np.append(data_2_year, data_2_val)\n",
" print(year)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "e7ff338f",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# This is just to show the plots in the document the function \n",
"# already saved the plot in the plots folder.\n",
"fig=plt.figure()\n",
"plt.plot(data_1,'k', label=label_1)\n",
"plt.plot(data_2,'k--', label=label_2)\n",
"plt.ylabel(ylabel) \n",
"plt.xlabel(xlabel) \n",
"plt.title(title,fontweight='bold') \n",
"plt.legend(loc='best')\n",
"plt.tight_layout() "
]
},
{
"cell_type": "markdown",
"id": "831a2c7e",
"metadata": {},
"source": [
"---\n",
"## EXERCISE 1 \n",
"Based on the above examples please try to do the folowing.\n",
"\n",
" 1. Generate a 15 year dataset for the location of your choice.\n",
" 2. use each method of temperature adjustment (Method 1, Method 2 and Method 3) separatelly.\n",
" 3. generate 3 plots (one for each method) comparing the values of the annual PET and adjusted PET.\n",
"--- "
]
},
{
"cell_type": "markdown",
"id": "600ce572",
"metadata": {},
"source": [
"### 1.5 Generating regional data\n",
"Here we will generate the data for **Kenya** and use some plotting functions to visualize the data. Notice regional data takes more space so make sure there is enough storage for the data.\n",
"\n",
"To generate the regional data please adjust the input values in the **run_stoPET()** function and generate 5 years data for Kenya using Method 2 as temperature adjustment with a 1.5 degree increase.\n",
"\n",
"### Example 2 \n",
"Now lets do a simple exercise based on a 5 year data for Kenya.\n",
"\n",
" * latval_min = -5.5\n",
" * latval_max = 5.5\n",
" * lonval_min = 33.0\n",
" * lonval_max = 42.0\n",
" * start year = 2000\n",
" * end year = 2005\n",
" * two ensembles\n",
" * using Method 2 for temperature adjustment\n",
" * temperature increase of 1.5 degrees"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "407bfe43",
"metadata": {},
"outputs": [],
"source": [
"def run_stoPET():\n",
" ## ----- CHANGE THE INPUT VARIABLES HERE -----##\n",
" datapath = 'stopet_parameter_files/'\n",
" outputpath = 'results/'\n",
" runtype = 'regional' #'single' \n",
" startyear = 2000\n",
" endyear = 2005\n",
"\n",
" # Single point stoPET run\n",
" latval = 1.73\n",
" lonval = 40.09\n",
"\n",
" # Regional stoPET run\n",
" latval_min = -5.5\n",
" latval_max = 5.5\n",
" lonval_min = 33.0\n",
" lonval_max = 42.0\n",
" locname = 'Kenya'\n",
"\n",
" number_ensm = 2\n",
" tempAdj = 2\n",
" deltat = 1.5\n",
" udpi_pet = 5\n",
"\n",
" ## ------ NO CHANGES BELLOW THIS -------------##\n",
" if runtype == 'single':\n",
" for ens_num in np.arange(0,number_ensm):\n",
" stoPET_wrapper_singlepoint(startyear, endyear, \n",
" latval, lonval, locname,\n",
" ens_num,datapath, \n",
" outputpath, tempAdj, \n",
" deltat, udpi_pet)\n",
" elif runtype == 'regional':\n",
" for ens_num in np.arange(0,number_ensm):\n",
" stoPET_wrapper_regional(startyear, endyear, \n",
" latval_min, latval_max, \n",
" lonval_min, lonval_max,\n",
" locname, ens_num, datapath, \n",
" outputpath, \n",
" tempAdj, deltat, udpi_pet)\n",
" else:\n",
" raise ValueError('runtype only takes single and regional ... please check!')\n"
]
},
{
"cell_type": "markdown",
"id": "2bfd3f9d",
"metadata": {},
"source": [
"Now run the function run_stoPET()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "0857a6da",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stoPET running ...\n"
]
},
{
"ename": "PermissionError",
"evalue": "[Errno 13] Permission denied: 'results/Kenya_E0_StoPET/2000_2_stoPET.nc'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mPermissionError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[58], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mrun_stoPET\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[54], line 35\u001b[0m, in \u001b[0;36mrun_stoPET\u001b[0;34m()\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m runtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mregional\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ens_num \u001b[38;5;129;01min\u001b[39;00m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m0\u001b[39m,number_ensm):\n\u001b[0;32m---> 35\u001b[0m \u001b[43mstoPET_wrapper_regional\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstartyear\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mendyear\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 36\u001b[0m \u001b[43m \u001b[49m\u001b[43mlatval_min\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlatval_max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 37\u001b[0m \u001b[43m \u001b[49m\u001b[43mlonval_min\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlonval_max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 38\u001b[0m \u001b[43m \u001b[49m\u001b[43mlocname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mens_num\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatapath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 39\u001b[0m \u001b[43m \u001b[49m\u001b[43moutputpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 40\u001b[0m \u001b[43m \u001b[49m\u001b[43mtempAdj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdeltat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mudpi_pet\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mruntype only takes single and regional ... please check!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
"File \u001b[0;32m~/Documents/CUWALID_training/stoPET/stoPET_v1.py:306\u001b[0m, in \u001b[0;36mstoPET_wrapper_regional\u001b[0;34m(startyear, endyear, latval_min, latval_max, lonval_min, lonval_max, locname, ens_num, datapath, outputpath, tempAdj, deltat, udpi_pet)\u001b[0m\n\u001b[1;32m 302\u001b[0m mpercent\u001b[38;5;241m=\u001b[39mDataset(datapath\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmonthly_cont_percentage.nc\u001b[39m\u001b[38;5;124m'\u001b[39m) \n\u001b[1;32m 303\u001b[0m mcont_vals \u001b[38;5;241m=\u001b[39m mpercent\u001b[38;5;241m.\u001b[39mvariables[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmcontper\u001b[39m\u001b[38;5;124m'\u001b[39m][:,:,:]\n\u001b[0;32m--> 306\u001b[0m stopet \u001b[38;5;241m=\u001b[39m \u001b[43mfuture_pet_ts_generate_regional\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstartyear\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mendyear\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlatval_min\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlatval_max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlonval_min\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlonval_max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 307\u001b[0m \u001b[43m \u001b[49m\u001b[43mlats\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlons\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mampl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43momega\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mphase\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshift\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskew\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mloc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 308\u001b[0m \u001b[43m \u001b[49m\u001b[43mslope_vals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmcont_vals\u001b[49m\u001b[43m,\u001b[49m\u001b[43mens_num\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatapath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutputpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtempAdj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdeltat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpetdt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstoPET finished successfully.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 312\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"File \u001b[0;32m~/Documents/CUWALID_training/stoPET/stoPET_v1.py:413\u001b[0m, in \u001b[0;36mfuture_pet_ts_generate_regional\u001b[0;34m(startyear, endyear, latval_min, latval_max, lonval_min, lonval_max, lats, lons, locname, ampl, omega, phase, shift, sr, ss, skew, loc, scale, slope_vals, mcont_vals, ens_num, datapath, outputpath, tempAdj, deltat, dpetdt)\u001b[0m\n\u001b[1;32m 411\u001b[0m filename2 \u001b[38;5;241m=\u001b[39m outputpath\u001b[38;5;241m+\u001b[39mlocname\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_E\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(ens_num)\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_StoPET/\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(yr)\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(tempAdj)\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_AdjstoPET.nc\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 412\u001b[0m tunits \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdays since \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(yr)\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-01-01\u001b[39m\u001b[38;5;124m'\u001b[39m \n\u001b[0;32m--> 413\u001b[0m \u001b[43mnc_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstoch_pet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlatlen\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlonlen\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpet\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtunits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename1\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 414\u001b[0m nc_write(stoch_pet_adj, latlen, lonlen, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpet\u001b[39m\u001b[38;5;124m'\u001b[39m, tunits, filename2)\n\u001b[1;32m 416\u001b[0m \u001b[38;5;66;03m# delete the array for next year\u001b[39;00m\n",
"File \u001b[0;32m~/Documents/CUWALID_training/stoPET/stoPET_v1.py:531\u001b[0m, in \u001b[0;36mnc_write\u001b[0;34m(data, lat, lon, varname, tunits, filename)\u001b[0m\n\u001b[1;32m 517\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnc_write\u001b[39m(data, lat, lon, varname, tunits, filename):\n\u001b[1;32m 518\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 519\u001b[0m \u001b[38;5;124;03m this function write the PET on a netCDF file.\u001b[39;00m\n\u001b[1;32m 520\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[38;5;124;03m :return: produce a netCDF file in the same directory.\u001b[39;00m\n\u001b[1;32m 529\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 531\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mDataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mw\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mNETCDF4_CLASSIC\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 533\u001b[0m time \u001b[38;5;241m=\u001b[39m ds\u001b[38;5;241m.\u001b[39mcreateDimension(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 534\u001b[0m latitude \u001b[38;5;241m=\u001b[39m ds\u001b[38;5;241m.\u001b[39mcreateDimension(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlatitude\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mlen\u001b[39m(lat))\n",
"File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2449\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2012\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'results/Kenya_E0_StoPET/2000_2_stoPET.nc'"
]
}
],
"source": [
"run_stoPET()"
]
},
{
"cell_type": "markdown",
"id": "22274c96",
"metadata": {},
"source": [
"Lets read first year data and aggregate to annual PET. Each year’s file will have a **four dimensional array (days, hours, latitude, longitude)**. The variable name for the stochastically generated PET is **pet** within the netCDF files."
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "76b2fbff",
"metadata": {},
"outputs": [
{
"ename": "OSError",
"evalue": "[Errno -51] NetCDF: Unknown file format: 'results/Kenya_E0_StoPET/2000_2_stoPET.nc'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[63], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m nc \u001b[38;5;241m=\u001b[39m \u001b[43mDataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresults/Kenya_E0_StoPET/2000_2_stoPET.nc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m lats \u001b[38;5;241m=\u001b[39m nc\u001b[38;5;241m.\u001b[39mvariables[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlatitude\u001b[39m\u001b[38;5;124m'\u001b[39m][:]\n\u001b[1;32m 3\u001b[0m lons \u001b[38;5;241m=\u001b[39m nc\u001b[38;5;241m.\u001b[39mvariables[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlongitude\u001b[39m\u001b[38;5;124m'\u001b[39m][:]\n",
"File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2449\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2012\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mOSError\u001b[0m: [Errno -51] NetCDF: Unknown file format: 'results/Kenya_E0_StoPET/2000_2_stoPET.nc'"
]
}
],
"source": [
"nc = Dataset('results/Kenya_E0_StoPET/2000_2_stoPET.nc')\n",
"lats = nc.variables['latitude'][:]\n",
"lons = nc.variables['longitude'][:]\n",
"pet = nc.variables['pet'][:,:,:,:] \n",
"# check array shape\n",
"print(pet.shape)\n",
"# make the annual sum PET\n",
"annual_pet = np.sum(pet, axis=(0,1))\n",
"# check array shape\n",
"print(annual_pet.shape)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "a3db87e6",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'annual_pet' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[64], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# let us plot the annual PET\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mannual_pet\u001b[49m\n\u001b[1;32m 3\u001b[0m cmap \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mcm\u001b[38;5;241m.\u001b[39mYlOrBr\n\u001b[1;32m 4\u001b[0m title \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAnnual PET\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
"\u001b[0;31mNameError\u001b[0m: name 'annual_pet' is not defined"
]
}
],
"source": [
"# let us plot the annual PET\n",
"data = annual_pet\n",
"cmap = plt.cm.YlOrBr\n",
"title = 'Annual PET'\n",
"cbar_label = '$\\mathbf{mm\\,year^{-1}}$'\n",
"climin = 1000.0 \n",
"climax = 2500.0 \n",
"plotpath = './plots/' \n",
"figfname = 'Kenya_annual+PET_2000.png'\n",
"plot_spatial(data, lats, lons, cmap , title, cbar_label, \n",
" climin, climax, plotpath, figfname)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "8681c674",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"m = Basemap(projection='cyl', llcrnrlat=min(lats), \n",
" urcrnrlat=max(lats), llcrnrlon=min(lons),\n",
" urcrnrlon=max(lons), resolution='l')\n",
"\n",
"cs4 = plt.imshow(data, interpolation='nearest', cmap=cmap,\n",
" extent=[min(lons), max(lons), \n",
" min(lats), max(lats)]) \n",
"m.drawcoastlines(linewidth=1.0)\n",
"m.drawcountries(linewidth=0.75)\n",
"parallels=np.arange(-90.,90.,10.0)\n",
"meridians=np.arange(0.,360.,10.0)\n",
"\n",
"m.drawlsmask(land_color=(0,0,0,0), ocean_color='white', lakes=False) \n",
"plt.title(title, fontweight='bold', loc='center')\n",
"cb4 = plt.colorbar(cs4, label=cbar_label, shrink=0.4, \n",
" pad=0.02, extend='both', orientation='horizontal')\n",
"cb4.mappable.set_clim(climin, climax)\n",
"plt.tight_layout() "
]
},
{
"cell_type": "markdown",
"id": "6524acbf",
"metadata": {},
"source": [
"---\n",
"## EXERCISE 2 \n",
"Based on the above example on generating regional PET data please do the folowing.\n",
"\n",
" 1. Generate a 5 year dataset for Uganda.\n",
" 2. use Method 1 for temperaature adjustment.\n",
" 3. generate comparison plots side-by-side (write your own visualization code using python subplots) \n",
" for the average annual PET values of the non-adjusted PET and adjusted PET.\n",
"--- "
]
},
{
"cell_type": "markdown",
"id": "9ac72883",
"metadata": {},
"source": [
"## 2. Batch job submission \n",
"In order to run the CUWALID model, we use the High Performance Computers (HPC) provided by institutes. These HPC's are better for running multiple jobs at the same time. This will save time and allow us to run models that use large data.\n",
"\n",
"Here, we discuss on how you can submit multiple jobs to HPC. ***Please notice that this is not the only way to do the work but just to share one way of doing it***.\n",
"\n",
"### 2.1 creating directories to hold necessary files\n",
"The first step we need to do is create three important directories in our home directory. These are:\n",
"\n",
"* bSub_runME\n",
"* bSub_logME\n",
"* bSub_doneME\n",
"\n",
"You can create these directories by runing the mkdir command in the terminal. Here is the command\n",
"* `mkdir bSub_runME`\n",
"* `mkdir bSub_logME`\n",
"* `mkdir bSub_doneME`\n",
"\n",
"Once you create these directories you can use them anytime for any job submission (i.e No need to make these directories everytime).\n",
"\n",
"**How do we use these three directories?**\n",
"\n",
"The **bSub_runME** directory is used to save our bash files (e.g ***myjob.bash***) that contain all the necessary submission commands and the job to be run.\n",
"\n",
"The **bSub_logME** directory is used to save error files and output files from the job we are running. If there isan error and the job is cancelled you can go tho this directory and read the error message in the ***jobid.error*** file. If there are any print statments in your job or any other print that would have been printed on the terminal will be saved in the ***jonid.out*** file.\n",
"\n",
"The **bSub_doneME** directory is used to save our bash files (e.g ***myjob.bash***) after the job is submitted. Hence, if there is an error and you have to run the job again you can easily move the bash files from this directory to the bSub_runME directory without creating them again.\n",
"\n",
"### 2.2 Creating the bash files\n",
"In order to submit multiple jobs to HPC, we need to follow certain commands that are set by the institute. Hence, we can prepare a python script to write these bash files so that we save time to write each file mannually. The following python script **write_bash_files.py** is provided to prepare the bash files used in the job sumbission. Below, we will discuss how it works using one example function in the script."
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "8587e7b3",
"metadata": {},
"outputs": [],
"source": [
"# This script prepare the .bash file\n",
"# containing the arguments for downloading data\n",
"import numpy as np\n",
"def write_bsub(year):\n",
" # file name to be saved in the bSub_runME directory\n",
" run_file = 'bSub_runMe/imerg_download_' + str(year) + '.bash' \n",
" # create empty array to hold the lines\n",
" lines = [] \n",
" # this is internal command indicateing it ia a bash file\n",
" lines.append('#!/bin/bash'+'\\n') \n",
" # job name\n",
" lines.append('#SBATCH --job-name=i' + str(year) +'\\n') \n",
" # time required to run job \n",
" lines.append('#SBATCH --time=0:15:00'+'\\n') \n",
" # number of nodes requested for the job\n",
" lines.append('#SBATCH --nodes=1'+'\\n') \n",
" # number of task allocated for each node\n",
" lines.append('#SBATCH --ntasks-per-node=1'+'\\n') \n",
" # RAM space requested for the job \n",
" lines.append('#SBATCH --mem=20gb'+'\\n') \n",
" # account name \n",
" lines.append('#SBATCH --account=geog014522'+'\\n') \n",
" # this is internal command for SLURM\n",
" lines.append('#cd $SLURM_SUBMIT_DIR'+'\\n') \n",
" # your home directory wher you run the job from\n",
" lines.append('cd /user/home/fp20123/'+' '+'\\n') \n",
" # which ever python you want to use\n",
" lines.append('module add lang/python/anaconda/3.7-2019.10'+' '+'\\n') \n",
" # this is if you using your own python environment\n",
" lines.append('source /user/home/fp20123/my_uavproject/bin/activate'+' '+'\\n') \n",
" # the python script you want to run\n",
" lines.append('python /user/home/fp20123/my_python_code.py ' + str(year) +'\\n') \n",
" # writing file\n",
" f = open(run_file, 'w') \n",
" for line in lines:\n",
" f.write(line)\n",
" # close file\n",
" f.close() \n",
" return 'done'"
]
},
{
"cell_type": "markdown",
"id": "89875c0c",
"metadata": {},
"source": [
"The above function helps us write the bash files with the necessary commands for the HPC job submission.\n",
"As you can see, most of the lines are common and similar required by the HPC, hence, we can use this function to write any bash file we need with little modification. An example of the bash file produced using this function is shown below.\n",
"\n",
"
\n",
"
\n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"\n",
"This will be what is written in the imerg_download_2012.bash according to the example. \n",
"\n",
"If there are multiple jobs that use similar python scripts, then what you have to do is run above function to write the bash files with same ***suffix*** and a differetiating ***prefix*** so that we can submit it at the same time. In the above example we can see the file name is **imerg_download_year.bash** here the imerg_download is the prefix while the year is suffix. Now we can write any number of years we want to download (e.g imerg_download_2018.bash, imerg_download_2019.bash, ...). Once we prepare these files, then we are ready to submit multiple jobs to the HPC.\n",
"\n",
"### 1.3 Submitting multiple jobs\n",
"Once we prepare the bash files containing the jobs we want to run on HPC next is to use a little shell script to submit the jobs. The shell script is given as **runBashFiles.sh**. The file contains a few lines of code that allows to read all the bash files we prepare and sumbit it to the HPC. Here is an example file looks like\n",
"\n",
"\n",
"
\n",
"
\n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"\n",
"In the above image as you can see from line 5, all the files that start with **imerg_download_** will be submited to the HPC and the files will be moved to the **bSub_doneME** directory we create earlier (Line 7).\n",
"\n",
"Line 6 is where you see the ***.error*** and ***.out*** files that we discussed above to put any error message and output message from the job will be printed and svaed in the **bSub_logME** directory. "
]
},
{
"cell_type": "markdown",
"id": "672d80eb",
"metadata": {},
"source": [
"---\n",
"## EXERCISE 3 \n",
"Based on the above example please try to do the following\n",
"\n",
" 1. Write a python script that calculates area of a circle with radius as an input argument. \n",
" 2. Print the area of the circle as an output.\n",
" 3. Write 5 bash files with diffrent radius values.\n",
" 4. Submit the job to the HPC.\n",
" 5. Check the results in the output file.\n",
"--- "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "de967eee",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
![](\"../../../fig/CardiffUniversity.PNG\"align=\"center\")
\n",
" ![](\"../../../fig/Down2Earth.PNG\"align=\"center\"width=\"250\"/)
\n",
" ![](\"../../../fig/UniversityOfBristol.PNG\"align=\"center\"width=\"250\"/)
\n",
" ![](\"../../../fig/bashfile.png\"align=\"center\")
\n",
" ![](\"../../../fig/shellfile.png\"align=\"center\")
\n",
"