{ "cells": [ { "cell_type": "markdown", "id": "ebbf2dba-77ab-4733-9b00-36603f33606b", "metadata": {}, "source": [ "# **INGESTING PDFs (Probability Density Functions)** #\n", "\n", "#### Objectives: ####\n", "+ Getting familiar on how [STORM](../README.md) reads input data\n", "+ Recipes for plotting statistics (via [Seaborn](https://seaborn.pydata.org/) and [Matplotlib](https://matplotlib.org/))\n", "\n", "---" ] }, { "cell_type": "markdown", "id": "f965856d-bbb6-407c-aea0-2d044049dc12", "metadata": {}, "source": [ "The cornerstone of STORM is its stochastic framework which is built upon probability density functions (PDFs).\n", "A PDF is a mathematical conduit that allows the modeling of (measurable) random variables.\n", "STORM simulates storm throughout the following random (natural) variables:\n", "+ ```TOTALP``` - total seasonal rainfall\n", "+ ```RADIUS``` - maximum storm radius\n", "+ ```BETPAR``` - decay rate (of maximum rainfall from the storm's centre towards its maximum radius)\n", "+ ```MAXINT``` - (maximum) rainfall intensity\n", "+ ```AVGDUR``` - (average) storm duration\n", "+ ```COPULA``` - copula's correlation parameter\n", "+ ```WINDIR``` - storm's core direction\n", "+ ```WSPEED``` - storm's advection velocity\n", "+ ```DOYEAR``` - storm's starting date\n", "+ ```DATIME``` - storm's starting time\n", "\n", "The aforementioned variables are modeled via several (families of) PDFs.\n", "Below there's an excerpt of the [PDFs-file](../model_input/ProbabilityDensityFunctions_TWO.csv) that contains the parameters defining such PDFs.\\\n", "*Caveat: The parameters here presented do not correspond to the actual hydrometeorology of the HAD (Horn-of-Africa Drylands) region.\n", "For the present exercise they're just numbers that enables running the code/script(s).*\n", "\n", "```\n", "TOTALP_PDF1+gumbel_l,5.511633973257439,0.2262071569584096\n", "RADIUS_PDF1+johnsonsb,1.5187255885489825,1.269645240869123,-0.27894731916038973,20.797740488039622\n", "BETPAR_PDF1+exponnorm,8.287219232100437,0.017846553384616465,0.010045344544579145\n", "MAXINT_PDF1+expon,0.10575951772499326,6.995586774590339\n", "AVGDUR_PDF1+geninvgauss,-0.0898831096228784,0.7703680797104097,2.843259631396887,82.07861011421329\n", "COPULA_RHO1+,-0.31622002749035444\n", "WINDIR_PDF1+vonmises,2.836341,0.00001\n", "WSPEED_PDF1+norm,7.55,1.9\n", "DATIME_VMF1+m1,0.2468419551426534,0.6893273101866058,6.418276632248997\n", "DATIME_VMF1+m2,0.3315907829508499,1.703495074238532,3.000253688430609\n", "DATIME_VMF1+m3,0.4215672619064808,2.57569021558914,0.4649344363713587\n", "DOYEAR_VMF1+m1,0.05446271224933757,1.907409236571684,105.3227046311876\n", "DOYEAR_VMF1+m2,0.08954689198507723,0.2286006656480273,51.97011694272198\n", "DOYEAR_VMF1+m3,0.07054990890263783,1.551363989955984,87.19112504916305\n", "DOYEAR_VMF1+m4,0.0873236680883794,1.17264771241493,52.91419173073581\n", "DOYEAR_VMF1+m5,0.6981168187745506,0.5586619737368196,6.828147043994408\n", "```\n", "\n", "In the previous segment of, for instance, the first line defines the modeling of total seasonal rainfall ```TOTALP```, for Season 1 (in the logarithmic-space), as a [left-skewed Gumbel](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gumbel_l.html#scipy.stats.gumbel_l) distribution with $\\mu \\approx 5.51$ and $\\sigma^2 \\approx 0.266$.\\\n", "*Ideally, the PDF-file is produced by a pre-pocessing script (still in development --as of this date--), and/or can be directly modified/overwritten with your own parameterization (if you now what you're doing).*\n", "\n", "---" ] }, { "cell_type": "markdown", "id": "6cb32805-1958-467d-a237-342426019620", "metadata": {}, "source": [ "The retrieveal, setting, and sampling of PDFs is done by STORM through the functions: READ_PDF_PAR, RETRIEVE_PDF, CHECK_PDF, RANDOM_SAMPLING, TRUNCATED_SAMPLING, SEASONAL_RAIN, ENHANCE_SR, COPULA_SAMPLING, TOD_CIRCULAR, and TOD_DISCRETE of the [rainfall.py](../rainfall.py) module.\n", "\n", "---" ] }, { "cell_type": "markdown", "id": "63b71fc3-0fb9-48a2-aba0-229ec929f2bf", "metadata": {}, "source": [ "## HANDLING JUST ONE PDF AT A TIME ##\n", "\n", "The exercise below is about constructing and visualizing just one PDF (i.e., ```RADIUS``` - maximum storm radius).\\\n", "*You are encouraged to find what the (first half of the) other PDFs look like.*" ] }, { "cell_type": "code", "execution_count": 62, "id": "b20d2aa1-3ef6-4762-b708-7568bb769ae3", "metadata": {}, "outputs": [], "source": [ "# loading libraries\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "from scipy import stats" ] }, { "cell_type": "markdown", "id": "dd70d9a6-c356-4b8c-9de3-54bf5de999d4", "metadata": {}, "source": [ "Let's read the [PDFs-file](../model_input/ProbabilityDensityFunctions_TWO.csv).\n", "It comes with two seasons but we'll use just one." ] }, { "cell_type": "code", "execution_count": 63, "id": "a81ef430-e06c-45bc-b982-b11d578f1e5b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 1 2 3 4\n", "0 \n", "TOTALP_PDF1+gumbel_l 5.511634 0.226207 NaN NaN\n", "TOTALP_PDF2+norm 5.362910 0.316761 NaN NaN\n", "RADIUS_PDF1+johnsonsb 1.518726 1.269645 -0.278947 20.797740\n", "RADIUS_PDF2+gamma 4.399626 -0.475113 1.399112 NaN\n", "BETPAR_PDF1+exponnorm 8.287219 0.017847 0.010045 NaN\n", "BETPAR_PDF2+burr 2.351236 0.850598 -0.001137 0.083777\n", "MAXINT_PDF1+expon 0.105760 6.995587 NaN NaN\n", "AVGDUR_PDF1+geninvgauss -0.089883 0.770368 2.843260 82.078610\n", "MAXINT_PDF1+Z1+expon 0.109404 5.760539 NaN NaN\n", "MAXINT_PDF1+Z2+expon 0.105760 7.113553 NaN NaN\n", "MAXINT_PDF1+Z3+expon 0.305323 7.352659 NaN NaN\n", "AVGDUR_PDF1+Z1+geninvgauss -0.105857 0.609322 5.046340 74.205047\n", "AVGDUR_PDF1+Z2+geninvgauss -0.083949 0.811979 2.380416 83.779999\n", "AVGDUR_PDF1+Z3+fisk 1.434418 10.177944 57.545228 NaN\n", "COPULA_RHO1+ -0.316220 NaN NaN NaN\n", "COPULA_RHO1+Z1 -0.276457 NaN NaN NaN\n", "COPULA_RHO1+Z2 -0.312464 NaN NaN NaN\n", "COPULA_RHO1+Z3 -0.440389 NaN NaN NaN\n", "MAXINT_PDF2+expon 0.105760 6.995587 NaN NaN\n", "AVGDUR_PDF2+geninvgauss -0.089883 0.770368 2.843260 82.078610\n", "MAXINT_PDF2+Z1+expon 0.109404 5.760539 NaN NaN\n", "MAXINT_PDF2+Z2+expon 0.105760 7.113553 NaN NaN\n", "MAXINT_PDF2+Z3+expon 0.305323 7.352659 NaN NaN\n", "AVGDUR_PDF2+Z1+geninvgauss -0.105857 0.609322 5.046340 74.205047\n", "AVGDUR_PDF2+Z2+geninvgauss -0.083949 0.811979 2.380416 83.779999\n", "AVGDUR_PDF2+Z3+fisk 1.434418 10.177944 57.545228 NaN\n", "COPULA_RHO2+ -0.316220 NaN NaN NaN\n", "COPULA_RHO2+Z1 -0.276457 NaN NaN NaN\n", "COPULA_RHO2+Z2 -0.312464 NaN NaN NaN\n", "COPULA_RHO2+Z3 -0.440389 NaN NaN NaN\n", "DATIME_VMF1+m1 0.246842 0.689327 6.418277 NaN\n", "DATIME_VMF1+m2 0.331591 1.703495 3.000254 NaN\n", "DATIME_VMF1+m3 0.421567 2.575690 0.464934 NaN\n", "DATIME_VMF2+m1 1.000000 1.444014 1.054371 NaN\n", "DOYEAR_VMF1+m1 0.054463 1.907409 105.322705 NaN\n", "DOYEAR_VMF1+m2 0.089547 0.228601 51.970117 NaN\n", "DOYEAR_VMF1+m3 0.070550 1.551364 87.191125 NaN\n", "DOYEAR_VMF1+m4 0.087324 1.172648 52.914192 NaN\n", "DOYEAR_VMF1+m5 0.698117 0.558662 6.828147 NaN\n", "DOYEAR_VMF2+m1 1.000000 0.713610 3.908576 NaN\n" ] } ], "source": [ "# https://stackoverflow.com/a/58227453/5885810 -> import tricky CSV\n", "file_pdfs = \"../model_input/ProbabilityDensityFunctions_TWO.csv\"\n", "PDFS = pd.read_fwf(file_pdfs, header=None)\n", "PDFS = PDFS.__getitem__(0).str.split(\",\", expand=True).set_index(0).astype(\"f8\")\n", "\n", "# how does it look like?\n", "print(PDFS)" ] }, { "cell_type": "markdown", "id": "2acd6cf4-a8bd-4cae-8c8e-d41acfdef06c", "metadata": {}, "source": [ "STORM re-shapes the above matrix, and coverts it (according to a given PDF-selection) into dictionaries of PDFs.\n", "Something like this:" ] }, { "cell_type": "code", "execution_count": 64, "id": "acfcd203-329d-4f0f-a1a7-d998bd0880fd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'': }\n", "{'': }\n" ] }, { "data": { "text/plain": [ "[None, None]" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# here the RADIUS-PDFs for the two season are stored in one list\n", "RADIUS = [\n", " {\"\": stats.johnsonsb(1.5187, 1.2696, -0.2789, 20.7977)},\n", " {\"\": stats.gamma(4.3996, -0.475, 1.399)},\n", "]\n", "\n", "# how does it look like?\n", "list(map(print, RADIUS))" ] }, { "cell_type": "markdown", "id": "96e9ccd1-c14e-46a1-89d5-51b81126f4d0", "metadata": {}, "source": [ "Now we \"trace\" individually each PDF (just to see the variations in/on the PDFs.\n", "The selected/established PDFs are [johnsonsb](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.johnsonsb.html#scipy-stats-johnsonsb) (for season 1) and [gamma](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gamma.html#scipy-stats-gamma) (for season 2 --if we're tracking seasons--).\n", "[Scipy](https://docs.scipy.org/doc/scipy/index.html) is the library in charge of handling PDFs." ] }, { "cell_type": "code", "execution_count": 65, "id": "3c8ae6f9-1e83-4ee4-bb63-0807cd79ad4c", "metadata": {}, "outputs": [], "source": [ "johns = RADIUS[0][\"\"]\n", "gamma = RADIUS[1][\"\"]\n", "\n", "# seed... to have some consistency among runs\n", "np.random.seed(seed=42)\n", "# here is where the \"random\" sampling is done\n", "h_johns = pd.DataFrame({\"distribution\": \"johnsonsb\", \"samples\": johns.rvs(size=400)})\n", "h_gamma = pd.DataFrame({\"distribution\": \"gamma\", \"samples\": gamma.rvs(size=300)})" ] }, { "cell_type": "markdown", "id": "5284ba8c-345e-49bd-971c-7ad038ce0d6f", "metadata": {}, "source": [ "We then group the previous outputs into a [Pandas](https://pandas.pydata.org/docs/) dataframe, so we can easily plot them together later." ] }, { "cell_type": "code", "execution_count": 66, "id": "c2ea849c-656d-4981-a929-23e1dd52492e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " distribution samples\n", "0 johnsonsb 3.678668\n", "1 johnsonsb 10.664618\n", "2 johnsonsb 6.581684\n", "3 johnsonsb 5.316964\n", "4 johnsonsb 2.216708\n", ".. ... ...\n", "695 gamma 8.466893\n", "696 gamma 4.631129\n", "697 gamma 2.597868\n", "698 gamma 6.362148\n", "699 gamma 5.696321\n", "\n", "[700 rows x 2 columns]\n" ] } ], "source": [ "data = pd.concat([h_johns, h_gamma], ignore_index=True)\n", "\n", "# how does it look like?\n", "print(data)" ] }, { "cell_type": "markdown", "id": "95f19a56-1020-46e9-83f9-a7ae59bf9c30", "metadata": {}, "source": [ "### Visualization ###\n", "\n", "First, let's define/call some nice Font Types." ] }, { "cell_type": "code", "execution_count": 67, "id": "25b21846-f2f8-4417-b4c2-695ba8f648dc", "metadata": {}, "outputs": [], "source": [ "plt.rcParams.update(plt.rcParamsDefault)\n", "plt.rcParams[\"font.family\"] = \"Trebuchet MS\", \"Impact\", \"Candara\", \"Papyrus\"" ] }, { "cell_type": "markdown", "id": "8eb72995-d9ec-409b-ab5f-bb68e4ec7b84", "metadata": {}, "source": [ "Then, let's define some basic variables to be shared among the data/plot." ] }, { "cell_type": "code", "execution_count": 68, "id": "dbec30cb-9716-423d-a6b4-e2ea990a5969", "metadata": {}, "outputs": [], "source": [ "_size = 200 # -> number of bins to split the X.axis\n", "x_min = 0\n", "x_max = 20\n", "s_tep = abs(x_max - x_min) / _size # -> resolution of the X.axis\n", "x_axis = np.linspace(x_min, x_max, num=_size + 1, endpoint=True)" ] }, { "cell_type": "markdown", "id": "ce6fe3ef-fa0c-4d58-9bdc-a1bea75bc766", "metadata": {}, "source": [ "Now, let's \"compute\" the PDFs for the values in ```x_axis```." ] }, { "cell_type": "code", "execution_count": 69, "id": "c1655039-2f44-495a-9c1b-ca8e7098059d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.00078869 0.00255882 0.0056964 0.01022536 0.01600589 0.02281604\n", " 0.03040706 0.03853594 0.04698228 0.05555549 0.06409643 0.072476 ]\n", "[0.0012772 0.00227664 0.00365583 0.00544392 0.00765683 0.01029853\n", " 0.01336251 0.0168333 0.02068797 0.02489752 0.02942824 0.03424292]\n" ] } ], "source": [ "d_johns = johns.pdf(x_axis)\n", "d_gamma = gamma.pdf(x_axis)\n", "\n", "# what are 'd_johns' and 'd_gamma'?\n", "print(d_johns[:12])\n", "print(d_gamma[:12])" ] }, { "cell_type": "markdown", "id": "002b2fc9-96d5-47a0-980f-c01deeccc9fb", "metadata": {}, "source": [ "Finally, let's do the plotting... \n", "*(Color names taken from [Colors-XKCD](https://www.w3schools.com/colors/colors_xkcd.asp))*\\\n", "" ] }, { "cell_type": "code", "execution_count": 80, "id": "a5c79489-d952-45e7-80a9-aaf46c439d6c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# some more parameters...\n", "binw = 1\n", "xbin = np.linspace(x_min, x_max, num=int(abs(x_max - x_min) / binw + 1), endpoint=True)\n", "xlab = np.linspace(x_min, x_max, num=10 + 1, endpoint=True)\n", "\n", "# the plot starts here\n", "fig, ax = plt.subplots(figsize=(7, 5), dpi=150)\n", "\n", "# seaborn histogram\n", "# https://seaborn.pydata.org/generated/seaborn.histplot.html\n", "sns.histplot(\n", " data=data,\n", " x=\"samples\",\n", " hue=\"distribution\",\n", " stat=\"density\",\n", " ax=ax,\n", " multiple=\"layer\",\n", " alpha=0.53,\n", " bins=xbin, # binwidth=binw\n", " element=\"step\",\n", " fill=False,\n", " lw=1.1,\n", " ls=\"dashed\",\n", " palette={\"johnsonsb\": \"xkcd:water blue\", \"gamma\": \"xkcd:watermelon\"},\n", " common_norm=False,\n", " zorder=0,\n", " # edgecolor='xkcd:washed out green',\n", ")\n", "\n", "# PDF-curves\n", "# use this if stat=='density'\n", "ax.plot(x_axis, d_johns, color=\"xkcd:bright blue\", lw=1.7, ls=\"solid\", zorder=2)\n", "ax.plot(x_axis, d_gamma, color=\"xkcd:cerise\", lw=1.5, ls=\"solid\", zorder=1)\n", "\n", "# # use this if stat=='probability'\n", "# ax.plot(\n", "# x_axis,\n", "# d_johns / d_johns.sum() * binw / s_tep,\n", "# color=\"xkcd:bright blue\",\n", "# lw=1.7,\n", "# ls=\"solid\",\n", "# zorder=2,\n", "# )\n", "# ax.plot(\n", "# x_axis,\n", "# d_gamma / d_gamma.sum() * binw / s_tep,\n", "# color=\"xkcd:cerise\",\n", "# lw=1.5,\n", "# ls=\"solid\",\n", "# zorder=1,\n", "# )\n", "\n", "# custom legend\n", "ax.legend(\n", " labels=[\"gamma histogram\", \"johnsonsb histogram\", \"johnsonsb pdf\", \"gamma pdf\"],\n", " loc=\"upper right\",\n", " fontsize=9,\n", ")\n", "\n", "# tweaking axes-stuff\n", "ax.set_xticks(xlab)\n", "# turn xlabs into integer-strings\n", "ax.set_xticklabels(\n", " labels=[str(int(x)) for x in xlab], fontdict={\"size\": 9, \"color\": \"xkcd:charcoal\"}\n", ")\n", "ax.tick_params(\n", " axis=\"x\",\n", " which=\"major\",\n", " direction=\"out\",\n", " pad=+2,\n", " color=\"xkcd:weird green\",\n", " length=4,\n", " width=0.3,\n", ")\n", "ax.set_xlim([x_min - 0.5, x_max + 0.5])\n", "plt.xlabel(\n", " r\"Storm radius [km]\",\n", " fontsize=12,\n", " color=\"xkcd:black\",\n", " labelpad=9,\n", " fontname=\"Papyrus\",\n", ")\n", "\n", "# # activate this block for more fun-control\n", "# ax.set_yticks(np.linspace(0, 0.16, 5), minor=False)\n", "# ax.tick_params(\n", "# axis=\"y\",\n", "# which=\"major\",\n", "# direction=\"in\",\n", "# pad=1.5,\n", "# color=\"xkcd:charcoal\",\n", "# labelcolor=\"xkcd:charcoal\",\n", "# labelsize=11,\n", "# length=3,\n", "# )\n", "# plt.ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(1, -2))\n", "# # doing minor ticks\n", "# ax.set_yticks(np.linspace(0, 0.17, 18), labels=None, minor=True)\n", "# ax.tick_params(\n", "# axis=\"y\",\n", "# which=\"minor\",\n", "# direction=\"in\",\n", "# color=\"xkcd:charcoal\",\n", "# length=1.5,\n", "# width=0.5,\n", "# )\n", "# ax.set_ylim([0, 0.17])\n", "# plt.ylabel(\"density [-]\", fontsize=12, color=\"xkcd:black\", labelpad=13)\n", "\n", "# plt.title(\"some unnecessary title\")\n", "\n", "plt.show()\n", "\n", "# # use these for exporting and cleaning [don't forget to comment out \"plt.show()\"!!]\n", "# plt.savefig(\n", "# \"one_.pdf\", bbox_inches=\"tight\", pad_inches=0.02, facecolor=fig.get_facecolor()\n", "# )\n", "# plt.close()\n", "# plt.clf()" ] }, { "cell_type": "markdown", "id": "52ba809f-bbc0-4205-b0f3-069d41b9c061", "metadata": {}, "source": [ "I'd would ask then... what would the above graph look like if [**binw**](#binw-ref) is different from **1**??" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:prll]", "language": "python", "name": "conda-env-prll-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 5 }