Web Application of EpyMix

example/EpyMix with Septoria.ipynb

@@ -544,7 +544,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -558,7 +558,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,

example/app_epymix.ipynb

@@ -0,0 +1,306 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b50cd883",
+   "metadata": {},
+   "source": [
+    "# Jupyter App"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e05e4afa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import ipywidgets\n",
+    "\n",
+    "from epymix.rain import rain as _rain \n",
+    "from epymix.inoculum import inoculum \n",
+    "from epymix.configuration import configuration\n",
+    "from epymix.SEIR import SEIR \n",
+    "from epymix.dispersion_gradient import dispersion_kernel_rust, dispersion_kernel_septo\n",
+    "from epymix.growth_companion import growth_pois\n",
+    "from epymix.SEIR_practices_data import SEIR_practice\n",
+    "from epymix.app_parameter_generator import parameter_set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "51d30dca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#disease_par='septo_par'\n",
+    "delta_t0 = 10 # constant, the model has been parameterise such as t = 10 degree-day\n",
+    "delta_t = 10 # time step\n",
+    "n_season = 1 # number of season\n",
+    "season = 250*int(delta_t0/delta_t)  # 2500 dd %% length of a cropping season\n",
+    "delta_companion = 0 # growth start lag of the companion crop (dd), negative: companion before, positive: companion after but for i<delta Poi=0\n",
+    "\n",
+    "Lr=1 # Lr: number of seasons\n",
+    "Lx=1 # Lx: number of patch along the x-axis\n",
+    "Ly=1 # Ly: number of patch along the y-axis\n",
+    "scenario_rot='uniform' # scenario_rot: rotation scenario (chose: uniform, random, chessboard, alternate, alternate_rank, etc)\n",
+    "wheat_fraction=0.5 # wheat_fraction: wheat fraction within each patch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6006cef7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mu_wheat = 0.03 * delta_t / delta_t0  # 0.03 %% mortality rate of S and E tissues (LAI/10dd)\n",
+    "nu = 0.03 * delta_t / delta_t0  # nu = mu %% mortality of I infectious tissues (LAI/10dd)\n",
+    "mu_companion = 0.03 * delta_t / delta_t0  # 0.03 %% mortality rate of the companion species (LAI/10dd)\n",
+    "beta_wheat = 0.09 * delta_t / delta_t0  # 0.09 %% wheat growth parameter (LAI/10dd)\n",
+    "beta_companion = 0.09 * delta_t / delta_t0  # beta_companion = beta_wheat %% growth parameter of the companion crop (LAI/10dd)\n",
+    "end_wheat = 140 * int(delta_t0 / delta_t)  # 1400 dj %% date of wheat growth end\n",
+    "end_companion = 140 * int(delta_t0 / delta_t)  # end_companion = end_wheat %% date of growth end for the companion crop\n",
+    "LAI_K = 6  # 6 %% carrying capacity (Maximum canopy LAI)\n",
+    "ber_wheat = 1  # 1 # wheat spore interception coefficient (the Beer-Lambert law)\n",
+    "ber_companion = 1  # 1 # spore interception coefficient of the companion species (the Beer-Lambert law)\n",
+    "h_wheat = 1  # wheat height\n",
+    "h_companion = 1  # companion height"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bceb8777",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year=2000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cdb4ac11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scenario_ino = 'initial_inoculum' # scenario_ino: which scenario of initial inoculum (chose: central_focus, initial_inoculum, annual_cloud)\n",
+    "frac_inf = 1 # frac_inf: fraction of patches within the field inoculated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "92d65c0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### PARAMETERS IN CASE OF PRACTICE DATA\n",
+    "data_lai_wheat = pd.read_csv('spline_coordinate_inc_wheat_10.csv', sep=';')\n",
+    "data_lai_comp = pd.read_csv('spline_coordinate_inc_comp_10.csv', sep=';')\n",
+    "data_lai_wheat_mu = pd.read_csv('spline_coordinate_coef_wheat_10.csv', sep=';')\n",
+    "experiment = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ae20eb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def app(crop='pure crop', disease_par='septo_par', year='2000', wheat_fraction=0.5, delta_companion = 0, view='LAIwheat'):\n",
+    "def app(crop='pure crop', disease_par='septo_par', experiment=5, year=2000, wheat_fraction=0.5, delta_companion = 0):\n",
+    "    disease, delta_ei, lambd, s0, pi_inf0, rho, psi, gamma, theta, sigma, sigma_asco, inf_begin, inoc_init_abs, ng_ext0_abs, day_length, alpha_asco, radius_asco, alpha_pycnid, radius_pycnid, alpha_ure, radius_ure = parameter_set(disease_par=disease_par, delta_t0=delta_t0, delta_t=delta_t)\n",
+    "\n",
+    "    arrangement = configuration(Lr, Lx, Ly, scenario_rot, wheat_fraction)\n",
+    "    arrangement_max = configuration(Lr, Lx, Ly, scenario_rot, wheat_fraction=1)\n",
+    "\n",
+    "    rain = _rain(year, n_season, delta_t)\n",
+    "    \n",
+    "    inoc_init, ng_ext0 = inoculum(scenario_ino, Lx, Ly, frac_inf, inoc_init_abs, ng_ext0_abs)\n",
+    "    \n",
+    "    kernel_asco, C_Disp_asco, kernel_pycnid, C_Disp_pycnid = dispersion_kernel_septo(day_length, alpha_asco, radius_asco, alpha_pycnid, radius_pycnid)\n",
+    "    kernel_ure, C_Disp_ure = dispersion_kernel_rust(day_length, alpha_ure, radius_ure)\n",
+    "    \n",
+    "    if crop == 'pure crop':\n",
+    "        t = n_season*250*int(delta_t0/delta_t) # length of the experiment\n",
+    "        \n",
+    "        Pth_inde, Poi_inde = growth_pois(t=t, season=season, arrangement=arrangement,\n",
+    "                    mu_companion=mu_companion, beta_companion=beta_companion, end_companion=end_companion, LAI_K=LAI_K)\n",
+    "\n",
+    "        Nsp, Pth, Poi, Sth, Sus, Lat, Ifc, Ifv, Rem, LAI, LAI_wheat, Poo, Eps, AUDPC, Scont = SEIR(\n",
+    "        t=t, \n",
+    "        delta_t0=delta_t0, delta_t=delta_t, \n",
+    "        season=season, delta_companion=delta_companion,\n",
+    "        disease=disease, rain=rain, arrangement=arrangement, inoc_init=inoc_init, ng_ext0=ng_ext0,\n",
+    "        mu_wheat=mu_wheat, nu=nu, beta_wheat=beta_wheat, #beta_companion=beta_companion, end_companion=end_companion\n",
+    "        end_wheat=end_wheat, LAI_K=LAI_K, ber_wheat=ber_wheat, ber_companion=ber_companion, Pth_inde=Pth_inde, Poi_inde=Poi_inde,\n",
+    "        h_wheat=h_wheat, h_companion=h_companion,\n",
+    "        lambd=lambd, delta_ei=delta_ei,\n",
+    "        s0=s0,pi_inf0=pi_inf0, rho=rho, psi=psi, gamma=gamma, theta=theta, sigma=sigma, sigma_asco=sigma_asco, inf_begin=inf_begin,\n",
+    "        C_Disp_ure=C_Disp_ure, kernel_ure=kernel_ure, C_Disp_asco=C_Disp_asco, kernel_asco=kernel_asco,\n",
+    "        C_Disp_pycnid=C_Disp_pycnid, kernel_pycnid=kernel_pycnid)\n",
+    "\n",
+    "        Nsp2, Pth2, Poi2, Sth2, Sus2, Lat2, Ifc2, Ifv2, Rem2, LAI2, LAI_wheat2, Poo2, Eps2, AUDPC2, Scont2 = SEIR(\n",
+    "        t=t, \n",
+    "        delta_t0=delta_t0, delta_t=delta_t, \n",
+    "        season=season, delta_companion=delta_companion,\n",
+    "        disease=disease, rain=rain, arrangement=arrangement_max, inoc_init=inoc_init, ng_ext0=ng_ext0,\n",
+    "        mu_wheat=mu_wheat, nu=nu, beta_wheat=beta_wheat, #beta_companion=beta_companion, end_companion=end_companion\n",
+    "        end_wheat=end_wheat, LAI_K=LAI_K, ber_wheat=ber_wheat, ber_companion=ber_companion, Pth_inde=Pth_inde, Poi_inde=Poi_inde,\n",
+    "        h_wheat=h_wheat, h_companion=h_companion,\n",
+    "        lambd=lambd, delta_ei=delta_ei,\n",
+    "        s0=s0,pi_inf0=pi_inf0, rho=rho, psi=psi, gamma=gamma, theta=theta, sigma=sigma, sigma_asco=sigma_asco, inf_begin=inf_begin,\n",
+    "        C_Disp_ure=C_Disp_ure, kernel_ure=kernel_ure, C_Disp_asco=C_Disp_asco, kernel_asco=kernel_asco,\n",
+    "        C_Disp_pycnid=C_Disp_pycnid, kernel_pycnid=kernel_pycnid)\n",
+    "\n",
+    "    elif crop == 'intercropping':\n",
+    "        data_lai_wheat.columns.values[experiment]\n",
+    "        lai_wheat_inc = np.array(data_lai_wheat.iloc[:, experiment])\n",
+    "        lai_comp_inc = np.array(data_lai_comp.iloc[:, experiment])\n",
+    "        lai_wheat_mu = np.array(data_lai_wheat_mu.iloc[:, experiment])\n",
+    "        t = sum(~np.isnan(lai_comp_inc))\n",
+    "        Nsp, Pth, Poi, Sth, Sus, Lat, Ifc, Ifv, Rem, LAI, LAI_wheat, Poo, Eps, AUDPC, Scont = \\\n",
+    "        SEIR_practice(t=t , delta_t0=delta_t0, delta_t=delta_t, season=season,\n",
+    "        disease=disease, rain=rain, arrangement=arrangement, inoc_init=inoc_init, ng_ext0=ng_ext0,\n",
+    "        lai_wheat_inc=lai_wheat_inc, lai_comp_inc=lai_comp_inc, lai_wheat_mu=lai_wheat_mu,\n",
+    "        nu=nu, ber_wheat=ber_wheat, ber_companion=ber_companion, h_wheat=h_wheat, h_companion=h_companion,\n",
+    "        lambd=lambd, delta_ei=delta_ei,\n",
+    "        s0=s0,pi_inf0=pi_inf0, rho=rho, psi=psi, gamma=gamma, theta=theta, sigma=sigma, sigma_asco=sigma_asco, inf_begin=inf_begin,\n",
+    "        C_Disp_ure=C_Disp_ure, kernel_ure=kernel_ure, C_Disp_asco=C_Disp_asco, kernel_asco=kernel_asco,\n",
+    "        C_Disp_pycnid=C_Disp_pycnid, kernel_pycnid=kernel_pycnid)\n",
+    "\n",
+    "        lai_comp_inc = np.array(data_lai_comp.iloc[:, experiment])*0\n",
+    "        Nsp2, Pth2, Poi2, Sth2, Sus2, Lat2, Ifc2, Ifv2, Rem2, LAI2, LAI_wheat2, Poo2, Eps2, AUDPC2, Scont2 = \\\n",
+    "        SEIR_practice(t=t , delta_t0=delta_t0, delta_t=delta_t, season=season,\n",
+    "        disease=disease, rain=rain, arrangement=arrangement, inoc_init=inoc_init, ng_ext0=ng_ext0,\n",
+    "        lai_wheat_inc=lai_wheat_inc, lai_comp_inc=lai_comp_inc, lai_wheat_mu=lai_wheat_mu,\n",
+    "        nu=nu, ber_wheat=ber_wheat, ber_companion=ber_companion, h_wheat=h_wheat, h_companion=h_companion,\n",
+    "        lambd=lambd, delta_ei=delta_ei,\n",
+    "        s0=s0,pi_inf0=pi_inf0, rho=rho, psi=psi, gamma=gamma, theta=theta, sigma=sigma, sigma_asco=sigma_asco, inf_begin=inf_begin,\n",
+    "        C_Disp_ure=C_Disp_ure, kernel_ure=kernel_ure, C_Disp_asco=C_Disp_asco, kernel_asco=kernel_asco,\n",
+    "        C_Disp_pycnid=C_Disp_pycnid, kernel_pycnid=kernel_pycnid)\n",
+    "\n",
+    "    \n",
+    "    T = [*range(0,t)]\n",
+    "    Sth = np.mean(Sth, axis=(1,2))\n",
+    "    Pth = np.mean(Pth, axis=(1,2))\n",
+    "    Poi = np.mean(Poi, axis=(1,2))\n",
+    "    Lat = np.mean(Lat, axis=(1,2))\n",
+    "    Ifc = np.mean(Ifc, axis=(1,2))\n",
+    "    Ifv = np.mean(Ifv, axis=(1,2))\n",
+    "    Sus = np.mean(Sus, axis=(1,2))\n",
+    "    Rem = np.mean(Rem, axis=(1,2))\n",
+    "    Nsp = np.mean(Nsp, axis=(1,2))\n",
+    "    LAI = np.mean(LAI, axis=(1,2))\n",
+    "    LAI_wheat = np.mean(LAI_wheat, axis=(1,2))\n",
+    "    Poo = np.mean(Poo, axis=(1,2))\n",
+    "    AUDPC = np.mean(AUDPC, axis=(1,2))\n",
+    "    Eps = np.mean(Eps, axis=(1,2))\n",
+    "    Scont = np.mean(Scont, axis=(1,2))\n",
+    "    \n",
+    "    # if view == 'LAIwheat':\n",
+    "    #     plt.plot(T, Sth, color='black')\n",
+    "    #     plt.ylim([0, 6])\n",
+    "    # elif view=='Desease':\n",
+    "    #     plt.plot(T, Ifc, color='red')\n",
+    "    #     plt.ylim([0, 6])\n",
+    "    # elif view=='All':\n",
+    "    plt.plot(T, Sth, color='black')\n",
+    "    plt.plot(T, Sus, color='green')\n",
+    "    plt.plot(T, Poi, color='brown')\n",
+    "    plt.plot(T, Ifv, color='red')\n",
+    "    plt.ylim([0, 6])\n",
+    "    plt.xlabel(\"Degrés-jour (joursx10 par °C)\")\n",
+    "    plt.ylabel(\"Surface foliaire (m² par m² de sol\")\n",
+    "    plt.plot(T, Sth, \"black\", label=\"Blé sans maladie\")\n",
+    "    plt.plot(T, Sus, \"green\", label=\"Blé avec maladie\")\n",
+    "    plt.plot(T, Poi, \"brown\", label=\"Compagne\")\n",
+    "    plt.plot(T, Ifv, \"red\", label=\"Maladie cumulée\")\n",
+    "    plt.legend(loc=\"upper left\")\n",
+    "    # plt.ylabel(\"LAI\")\n",
+    "\n",
+    "    \n",
+    "    print(f'AUDPC: {\"{:.2f}\".format(AUDPC[-1])}')\n",
+    "    print(f'Réduction de la maladie: {\"{:.2f}\".format(float(1-AUDPC[-1]/AUDPC2[-1]))}%')\n",
+    "     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "533d9549",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5219427c73bf494ca097101a446bb8e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='crop', options=('pure crop', 'intercropping'), value='pure crop'),…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.app(crop='pure crop', disease_par='septo_par', experiment=5, year=2000, wheat_fraction=0.5, delta_companion=0)>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ipywidgets.interact(app, crop=['pure crop', 'intercropping'], experiment=[('fababean_N0',61),('fababean_N1',63),('pea_N0',13),('pea_N1',14)], disease_par=['septo_par', 'rust_par'], year=list(range(1993, 2016)), wheat_fraction=(0,1,0.05), \n",
+    "delta_companion=(-100,100,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d0d90ec",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "epyland",
+   "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": "3.9.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cc495b77a574b98b57b7f5732d92c368da0bdafbfc1ca32d8caf81cd26f9b127"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}