Exp4_single_species_organization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXp4, spatioanl organization in a single-species population\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.ndimage as ndi\n",
    "from random import random\n",
    "import matplotlib\n",
    "from netCDF4 import Dataset\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt    # The code below assumes this convenient renaming\n",
    "import math\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "from BioMi_function_collection import *\n",
    "import sys \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time stepping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func_timestepping():\n",
    "    \"\"\"\n",
    "    Runs timestepping, but needs everything to have already been defined previously in the settings cell.\n",
    "    \n",
    "    Has discrete death (through demographics)\n",
    "    \"\"\"\n",
    "\n",
    "    #--- time loop ----\n",
    "    for t in range(ntim):\n",
    "\n",
    "\n",
    "        #--- size loop ---\n",
    "        for l in range(nsiz):\n",
    "\n",
    "            \n",
    "            #--- Object birthdays! Time-shift of the demographics levels ---\n",
    "            for i in range(nage-1,0,-1):\n",
    "                N[:,:,l,t,i] = N[:,:,l,t-1,i-1]   #note: oldest level is forgotten\n",
    "            \n",
    "            \n",
    "            #--- Probability of birth p ---\n",
    "            if pulse_growth_flag:\n",
    "                \n",
    "                dp_pulse[:,:] = 0.\n",
    "\n",
    "                #--- Adjust p under certain rules ---\n",
    "                \n",
    "                # Rule 1: pulsating growth\n",
    "                for i in range(len(x)):\n",
    "                    for j in range(len(y)):\n",
    "                        xr = np.array( range(i-nx_pulse,i+nx_pulse+1) )\n",
    "                        xr = np.where(xr < 0  , xr+nx, xr)\n",
    "                        xr = np.where(xr >= nx, xr-nx, xr)\n",
    "                        yr = np.array( range(j-ny_pulse,j+ny_pulse+1) )\n",
    "                        yr = np.where(yr < 0  , yr+ny, yr)\n",
    "                        yr = np.where(yr >= ny, yr-ny, yr)\n",
    "                        submesh = np.ix_( xr, yr )\n",
    "                        #print( submesh)\n",
    "                        dp_pulse[submesh] = dp_pulse[submesh] + f_pulse[:,:] * np.sum(N[i,j,l,t,:])\n",
    "                    \n",
    "\n",
    "                p[:,:,l,t] = p[:,:,l,t] + prob_sub * dp_pulse[:,:] * fac_pulse\n",
    "\n",
    "                # Rule 2: environmental deformation, so that average birth rate is conserved at prob_sub\n",
    "                if np.mean(p[:,:,l,t])>0.:\n",
    "                    #print( np.mean(p[:,:,l,t]) )\n",
    "                    fac_p = prob_sub / np.mean(p[:,:,l,t])\n",
    "                    p[:,:,l,t] = p[:,:,l,t] * fac_p\n",
    "\n",
    "\n",
    "            #--- Births ---\n",
    "            B = np.random.binomial(N_tot_ref[l], p[:,:,l,t].ravel(), nx*ny)\n",
    "            B = B.reshape([nx,ny])\n",
    "            # Warning! Need to make sure that the resizing is done correctly, so that x and y aren't swapped accidentally\n",
    "\n",
    "            \n",
    "            #--- Update object number ---\n",
    "            N[:,:,l,t,0] = B    # add births as level 1 demographics\n",
    "            \n",
    "            \n",
    "            print( \"t=%s  time = %s\" % (t, t*dtim),' sizebin' ,l,'max birth: ',np.max(B),np.max(N[:,:,l,t,0]))\n",
    "\n",
    "        \n",
    "    return N, p, dp_pulse\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Settings  and initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prob_sub=1e-08\n",
      "size=50.0 B0=2e-07 N_tot_ref=11999999.0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#--- Grid parameters ----------\n",
    "nx   = 100     #grid size\n",
    "ny   = 100\n",
    "nsiz = 1       #nr of sizes in spectrum\n",
    "\n",
    "#ntim = 10     #nr of timesteps\n",
    "#ntim = 12*60    #nr of timesteps  12 hours\n",
    "ntim = 24*60    #nr of timesteps  12 hours\n",
    "ntim = 200    #nr of timesteps  12 hours\n",
    "\n",
    "nage = 1        #dimension of thermal demographics: number of thermal age strata\n",
    "\n",
    "dx   = 100.    #x gridspacing     [m]\n",
    "dy   = 100.     #y gridspacing     [m]\n",
    "dsiz = 100.    #width of size bin [m]\n",
    "dtim = 60.     #time increment    [s]\n",
    "\n",
    "#--- Reference domain\n",
    "L_ref    = 1000000.   #size of very large reference domain  [m]\n",
    "dt_ref   = dtim       #timestep of very large ref domain    [s]\n",
    "\n",
    "#--- birth and lifetime constants --------\n",
    "B_0  = np.zeros(nsiz)\n",
    "B_0[:]    = 2.e-7   #average nr of object births / m2 / s\n",
    "\n",
    "tau_D     = 300.    #object life time  [s]\n",
    "tau_D_dt  = int(tau_D/dtim) #object life time in timesteps []\n",
    "\n",
    "nage = int(tau_D/dtim)   #demographics: number of object age strata\n",
    "\n",
    "#--- pulsating growth parameters\n",
    "pulse_growth_flag = 1\n",
    "r_pulse   = 300.    #radius of impact cone of pulsating growth  [m]\n",
    "#fac_pulse =   40.    #factor of proportionality in impact of pulsating growth on p\n",
    "fac_pulse = 2000.    \n",
    "\n",
    "#--- declare arrays -----------\n",
    "x    = ( np.array(range(nx  )) + 0.5) * dx\n",
    "y    = ( np.array(range(ny  )) + 0.5) * dy\n",
    "siz  = ( np.array(range(nsiz)) + 0.5) * dsiz\n",
    "tim  = ( np.array(range(ntim)) + 0.5) * dtim\n",
    "\n",
    "N        = np.zeros( (nx,ny,nsiz,ntim,nage) ).astype(int)    #array containing object number\n",
    "p        = np.empty( (nx,ny,nsiz,ntim) )    #array containing object birth probability\n",
    "dp_pulse = np.empty( (nx,ny) )    #perturbation in p due to pulsating growth (rule 1)\n",
    "\n",
    "\n",
    "#--- binomial probability values -----------\n",
    "N_tot_ref = np.zeros(nsiz)\n",
    "N_tot_ref[:] = (B_0[:]*L_ref*L_ref*dt_ref).astype(int)  #total nr of objects born in reference domain\n",
    "#print( N_tot_ref[0] )\n",
    "\n",
    "prob_sub = dx*dy*dtim/(dt_ref*L_ref*L_ref)     #probability that one of N_tot_ref births happens in a gridbox\n",
    "print( \"prob_sub=%s\" % (prob_sub) )\n",
    "\n",
    "p[:,:,:,:] = prob_sub\n",
    "\n",
    "for i in range(len(siz)):\n",
    "    print( \"size=%s B0=%s N_tot_ref=%s\" % (siz[i],B_0[i],N_tot_ref[i]) )\n",
    "\n",
    "    \n",
    "#--- set up pulsating growth stuff ---   \n",
    "print(pulse_growth_flag)\n",
    "\n",
    "if pulse_growth_flag:\n",
    "    nx_pulse = np.int( r_pulse / dx)\n",
    "    ny_pulse = np.int( r_pulse / dy)\n",
    "\n",
    "    f_pulse  = np.empty( (2 * nx_pulse + 1, 2 * ny_pulse + 1) )  \n",
    "\n",
    "    for i in range(f_pulse.shape[0]):\n",
    "        for j in range(f_pulse.shape[0]):\n",
    "            f_pulse[i,j] = np.max( [0., 1. - ( (dx * (i - nx_pulse))**2 + (dy * (j - ny_pulse))**2 )**0.5 / r_pulse ] )\n",
    "            \n",
    "\n",
    "#Setting the seed so things are always the same\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now the fun starts!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t=0  time = 0.0  sizebin 0 max birth:  3 3\n",
      "t=1  time = 60.0  sizebin 0 max birth:  3 3\n",
      "t=2  time = 120.0  sizebin 0 max birth:  3 3\n",
      "t=3  time = 180.0  sizebin 0 max birth:  4 4\n",
      "t=4  time = 240.0  sizebin 0 max birth:  3 3\n",
      "t=5  time = 300.0  sizebin 0 max birth:  3 3\n",
      "t=6  time = 360.0  sizebin 0 max birth:  4 4\n",
      "t=7  time = 420.0  sizebin 0 max birth:  3 3\n",
      "t=8  time = 480.0  sizebin 0 max birth:  3 3\n",
      "t=9  time = 540.0  sizebin 0 max birth:  3 3\n",
      "t=10  time = 600.0  sizebin 0 max birth:  3 3\n",
      "t=11  time = 660.0  sizebin 0 max birth:  3 3\n",
      "t=12  time = 720.0  sizebin 0 max birth:  4 4\n",
      "t=13  time = 780.0  sizebin 0 max birth:  4 4\n",
      "t=14  time = 840.0  sizebin 0 max birth:  4 4\n",
      "t=15  time = 900.0  sizebin 0 max birth:  3 3\n",
      "t=16  time = 960.0  sizebin 0 max birth:  3 3\n",
      "t=17  time = 1020.0  sizebin 0 max birth:  3 3\n",
      "t=18  time = 1080.0  sizebin 0 max birth:  3 3\n",
      "t=19  time = 1140.0  sizebin 0 max birth:  4 4\n",
      "t=20  time = 1200.0  sizebin 0 max birth:  3 3\n",
      "t=21  time = 1260.0  sizebin 0 max birth:  4 4\n",
      "t=22  time = 1320.0  sizebin 0 max birth:  3 3\n",
      "t=23  time = 1380.0  sizebin 0 max birth:  3 3\n",
      "t=24  time = 1440.0  sizebin 0 max birth:  3 3\n",
      "t=25  time = 1500.0  sizebin 0 max birth:  4 4\n",
      "t=26  time = 1560.0  sizebin 0 max birth:  4 4\n",
      "t=27  time = 1620.0  sizebin 0 max birth:  3 3\n",
      "t=28  time = 1680.0  sizebin 0 max birth:  3 3\n",
      "t=29  time = 1740.0  sizebin 0 max birth:  3 3\n",
      "t=30  time = 1800.0  sizebin 0 max birth:  4 4\n",
      "t=31  time = 1860.0  sizebin 0 max birth:  3 3\n",
      "t=32  time = 1920.0  sizebin 0 max birth:  3 3\n",
      "t=33  time = 1980.0  sizebin 0 max birth:  3 3\n",
      "t=34  time = 2040.0  sizebin 0 max birth:  3 3\n",
      "t=35  time = 2100.0  sizebin 0 max birth:  3 3\n",
      "t=36  time = 2160.0  sizebin 0 max birth:  4 4\n",
      "t=37  time = 2220.0  sizebin 0 max birth:  3 3\n",
      "t=38  time = 2280.0  sizebin 0 max birth:  3 3\n",
      "t=39  time = 2340.0  sizebin 0 max birth:  3 3\n",
      "t=40  time = 2400.0  sizebin 0 max birth:  3 3\n",
      "t=41  time = 2460.0  sizebin 0 max birth:  4 4\n",
      "t=42  time = 2520.0  sizebin 0 max birth:  3 3\n",
      "t=43  time = 2580.0  sizebin 0 max birth:  4 4\n",
      "t=44  time = 2640.0  sizebin 0 max birth:  3 3\n",
      "t=45  time = 2700.0  sizebin 0 max birth:  3 3\n",
      "t=46  time = 2760.0  sizebin 0 max birth:  3 3\n",
      "t=47  time = 2820.0  sizebin 0 max birth:  3 3\n",
      "t=48  time = 2880.0  sizebin 0 max birth:  4 4\n",
      "t=49  time = 2940.0  sizebin 0 max birth:  3 3\n",
      "t=50  time = 3000.0  sizebin 0 max birth:  4 4\n",
      "t=51  time = 3060.0  sizebin 0 max birth:  3 3\n",
      "t=52  time = 3120.0  sizebin 0 max birth:  4 4\n",
      "t=53  time = 3180.0  sizebin 0 max birth:  3 3\n",
      "t=54  time = 3240.0  sizebin 0 max birth:  3 3\n",
      "t=55  time = 3300.0  sizebin 0 max birth:  4 4\n",
      "t=56  time = 3360.0  sizebin 0 max birth:  3 3\n",
      "t=57  time = 3420.0  sizebin 0 max birth:  4 4\n",
      "t=58  time = 3480.0  sizebin 0 max birth:  3 3\n",
      "t=59  time = 3540.0  sizebin 0 max birth:  3 3\n",
      "t=60  time = 3600.0  sizebin 0 max birth:  5 5\n",
      "t=61  time = 3660.0  sizebin 0 max birth:  3 3\n",
      "t=62  time = 3720.0  sizebin 0 max birth:  3 3\n",
      "t=63  time = 3780.0  sizebin 0 max birth:  3 3\n",
      "t=64  time = 3840.0  sizebin 0 max birth:  4 4\n",
      "t=65  time = 3900.0  sizebin 0 max birth:  4 4\n",
      "t=66  time = 3960.0  sizebin 0 max birth:  4 4\n",
      "t=67  time = 4020.0  sizebin 0 max birth:  4 4\n",
      "t=68  time = 4080.0  sizebin 0 max birth:  4 4\n",
      "t=69  time = 4140.0  sizebin 0 max birth:  4 4\n",
      "t=70  time = 4200.0  sizebin 0 max birth:  4 4\n",
      "t=71  time = 4260.0  sizebin 0 max birth:  5 5\n",
      "t=72  time = 4320.0  sizebin 0 max birth:  3 3\n",
      "t=73  time = 4380.0  sizebin 0 max birth:  4 4\n",
      "t=74  time = 4440.0  sizebin 0 max birth:  4 4\n",
      "t=75  time = 4500.0  sizebin 0 max birth:  3 3\n",
      "t=76  time = 4560.0  sizebin 0 max birth:  4 4\n",
      "t=77  time = 4620.0  sizebin 0 max birth:  3 3\n",
      "t=78  time = 4680.0  sizebin 0 max birth:  3 3\n",
      "t=79  time = 4740.0  sizebin 0 max birth:  4 4\n",
      "t=80  time = 4800.0  sizebin 0 max birth:  3 3\n",
      "t=81  time = 4860.0  sizebin 0 max birth:  4 4\n",
      "t=82  time = 4920.0  sizebin 0 max birth:  4 4\n",
      "t=83  time = 4980.0  sizebin 0 max birth:  4 4\n",
      "t=84  time = 5040.0  sizebin 0 max birth:  4 4\n",
      "t=85  time = 5100.0  sizebin 0 max birth:  3 3\n",
      "t=86  time = 5160.0  sizebin 0 max birth:  4 4\n",
      "t=87  time = 5220.0  sizebin 0 max birth:  3 3\n",
      "t=88  time = 5280.0  sizebin 0 max birth:  5 5\n",
      "t=89  time = 5340.0  sizebin 0 max birth:  3 3\n",
      "t=90  time = 5400.0  sizebin 0 max birth:  4 4\n",
      "t=91  time = 5460.0  sizebin 0 max birth:  3 3\n",
      "t=92  time = 5520.0  sizebin 0 max birth:  4 4\n",
      "t=93  time = 5580.0  sizebin 0 max birth:  4 4\n",
      "t=94  time = 5640.0  sizebin 0 max birth:  4 4\n",
      "t=95  time = 5700.0  sizebin 0 max birth:  3 3\n",
      "t=96  time = 5760.0  sizebin 0 max birth:  3 3\n",
      "t=97  time = 5820.0  sizebin 0 max birth:  4 4\n",
      "t=98  time = 5880.0  sizebin 0 max birth:  3 3\n",
      "t=99  time = 5940.0  sizebin 0 max birth:  4 4\n",
      "t=100  time = 6000.0  sizebin 0 max birth:  3 3\n",
      "t=101  time = 6060.0  sizebin 0 max birth:  3 3\n",
      "t=102  time = 6120.0  sizebin 0 max birth:  5 5\n",
      "t=103  time = 6180.0  sizebin 0 max birth:  4 4\n",
      "t=104  time = 6240.0  sizebin 0 max birth:  4 4\n",
      "t=105  time = 6300.0  sizebin 0 max birth:  3 3\n",
      "t=106  time = 6360.0  sizebin 0 max birth:  4 4\n",
      "t=107  time = 6420.0  sizebin 0 max birth:  3 3\n",
      "t=108  time = 6480.0  sizebin 0 max birth:  5 5\n",
      "t=109  time = 6540.0  sizebin 0 max birth:  3 3\n",
      "t=110  time = 6600.0  sizebin 0 max birth:  3 3\n",
      "t=111  time = 6660.0  sizebin 0 max birth:  4 4\n",
      "t=112  time = 6720.0  sizebin 0 max birth:  3 3\n",
      "t=113  time = 6780.0  sizebin 0 max birth:  3 3\n",
      "t=114  time = 6840.0  sizebin 0 max birth:  4 4\n",
      "t=115  time = 6900.0  sizebin 0 max birth:  4 4\n",
      "t=116  time = 6960.0  sizebin 0 max birth:  4 4\n",
      "t=117  time = 7020.0  sizebin 0 max birth:  3 3\n",
      "t=118  time = 7080.0  sizebin 0 max birth:  4 4\n",
      "t=119  time = 7140.0  sizebin 0 max birth:  4 4\n",
      "t=120  time = 7200.0  sizebin 0 max birth:  3 3\n",
      "t=121  time = 7260.0  sizebin 0 max birth:  4 4\n",
      "t=122  time = 7320.0  sizebin 0 max birth:  4 4\n",
      "t=123  time = 7380.0  sizebin 0 max birth:  4 4\n",
      "t=124  time = 7440.0  sizebin 0 max birth:  4 4\n",
      "t=125  time = 7500.0  sizebin 0 max birth:  5 5\n",
      "t=126  time = 7560.0  sizebin 0 max birth:  4 4\n",
      "t=127  time = 7620.0  sizebin 0 max birth:  3 3\n",
      "t=128  time = 7680.0  sizebin 0 max birth:  3 3\n",
      "t=129  time = 7740.0  sizebin 0 max birth:  3 3\n",
      "t=130  time = 7800.0  sizebin 0 max birth:  3 3\n",
      "t=131  time = 7860.0  sizebin 0 max birth:  3 3\n",
      "t=132  time = 7920.0  sizebin 0 max birth:  4 4\n",
      "t=133  time = 7980.0  sizebin 0 max birth:  4 4\n",
      "t=134  time = 8040.0  sizebin 0 max birth:  4 4\n",
      "t=135  time = 8100.0  sizebin 0 max birth:  3 3\n",
      "t=136  time = 8160.0  sizebin 0 max birth:  6 6\n",
      "t=137  time = 8220.0  sizebin 0 max birth:  3 3\n",
      "t=138  time = 8280.0  sizebin 0 max birth:  4 4\n",
      "t=139  time = 8340.0  sizebin 0 max birth:  3 3\n",
      "t=140  time = 8400.0  sizebin 0 max birth:  3 3\n",
      "t=141  time = 8460.0  sizebin 0 max birth:  4 4\n",
      "t=142  time = 8520.0  sizebin 0 max birth:  3 3\n",
      "t=143  time = 8580.0  sizebin 0 max birth:  3 3\n",
      "t=144  time = 8640.0  sizebin 0 max birth:  4 4\n",
      "t=145  time = 8700.0  sizebin 0 max birth:  3 3\n",
      "t=146  time = 8760.0  sizebin 0 max birth:  3 3\n",
      "t=147  time = 8820.0  sizebin 0 max birth:  3 3\n",
      "t=148  time = 8880.0  sizebin 0 max birth:  4 4\n",
      "t=149  time = 8940.0  sizebin 0 max birth:  3 3\n",
      "t=150  time = 9000.0  sizebin 0 max birth:  4 4\n",
      "t=151  time = 9060.0  sizebin 0 max birth:  3 3\n",
      "t=152  time = 9120.0  sizebin 0 max birth:  5 5\n",
      "t=153  time = 9180.0  sizebin 0 max birth:  4 4\n",
      "t=154  time = 9240.0  sizebin 0 max birth:  4 4\n",
      "t=155  time = 9300.0  sizebin 0 max birth:  3 3\n",
      "t=156  time = 9360.0  sizebin 0 max birth:  4 4\n",
      "t=157  time = 9420.0  sizebin 0 max birth:  3 3\n",
      "t=158  time = 9480.0  sizebin 0 max birth:  4 4\n",
      "t=159  time = 9540.0  sizebin 0 max birth:  3 3\n",
      "t=160  time = 9600.0  sizebin 0 max birth:  3 3\n",
      "t=161  time = 9660.0  sizebin 0 max birth:  4 4\n",
      "t=162  time = 9720.0  sizebin 0 max birth:  4 4\n",
      "t=163  time = 9780.0  sizebin 0 max birth:  4 4\n",
      "t=164  time = 9840.0  sizebin 0 max birth:  4 4\n",
      "t=165  time = 9900.0  sizebin 0 max birth:  3 3\n",
      "t=166  time = 9960.0  sizebin 0 max birth:  3 3\n",
      "t=167  time = 10020.0  sizebin 0 max birth:  3 3\n",
      "t=168  time = 10080.0  sizebin 0 max birth:  3 3\n",
      "t=169  time = 10140.0  sizebin 0 max birth:  4 4\n",
      "t=170  time = 10200.0  sizebin 0 max birth:  3 3\n",
      "t=171  time = 10260.0  sizebin 0 max birth:  4 4\n",
      "t=172  time = 10320.0  sizebin 0 max birth:  4 4\n",
      "t=173  time = 10380.0  sizebin 0 max birth:  3 3\n",
      "t=174  time = 10440.0  sizebin 0 max birth:  4 4\n",
      "t=175  time = 10500.0  sizebin 0 max birth:  4 4\n",
      "t=176  time = 10560.0  sizebin 0 max birth:  4 4\n",
      "t=177  time = 10620.0  sizebin 0 max birth:  4 4\n",
      "t=178  time = 10680.0  sizebin 0 max birth:  4 4\n",
      "t=179  time = 10740.0  sizebin 0 max birth:  4 4\n",
      "t=180  time = 10800.0  sizebin 0 max birth:  5 5\n",
      "t=181  time = 10860.0  sizebin 0 max birth:  3 3\n",
      "t=182  time = 10920.0  sizebin 0 max birth:  3 3\n",
      "t=183  time = 10980.0  sizebin 0 max birth:  3 3\n",
      "t=184  time = 11040.0  sizebin 0 max birth:  3 3\n",
      "t=185  time = 11100.0  sizebin 0 max birth:  5 5\n",
      "t=186  time = 11160.0  sizebin 0 max birth:  3 3\n",
      "t=187  time = 11220.0  sizebin 0 max birth:  4 4\n",
      "t=188  time = 11280.0  sizebin 0 max birth:  3 3\n",
      "t=189  time = 11340.0  sizebin 0 max birth:  4 4\n",
      "t=190  time = 11400.0  sizebin 0 max birth:  4 4\n",
      "t=191  time = 11460.0  sizebin 0 max birth:  3 3\n",
      "t=192  time = 11520.0  sizebin 0 max birth:  4 4\n",
      "t=193  time = 11580.0  sizebin 0 max birth:  3 3\n",
      "t=194  time = 11640.0  sizebin 0 max birth:  3 3\n",
      "t=195  time = 11700.0  sizebin 0 max birth:  4 4\n",
      "t=196  time = 11760.0  sizebin 0 max birth:  4 4\n",
      "t=197  time = 11820.0  sizebin 0 max birth:  4 4\n",
      "t=198  time = 11880.0  sizebin 0 max birth:  4 4\n",
      "t=199  time = 11940.0  sizebin 0 max birth:  4 4\n"
     ]
    }
   ],
   "source": [
    "N, p, dp_pulse = func_timestepping()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save N field to netcdf file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing to file: N_thermals.nc\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "fnameout = \"N_thermals.nc\"\n",
    "\n",
    "out = Dataset(fnameout, 'w', format='NETCDF4')\n",
    "print(\"writing to file: \" + fnameout)\n",
    "\n",
    "\n",
    "out.creator     = \"Roel Neggers, IGMK, University of Cologne\"\n",
    "out.created     = datetime.datetime.today().strftime('%d %b %Y')\n",
    "\n",
    "\n",
    "xdim    = out.createDimension('nx'    , nx)\n",
    "ydim    = out.createDimension('ny'    , ny)\n",
    "sizdim  = out.createDimension('nsiz'  , nsiz)\n",
    "timdim  = out.createDimension('ntim'  , ntim)\n",
    "agedim  = out.createDimension('nage'  , nage)\n",
    "\n",
    "\n",
    "out_x = out.createVariable('x', np.float32, ('nx'))\n",
    "out_x.units = 'm'\n",
    "out_x.long_name = 'gridsize in x direction'\n",
    "\n",
    "out_y = out.createVariable('y', np.float32, ('ny'))\n",
    "out_y.units = 'm'\n",
    "out_y.long_name = 'gridsize in y direction'\n",
    "\n",
    "out_siz = out.createVariable('siz', np.float32, ('nsiz'))\n",
    "out_siz.units = 'm'\n",
    "out_siz.long_name = 'object size'\n",
    "\n",
    "out_tim = out.createVariable('time', np.float32, ('ntim'))\n",
    "out_tim.units = 's'\n",
    "out_tim.long_name = 'time'\n",
    "\n",
    "out_age = out.createVariable('age', np.int32, ('nage'))\n",
    "out_age.units = 'index'\n",
    "out_age.long_name = 'age stratum'\n",
    "\n",
    "\n",
    "out_N  = out.createVariable('N', np.int32, ('nx','ny','nsiz','ntim','nage'))\n",
    "out_N.units = 'count'\n",
    "out_N.long_name = 'object number'\n",
    "\n",
    "out_x  [:] = x\n",
    "out_y  [:] = y\n",
    "out_siz[:] = siz\n",
    "out_tim[:] = tim\n",
    "out_age[:] = range(nage)\n",
    "\n",
    "out_N[:,:,:,:,:] = N[:,:,:,:,:]\n",
    "\n",
    "out.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2D scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'y [km]')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plot_scatter_time(N,10,dx=dx,dy=dy,figsize=(6,6),color=['b'],sizes=[5],buffer=[0.97],alpha=[0.2])\n",
    "axes =plt.gca()\n",
    "\n",
    "#Hacking the correct ticks and labels in\n",
    "labels = axes.get_xticklabels()\n",
    "nxlab = len(labels)\n",
    "xlab = np.empty( (nxlab) ).astype(np.str)\n",
    "xlab[:] = ''\n",
    "xlab[1      ] = '0'\n",
    "xlab[-2] = '10'\n",
    "axes.set_xticklabels(xlab)\n",
    "axes.set_yticklabels(xlab)\n",
    "\n",
    "axes.set_xlabel('x [km]')\n",
    "axes.set_xlabel('y [km]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Video maker\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Slighty modified version of the standard movie making function to get the right labels \n",
    "\n",
    "\n",
    "def plot_scatter_movie(N,t0,t1,output_folder='mov/',base_str='N_movie_',name='',dpi=200,\n",
    "    sizes         = [1,10,20,50,90],\n",
    "    color         = ['k','b','orange','r','g'],\n",
    "    buffer        = [0.9,0.85,0.8,0.75,0.7],\n",
    "    alpha         = [0.5,0.5,0.5,0.5,0.5],\n",
    "    figsize       = (6,6),\n",
    "    time_tracking = True,\n",
    "    t_substeps    = 10,\n",
    "    dx=1,dy=1,\n",
    "    rotation=0):\n",
    "    \"\"\"\n",
    "    A wrapper for plot_scatter_time that loops from t0 to t1 plotting the scatter plots for each and saving them.\n",
    "    \n",
    "    Currently only works for up to 5 different objects\n",
    "    WARNNG: Seems to have a memory leak! Don't run forever.\n",
    "    \"\"\"\n",
    "    \n",
    "    for t in range(t0,t1): \n",
    "        fig = plot_scatter_time(N,t,\n",
    "        sizes         = sizes     ,\n",
    "        color         = color     ,\n",
    "        buffer        = buffer    ,\n",
    "        alpha         = alpha     ,\n",
    "        figsize       = figsize   ,\n",
    "        time_tracking = time_tracking,\n",
    "        t_substeps    = t_substeps,\n",
    "        dx = dx, dy =dy,\n",
    "        rotation = rotation)\n",
    "        \n",
    "        axes =plt.gca()\n",
    "        #Hacking the correct ticks and labels in\n",
    "        labels = axes.get_xticklabels()\n",
    "        nxlab = len(labels)\n",
    "        xlab = np.empty( (nxlab) ).astype(np.str)\n",
    "        xlab[:] = ''\n",
    "        xlab[1      ] = '0'\n",
    "        xlab[-2] = '10'\n",
    "        axes.set_xticklabels(xlab)\n",
    "        axes.set_yticklabels(xlab)\n",
    "        axes.set_xlabel('x [km]')\n",
    "        axes.set_ylabel('y [km]')\n",
    "        \n",
    "        \n",
    "        ext = \".png\"\n",
    "        savestring =base_str+name+'_dt'+str(t)+ext\n",
    "        if t<100:\n",
    "            savestring =base_str+name+'_dt0'+str(t)+ext\n",
    "        if t<10:\n",
    "            savestring =base_str+name+'_dt00'+str(t)+ext\n",
    "\n",
    "        fig.savefig(output_folder+savestring,bbox_inches = 'tight',dpi=200)\n",
    "        print('saved: '+output_folder+savestring)\n",
    "        plt.close(fig)\n",
    "        \n",
    "        \n",
    "    \n",
    "    return\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved: mov/N_movie_cluster_dt000.png\n",
      "saved: mov/N_movie_cluster_dt001.png\n",
      "saved: mov/N_movie_cluster_dt002.png\n",
      "saved: mov/N_movie_cluster_dt003.png\n",
      "saved: mov/N_movie_cluster_dt004.png\n",
      "saved: mov/N_movie_cluster_dt005.png\n",
      "saved: mov/N_movie_cluster_dt006.png\n",
      "saved: mov/N_movie_cluster_dt007.png\n",
      "saved: mov/N_movie_cluster_dt008.png\n",
      "saved: mov/N_movie_cluster_dt009.png\n"
     ]
    }
   ],
   "source": [
    "#Makes and saves individual png of the timesteps to be made into a movie later on. \n",
    "plot_scatter_movie(N,0,10,name='cluster',dx=dx,dy=dy,figsize=(6,6),color=['b'],sizes=[5],buffer=[0.97],alpha=[0.2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start clustering...\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "#l=0\n",
    "l=nsiz-1\n",
    "\n",
    "#Specific time point to save 2D fields\n",
    "ntim2 = 5\n",
    "\n",
    "\n",
    "#Set N-threshold for clustering\n",
    "N_crit = 1\n",
    "#N_crit = 2\n",
    "\n",
    "Npixel_largest = np.array(range(ntim))\n",
    "Ntot           = np.array(range(ntim))\n",
    "Ntot_lab       = np.array(range(ntim))\n",
    "\n",
    "nbin = 39\n",
    "sd_size_lin = np.empty((ntim,nbin))\n",
    "sd_N_lin    = np.empty((ntim,nbin))\n",
    "\n",
    "print('start clustering...')\n",
    "\n",
    "for t in range(1,ntim):\n",
    "\n",
    "    #Making a 2D binary field and clustering it with periodic boundary conditions\n",
    "    N_final = np.sum( N[:,:,l,t,:], axis=2)\n",
    "    binary_N = np.zeros_like(N_final)\n",
    "\n",
    "    binary_N[N_final>N_crit] = 1\n",
    "\n",
    "    labeled_N = cluster_2D(binary_N,buffer_size=20)\n",
    "    \n",
    "    if t==ntim2:\n",
    "        binary_N_ntim2  = binary_N\n",
    "        labeled_N_ntim2 = labeled_N     #store timepoint of interest\n",
    "\n",
    "    labels, pixels = np.unique(labeled_N.ravel(),return_counts=True)\n",
    "    \n",
    "    if (np.size(pixels[1:])>0):\n",
    "        Npixel_largest[t] = np.max(pixels[1:])\n",
    "        Ntot          [t] = np.sum(N[:,:,l,t,:])\n",
    "        Ntot_lab      [t] = np.sum(binary_N[:,:])\n",
    "\n",
    "    #print( \"  t=%s  time = %s\" % (t, t*dtim), 'pixels of largest cluster: ',max(pixels[1:]))\n",
    "\n",
    "    #-- size sorting --\n",
    "    bins_lin2, ind_lin2, CSD_lin2 = lin_binner_minmax(pixels[1:] ,1,nbin+1,nbin)\n",
    "    #print( (t,len(bins_lin2),len(CSD_lin2),len(sd_size_lin[t,:]))) \n",
    "\n",
    "    sd_size_lin[t,:] = np.sqrt(bins_lin2[1:])\n",
    "    sd_N_lin   [t,:] = CSD_lin2\n",
    "    \n",
    "print('done')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convective memory\n",
    "\n",
    "Timeseries of the number of pixels in the largest cluster on the grid, different experiments yield different time-series. However, what they all share is a gradual increase in max cluster size.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,6))\n",
    "\n",
    "plt.plot(tim,Npixel_largest,'o-')\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('Npixel largest')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "ext = \".pdf\"\n",
    "\n",
    "savestring ='largest_cluster_over_time.pdf'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For comparison: Total number of objects on the grid \n",
    "\n",
    "Quickly equilibrates, should be equal to B0. \n",
    "\n",
    "Expresses balance between grid-average birth and death rates.\n",
    "\n",
    "Note: Rule 2 ensures this is the case, by making the cluster-environment more hostile for object births in such a way that grid-average p is conserved.\n",
    "\n",
    "So this is not the same as convective memory, it expresses the constraint on B0 we impose.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa3e33df208>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "plt.plot(tim,Ntot,'o-')\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('Ntot')\n",
    "\n",
    "plt.show() \n",
    "\n",
    "\n",
    "Narea     = 100. * Ntot    [:].astype(np.float) / (nx*ny)\n",
    "Narea_lab = 100. * Ntot_lab[:].astype(np.float) / (nx*ny)\n",
    "#print(Narea)\n",
    "\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "plt.plot(tim,Narea    ,'o-',label='all points')\n",
    "plt.plot(tim,Narea_lab,'o-',color='orange',label='meeting clustering threshold')\n",
    "\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('Narea  [%]')\n",
    "\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster plot of a specific timepoint\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gigantic plot because I can't get the pcolormesh plot to have the right linewidth otherwise\n",
    "\n",
    "t= 150\n",
    "l = 0\n",
    "#Summing over all ages\n",
    "N_final = np.sum( N[:,:,l,t,:], axis=2)\n",
    "\n",
    "#Making it a binary 1 where more than the critical amount are\n",
    "binary_N = np.zeros_like(N_final)\n",
    "binary_N[N_final>N_crit] = 1\n",
    "\n",
    "#Label and mask out\n",
    "labeled_N = cluster_2D(binary_N,buffer_size=20)\n",
    "masked_N = np.ma.masked_where(labeled_N<1,labeled_N)\n",
    "\n",
    "#plot\n",
    "fig,axes = plt.subplots(1,1,figsize=(18,18))\n",
    "plt.pcolormesh(masked_N,cmap=func_generate_random_cmap(),rasterized=True,edgecolor='w')\n",
    "\n",
    "\n",
    "plt.xlabel(\"%s  [%s]\" % (\"x\",\"km\"),fontsize=30)\n",
    "plt.ylabel(\"%s  [%s]\" % (\"y\",\"km\"),fontsize=30)\n",
    "axes.set_xticks([0,nx])\n",
    "axes.set_yticks([0,ny])\n",
    "axes.set_xticklabels([0,10],fontsize=30)\n",
    "axes.set_yticklabels([0,10],fontsize=30)\n",
    "\n",
    "plt.show()                         # Show the figure\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('fig/0615_example.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# loading and plotting a single timestep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "inpu = Dataset('N_thermals_1079.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = inpu.variables['N'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 100, 1, 5)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gigantic plot because I can't get the pcolormesh plot to have the right linewidth otherwise\n",
    "\n",
    "l = 0\n",
    "#Summing over all ages\n",
    "N_final = np.sum( N[:,:,l,:], axis=2)\n",
    "\n",
    "#Making it a binary 1 where more than the critical amount are\n",
    "binary_N = np.zeros_like(N_final)\n",
    "binary_N[N_final>N_crit] = 1\n",
    "\n",
    "#Label and mask out\n",
    "labeled_N = cluster_2D(binary_N,buffer_size=20)\n",
    "masked_N = np.ma.masked_where(labeled_N<1,labeled_N)\n",
    "\n",
    "#plot\n",
    "fig,axes = plt.subplots(1,1,figsize=(18,18))\n",
    "plt.pcolormesh(masked_N,cmap=func_generate_random_cmap(10000),rasterized=True,edgecolor='w')\n",
    "\n",
    "\n",
    "plt.xlabel(\"%s  [%s]\" % (\"x\",\"km\"),fontsize=40)\n",
    "plt.ylabel(\"%s  [%s]\" % (\"y\",\"km\"),fontsize=40)\n",
    "axes.set_xticks([0,nx])\n",
    "axes.set_yticks([0,ny])\n",
    "axes.set_xticklabels([0,10],fontsize=40)\n",
    "axes.set_yticklabels([0,10],fontsize=40)\n",
    "\n",
    "plt.show()                         # Show the figure\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('fig/2D_cluster_map_dt1080_0616.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting the size of each cluster and binning\n",
    "\n",
    "## first for log binning according to pixel number\n",
    "\n",
    "## then linear binning of the root of pixel area\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pixels of largest cluster:  27\n",
      "root of largest cluster number:  5.196152422706632\n",
      "cloud size array, in pixels  :  [ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18.\n",
      " 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36.\n",
      " 37. 38. 39. 40.]\n",
      "cloud size array av, sqrt of area   :  [1.41421356 1.73205081 2.         2.23606798 2.44948974 2.64575131\n",
      " 2.82842712 3.         3.16227766 3.31662479 3.46410162 3.60555128\n",
      " 3.74165739 3.87298335 4.         4.12310563 4.24264069 4.35889894\n",
      " 4.47213595 4.58257569 4.69041576 4.79583152 4.89897949 5.\n",
      " 5.09901951 5.19615242 5.29150262 5.38516481 5.47722558 5.56776436\n",
      " 5.65685425 5.74456265 5.83095189 5.91607978 6.         6.08276253\n",
      " 6.164414   6.244998   6.32455532]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#--- determine the size of all clusters ---\n",
    "labels, pixels = np.unique(labeled_N_ntim2.ravel(),return_counts=True)\n",
    "\n",
    "print('pixels of largest cluster: ',max(pixels[1:]))\n",
    "print('root of largest cluster number: ',max(np.sqrt(pixels[1:])))\n",
    "\n",
    "\n",
    "#--- size-sort 'em --\n",
    "bins_log , ind_log , CSD_log  = log_binner_minmax(        pixels[1:] ,1,150,10)\n",
    "bins_lin , ind_lin , CSD_lin  = lin_binner_minmax(np.sqrt(pixels[1:]),1,15 ,14)\n",
    "bins_lin2, ind_lin2, CSD_lin2 = lin_binner_minmax(        pixels[1:] ,1,40 ,39)\n",
    "\n",
    "#print(labels)\n",
    "#print(pixels)\n",
    "#print(np.sqrt(pixels[1:]))\n",
    "\n",
    "print('cloud size array, in pixels  : ',bins_lin2)\n",
    "#print('cloud size density: ',CSD_lin2 )\n",
    "\n",
    "\n",
    "#--- do some statistics ---\n",
    "\n",
    "sd_size_lin_av  = np.average   ( sd_size_lin[1:,:]  , axis=0 )    #mean\n",
    "print('cloud size array av, sqrt of area   : ',sd_size_lin_av)\n",
    "\n",
    "dummy = np.where( np.isnan(sd_N_lin), 0, sd_N_lin)   #filter out nan's, set to zero\n",
    "\n",
    "sd_N_lin_p01    = np.percentile( dummy[1:,:],  1., axis=0 )    #25% percentile\n",
    "sd_N_lin_p25    = np.percentile( dummy[1:,:], 25., axis=0 )    #25% percentile\n",
    "sd_N_lin_p50    = np.percentile( dummy[1:,:], 50., axis=0 )    #50% percentile\n",
    "sd_N_lin_p75    = np.percentile( dummy[1:,:], 75., axis=0 )    #75% percentile\n",
    "sd_N_lin_p99    = np.percentile( dummy[1:,:], 99., axis=0 )    #75% percentile\n",
    "\n",
    "\n",
    "#--- percentile plot of the CSD ---\n",
    "fig,ax = plt.subplots(1,1,figsize=(6,6))\n",
    "\n",
    "#plt.plot(np.sqrt(bins_lin2[1:]),CSD_lin2,'o-',label=\"t=%s\" % (t))\n",
    "\n",
    "#for t in [10,100,ntim-1]:\n",
    "#    plt.plot(sd_size_lin[t,:],sd_N_lin[t,:],'o-',label=\"t=%s\" % (t))\n",
    "\n",
    "ax.fill_between(sd_size_lin_av*dx, sd_N_lin_p01, sd_N_lin_p99, alpha=0.1,label='percentile 01-99')\n",
    "ax.fill_between(sd_size_lin_av*dx, sd_N_lin_p25, sd_N_lin_p75, alpha=0.2,label='percentile 25-75')\n",
    "ax.plot        (sd_size_lin_av*dx, sd_N_lin_p50, 'o:', color='k'        ,label='percentile 50')\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "\n",
    "ax.set_xlabel('cluster size  [m]', fontsize=18)\n",
    "ax.set_ylabel('number', fontsize=18)\n",
    "\n",
    "ax.set_xlim([120.,600.])\n",
    "ax.set_ylim([.7,700.])\n",
    "plt.setp(ax.get_yticklabels(which='minor'), visible=False)   #switch off the minor tickmarks\n",
    "\n",
    "ax.set_xticks([200,300,400,500,600])\n",
    "ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "plt.setp(ax.get_xticklabels(which='minor'), visible=False)   #switch off the minor tickmarks\n",
    "\n",
    "ax.set_yticks([1,2,3,4,10,100])\n",
    "ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "\n",
    "plt.legend()\n",
    "    \n",
    "plt.show()                         # Show the figure\n",
    "\n",
    "#fig.savefig('N_thermals_movie_CSD.png',bbox_inches = 'tight')\n",
    "#fig.savefig('N_thermals_movie_CSD.pdf',bbox_inches = 'tight')\n",
    "#\n",
    "#plt.close(fig)\n",
    "\n"
   ]
  },
  {
   "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": "3.6.8"
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