{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"FM EC-module tests
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PolyTim interpolation (boundaries)
"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import sys, os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Some stuff needed in the calculation\n",
"\n",
"# Intersection between two line segments, \n",
"# expressed as a fraction of the first segment (p1->p2)\n",
"def crs(p1,p2,p3,p4):\n",
" # intersection lambda of lines p1->p2 and p3->p4\n",
" # in terms of vector p1->p2\n",
" l = ((p3[0]-p1[0])*(p3[1]-p4[1])-(p3[1]-p1[1])*(p3[0]-p4[0])) \\\n",
" / ((p2[0]-p1[0])*(p3[1]-p4[1])-(p2[1]-p1[1])*(p3[0]-p4[0]))\n",
" return(np.array([l,1.-l]))\n",
"\n",
"def time_interpolation(time_asked, tslist):\n",
" results = []\n",
" for ts in tslist:\n",
" if ((time_askedts[-1,0])):\n",
" results.append(np.NaN)\n",
" else:\n",
" for itime in range(ts.shape[0]-1):\n",
" if ((time_asked - ts[itime,0])*(time_asked - ts[itime+1,0])<0):\n",
" break\n",
" t1 = ts[itime,0]\n",
" t2 = ts[itime+1,0]\n",
" wt = (ts[itime+1,0] - time_asked)/(ts[itime+1,0] - ts[itime,0])\n",
" val = wt*ts[itime,1] + (1.-wt)* ts[itime+1,1]\n",
" results.append(val)\n",
" return(results)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test f01-c001
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculation
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Input: Two time series (old style) for two support points"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"tim01 = np.loadtxt('tfl_01_0001.tim')\n",
"tim02 = np.loadtxt('tfl_01_0002.tim')\n",
"bndpair=[[0.0,0.0],[0.0,1.5]] # boundary cell face, pli-segment"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Time is given in minutes in these .tim files, so convert to seconds\n",
"to_seconds = 60.0\n",
"tim01[:,0] = tim01[:,0]*to_seconds\n",
"tim02[:,0] = tim02[:,0]*to_seconds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copied from the test configuration file:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Times = 10.0, 20.0, 30.0, 40.0 # times for requesting data from EC-module\n",
"LocationsX = 0.75, 0.75, 0.75 # location X-coordinate \n",
"LocationsY = 0.667, 1.000, 1.333 # location Y-coordinate\n",
"LocationsX2 = -1.000, -1.000, -1.000 # location X-coordinate \n",
"LocationsY2 = 0.667, 1.000, 1.333 # location Y-coordinate\n",
"npt = len(LocationsX)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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dTnIL8BCwC7i3qp5JcvNg+yHgMHA9cBQ4BXxyeiVLc2R5ub88eLB/KWZxsR/sZ9qlKUlV\nzWTgXq9X6+vrMxlbkuZVkserqjeqn99QlaQOMtwlqYMMd0nqIMNdkjrIcJekDprZp2WSnASOneXu\ne4Dvt1jOtM1TvfNUK8xXvfNUK8xXvfNUK0xW796qGvkt0JmF+ySSrDf5KNC5Yp7qnadaYb7qnada\nYb7qnadaYWfq9bKMJHWQ4S5JHTSv4b466wLGNE/1zlOtMF/1zlOtMF/1zlOtsAP1zuU1d0nS9ub1\nzF2StI1zNtyT/OskzyR5PcmWd5W3enh3kncn+bMkfz1YvmvK9Y4cL8n7kjwx9PN3SW4dbPvdJC8P\nbbt+lrUO+r2Y5KlBPevj7r+T9Sa5NMn/SvKtwXHz20Pbpj63kzxEftS+M6h1eVDjU0keTXLl0LZN\nj4kZ17svyY+H/n0/23TfGdT674fqfDrJ/0vy7sG2due2qs7JH+CXgPcBDwO9LfrsAr4N/APgAuCb\nwBWDbf8RuG3w+jbgc1Oud6zxBrX/Lf3PrAL8LvDvdmhuG9UKvAjsmfR/607UC1wEfGDw+u3A80PH\nwlTndrvjcKjP9cBXgQDXAH/ZdN8Z1PpB4F2D19edqXW7Y2LG9e4DvnI2++50rRv6/xrwP6c1t+fs\nmXtVPVtVz43ott3Duw8AfzR4/UfAv5pOpT8x7ngfAb5dVWf7Ra5JTDo359zcVtV3q+obg9f/G3iW\nnXuO7yQPkW+y747WWlWPVtWPBqtH6D9ZbVYmmZ9zbm43uBH44rSKOWfDvaHtHsz93vrp06D+Fnjv\nlGsZd7wbePM/7KcHvw7fO+VLHU1rLeBrSR5PsnIW+7dlrPGSLAH/GPjLoeZpzu0kD5Hf6YfLjzve\nTfR/4zhjq2NiWprW+8HBv+9Xk/yjMfdtS+PxkuwG9gNfGmpudW5HPolpmpJ8Dfj5TTYdrKo/aWuc\nqqokE38saLt6xxkv/ccV/kvgPww13wXcTv8f+Hbg94HfmnGt11bVy0l+DvizJH9VVY+Msf9O10uS\nn6X/H8ytVfV3g+ZW5/atIsmH6Yf7tUPNI4+JGfgGsFhV/2dwP+XLwOUzrmmUXwP+vKp+ONTW6tzO\nNNyr6p9N+BbbPZj7e0kuqqrvDn79fWXCsbatN8k4410HfKOqvjf03j95neRu4CuzrrWqXh4sX0ny\nX+n/2vkI5+jcJnkb/WBfq6oHht671bndxCQPkX9bg33b1Ohh9kneD9wDXFdVPzjTvs0xMbN6h/5P\nnKo6nOQLSfY02Xenax3ypt/c257beb8ss93Dux8EfnPw+jeB1n4T2MI4473pWtsgtM74KPB0q9W9\n0chak/xMkrefeQ386lBN59zcJgnwn4Fnq+o/bdg27bmd5CHyTfbd0VqTLAIPAB+rqueH2rc7JmZZ\n788P/v1JcjX9XPtBk313utZBje8APsTQcTyVuZ3WneNJf+j/R3gCeBX4HvDQoP0XgMND/a6n/8mI\nb9O/nHOm/T3A/wD+Gvga8O4p17vpeJvU+zP0D7x3bNj/vwBPAU8ODoiLZlkr/Tv+3xz8PHOuzy39\nSwc1mL8nBj/X79TcbnYcAjcDNw9eB7hzsP0phj4BttUxPMX5HFXrPcCPhuZxfdQxMeN6bxnU8036\nN4A/eK7O7WD9E8D9G/ZrfW79hqokddC8X5aRJG3CcJekDjLcJamDDHdJ6iDDXZI6yHCXpA4y3CWp\ngwx3Seqg/w9tBJvUT+f4KQAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# definition of a pair of support points\n",
"xbnd = np.array([bndpair[0][0],bndpair[1][0]])\n",
"ybnd = np.array([bndpair[0][1],bndpair[1][1]])\n",
"fig = plt.figure();\n",
"plt.plot(xbnd, ybnd, \"ro\",xbnd, ybnd, \"r-\"); \n",
"\n",
"# definition of flow links\n",
"ws = []\n",
"for ipt in range(npt):\n",
" flowlink=[[LocationsX[ipt],LocationsY[ipt]], [LocationsX2[ipt],LocationsY2[ipt]]] \n",
"\n",
" # determine spatial interpolation weight\n",
" ws.append(crs(np.array(bndpair[0]),np.array(bndpair[1]),np.array(flowlink[0]),np.array(flowlink[1]))) \n",
" \n",
" # plot \n",
" xflow = np.array([flowlink[0][0],flowlink[1][0]])\n",
" yflow = np.array([flowlink[0][1],flowlink[1][1]])\n",
" plt.plot(xflow, yflow, \"bo\",xflow, yflow, \"b-\"); \n",
"\n",
"# fig.suptitle(\"Weigth factor at intersection : %f\"%(ws[0]))\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[array([ 0.44466667, 0.55533333]),\n",
" array([ 0.66666667, 0.33333333]),\n",
" array([ 0.88866667, 0.11133333])]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ws # Show the derived weight functions"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10.0 [0.00016666666666666718, 0.00016666666666666718, 0.00016666666666666718]\n",
"20.0 [0.00033333333333333327, 0.00033333333333333327, 0.00033333333333333327]\n",
"30.0 [0.00050000000000000044, 0.00050000000000000044, 0.00050000000000000044]\n",
"40.0 [0.00066666666666666654, 0.00066666666666666654, 0.00066666666666666654]\n"
]
}
],
"source": [
"for time_asked in Times:\n",
" interp = []\n",
" values_at_support_points = time_interpolation(time_asked,[tim01,tim02])\n",
" for iw in range(len(ws)):\n",
" interpval = np.dot(ws[iw],values_at_support_points)\n",
" interp.append(interpval)\n",
" print time_asked, interp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test results
"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"testdir = './'\n",
"outfile = 'test01.out'\n",
"tstfile = 'test01.ref'"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rawrefdata = np.loadtxt(os.path.join(testdir,reffile))\n",
"rawtstdata = np.loadtxt(os.path.join(testdir,tstfile))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.00000000e+00, 1.66666667e-05, 1.66666667e-05],\n",
" [ 2.00000000e+00, 3.33333333e-05, 3.33333333e-05],\n",
" [ 3.00000000e+00, 6.00000000e-05, 5.00000000e-05],\n",
" [ 4.00000000e+00, 6.66666667e-05, 6.66666667e-05]])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rawrefdata"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Retrieve reference and test data:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reftimes = rawrefdata[:,0]\n",
"tsttimes = rawtstdata[:,0]\n",
"refvalues = rawrefdata[:,1:]\n",
"tstvalues = rawtstdata[:,1:]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.66666667e-05, 1.66666667e-05],\n",
" [ 3.33333333e-05, 3.33333333e-05],\n",
" [ 6.00000000e-05, 5.00000000e-05],\n",
" [ 6.66666667e-05, 6.66666667e-05]])"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tstvalues"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tim01[:,0] = tim01[:,0]*f_time_seconds\n",
"tim02[:,0] = tim02[:,0]*f_time_seconds"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.00000000e+00, 0.00000000e+00],\n",
" [ 3.60000000e+04, 1.00000000e-02],\n",
" [ 7.20000000e+04, 0.00000000e+00],\n",
" [ 1.08000000e+05, -1.00000000e-02],\n",
" [ 1.44000000e+05, 0.00000000e+00],\n",
" [ 1.80000000e+05, 1.00000000e-02],\n",
" [ 2.16000000e+05, 0.00000000e+00],\n",
" [ 2.52000000e+05, -1.00000000e-02],\n",
" [ 2.88000000e+05, 0.00000000e+00],\n",
" [ 3.24000000e+05, 1.00000000e-02],\n",
" [ 3.60000000e+05, 0.00000000e+00],\n",
" [ 3.96000000e+05, -1.00000000e-02],\n",
" [ 4.32000000e+05, 0.00000000e+00]])"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tim01"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Interpolate each timeseries at the requested time ..."
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.0044444444444444444, 0.0094949494949495023]\n"
]
}
],
"source": [
"print values_at_support_points"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bndpair[0][0]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}