{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Get data files from a URL\n", "This notebook shows how to obtain data files from public URLs. \n", "If you want to distribute files (web developers) the files need to be in the webroot (i.e. http://FQDN/path/to/file/filename.txt)\n", "\n", "Here we will do an example with a file that contains topographic data in XYZ format" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [], "source": [ "#Step 1: import needed modules to interact with the internet\n", "from urllib.request import urlopen # import a method that will get a file then open the file\n", "import numpy #import numpy\n", "import pandas #import pandas\n", "from math import sqrt" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#Step 2: make the connection to the remote file (actually its implementing \"bash curl -O http://fqdn/path ...\")\n", "remote_url = 'http://www.rtfmps.com/share_files/pip-corner-sumps.txt' # \n", "elevationXYZ = urlopen(remote_url).read().decode().split() # Gets the file contents as a single vector, comma delimited, file is not retained locally" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 119, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Python primatives to structure the data\n", "howmany = len(elevationXYZ) # how long is the vector?\n", "nrow = int(howmany/3)\n", "xyz = [[0 for j in range(3)] for j in range(nrow)] # null space to receive data define columnX" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 120, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Now will build xyz as a matrix with 3 columns\n", "index = 0\n", "for irow in range(0,nrow):\n", " xyz[irow][0]=float(elevationXYZ[index])\n", " xyz[irow][1]=float(elevationXYZ[index+1])\n", " xyz[irow][2]=float(elevationXYZ[index+2])\n", " index += 3 #increment the index" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "# xyz is now a 3-column float array and can now probably be treated as a data frame\n", "df = pandas.DataFrame(xyz)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 774 entries, 0 to 773\n", "Data columns (total 3 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 0 774 non-null float64\n", " 1 1 774 non-null float64\n", " 2 2 774 non-null float64\n", "dtypes: float64(3)\n", "memory usage: 18.3 KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib\n", "import numpy as np\n", "import matplotlib.cm as cm\n", "import matplotlib.pyplot as plt\n", "delta = 0.025\n", "x = np.arange(-3.0, 3.0, delta)\n", "y = np.arange(-2.0, 2.0, delta)\n", "X, Y = np.meshgrid(x, y)\n", "Z1 = np.exp(-X**2 - Y**2)\n", "Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)\n", "Z = (Z1 - Z2) * 2\n", "fig, ax = plt.subplots()\n", "CS = ax.contour(X, Y, Z)\n", "ax.clabel(CS, inline=1, fontsize=10)\n", "ax.set_title('Simplest default with labels')" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "X, Y = np.meshgrid(xyz[:][0], xyz[:][1])\n", "Z = xyz[:][2]\n", "#fig, ax = plt.subplots()\n", "#CS = ax.contour(X, Y, Z)\n", "#ax.clabel(CS, inline=1, fontsize=10)\n", "#ax.set_title('Simplest default with labels')" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 4.52063104e-06, 5.24894067e-06, 6.08697321e-06, ...,\n", " 9.82013796e-07, 5.79256020e-07, 2.55995511e-07],\n", " [ 4.99294681e-06, 5.79735018e-06, 6.72294028e-06, ...,\n", " 7.40995503e-07, 3.27884007e-07, 2.57498016e-19],\n", " [ 5.50772112e-06, 6.39505861e-06, 7.41607717e-06, ...,\n", " 4.18911596e-07, 3.15943289e-19, -3.27884007e-07],\n", " ...,\n", " [ 5.97236348e-06, 6.92886625e-06, 8.02817162e-06, ...,\n", " -2.08885159e-02, -1.89602868e-02, -1.71885358e-02],\n", " [ 5.41648023e-06, 6.28368636e-06, 7.28030161e-06, ...,\n", " -1.99324015e-02, -1.80924123e-02, -1.64017434e-02],\n", " [ 4.90598951e-06, 5.69120666e-06, 6.59353913e-06, ...,\n", " -1.89962699e-02, -1.72426787e-02, -1.56313990e-02]])" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because the original data is irregular spaced elevations in xyz structure, to draw a contour map we need values on\n", "a regular spaced grid. The process of interpolating observations onto such a grid is called gridding, go figure!\n", "\n", "There are many elaborate gridding methods, we will just use an inverse distance weighting.\n", "\n", "First lets define the grid based on the data, we can use the dataframe or the xyz vector" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [], "source": [ "xlow = min(xyz[:][0])\n", "xhigh = max(xyz[:][0])\n", "ylow= min(xyz[:][1])\n", "yhigh= max(xyz[:][1])\n", "xlines = 10 # how many lines along x-axis\n", "ylines = 10 # how many lines along y-axis\n", "deltax = float((xhigh - xlow)/(xlines))\n", "deltay = float((yhigh - ylow)/(ylines))\n", "xgrid = np.arange(xlow*0.89, xhigh*(1.11), deltax) # build an array of xgrid values, the extension factors optional\n", "ygrid = np.arange(ylow*0.89, yhigh*(1.11), deltay) # build an array of ygrid values\n", "X, Y = np.meshgrid(xgrid, ygrid) # build a mesh for the plot call" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "blank\n" ] } ], "source": [ "print(\"blank\")" ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [], "source": [ "distance=[0 for i in range(nrow)] #null array to store distances from grid coordinates to observations -- this object will get huge!\n", "for irow in range(0,xlines):\n", " for jcol in range(0,ylines):\n", " rownum = 0\n", " for krow in range(0,nrow):\n", " distance[krow]=sqrt(((xgrid[jcol]-xyz[krow][0])**2)+((ygrid[irow]-xyz[krow][1])**2))\n", "# need to insert test for grid same as observatio\n", " numerator = 0\n", " denominator = 0\n", " for iobs in range(0,nrow):\n", " numerator += xyz[iobs][2]\n", " \n" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[74.90959724, 93.21251922, 0.0],\n", " [75.17907367, 64.40278759, 0.0],\n", " [94.9935575, 93.07951286, 0.0],\n", " 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