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@urschrei
Last active March 14, 2021 16:11
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Map interpolated values using a contour plot, and a scatter plot
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{
"metadata": {
"name": "",
"signature": "sha256:4e1826d3fd8e548dd69911b828c6d378a24ba4c4b875e5dc06e3864d1f5063da"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import pandas as pd\n",
"from matplotlib.mlab import griddata\n",
"from mpl_toolkits.basemap import Basemap, interp\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.colors import Normalize\n",
"mpl.rcParams['figure.figsize'] = (16, 12)\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 144
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dd = {\"Lon\": {0: 32.600000000000001,\n",
" 1: 27.100000000000001,\n",
" 2: 32.700000000000003,\n",
" 3: 24.199999999999999,\n",
" 4: 28.5,\n",
" 5: 28.100000000000001,\n",
" 6: 27.899999999999999,\n",
" 7: 24.800000000000001,\n",
" 8: 31.100000000000001,\n",
" 9: 25.899999999999999,\n",
" 10: 29.100000000000001,\n",
" 11: 25.800000000000001,\n",
" 12: 33.200000000000003,\n",
" 13: 28.300000000000001,\n",
" 14: 27.600000000000001,\n",
" 15: 28.899999999999999,\n",
" 16: 31.300000000000001,\n",
" 17: 31.899999999999999,\n",
" 18: 23.100000000000001,\n",
" 19: 31.399999999999999,\n",
" 20: 27.100000000000001,\n",
" 21: 24.399999999999999,\n",
" 22: 28.600000000000001,\n",
" 23: 31.300000000000001,\n",
" 24: 23.300000000000001,\n",
" 25: 30.199999999999999,\n",
" 26: 24.300000000000001,\n",
" 27: 26.399999999999999,\n",
" 28: 23.100000000000001},\n",
" \"Lat\": {0: -13.6,\n",
" 1: -16.899999999999999,\n",
" 2: -10.199999999999999,\n",
" 3: -13.6,\n",
" 4: -14.4,\n",
" 5: -12.6,\n",
" 6: -15.800000000000001,\n",
" 7: -14.800000000000001,\n",
" 8: -10.199999999999999,\n",
" 9: -13.5,\n",
" 10: -9.8000000000000007,\n",
" 11: -17.800000000000001,\n",
" 12: -12.300000000000001,\n",
" 13: -15.4,\n",
" 14: -16.100000000000001,\n",
" 15: -11.1,\n",
" 16: -8.9000000000000004,\n",
" 17: -13.300000000000001,\n",
" 18: -15.300000000000001,\n",
" 19: -11.9,\n",
" 20: -15.0,\n",
" 21: -11.800000000000001,\n",
" 22: -13.0,\n",
" 23: -14.300000000000001,\n",
" 24: -16.100000000000001,\n",
" 25: -13.199999999999999,\n",
" 26: -17.5,\n",
" 27: -12.199999999999999,\n",
" 28: -13.5},\n",
" \"Z\": {0: 41,\n",
" 1: 43,\n",
" 2: 46,\n",
" 3: 33,\n",
" 4: 43,\n",
" 5: 33,\n",
" 6: 46,\n",
" 7: 44,\n",
" 8: 35,\n",
" 9: 24,\n",
" 10: 10,\n",
" 11: 39,\n",
" 12: 44,\n",
" 13: 46,\n",
" 14: 47,\n",
" 15: 31,\n",
" 16: 39,\n",
" 17: 45,\n",
" 18: 31,\n",
" 19: 39,\n",
" 20: 42,\n",
" 21: 15,\n",
" 22: 39,\n",
" 23: 44,\n",
" 24: 39,\n",
" 25: 38,\n",
" 26: 32,\n",
" 27: 23,\n",
" 28: 27}}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 96
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# uncomment to get from CSV\n",
"# data = pd.read_csv(\n",
"# 'means.tsv',\n",
"# delim_whitespace=True, header=None,\n",
"# names=[\"Lon\", \"Lat\", \"Z\"])\n",
"data = pd.DataFrame(dd)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 159
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# define map extent\n",
"lllon = 21\n",
"lllat = -18\n",
"urlon = 34\n",
"urlat = -8\n",
"\n",
"# set up Basemap instance\n",
"m = Basemap(\n",
" projection = 'merc',\n",
" llcrnrlon = lllon, llcrnrlat = lllat, urcrnrlon = urlon, urcrnrlat = urlat,\n",
" resolution='h')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 160
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# transform lon / lat coordinates to map projection\n",
"data['projected_lon'], data['projected_lat'] = m(*(data[\"Lon\"].values, data[\"Lat\"].values))\n",
"norm = Normalize()\n",
"\n",
"# grid data\n",
"numcols, numrows = 1000, 1000\n",
"xi = np.linspace(data['projected_lon'].min(), data['projected_lon'].max(), numcols)\n",
"yi = np.linspace(data['projected_lat'].min(), data['projected_lat'].max(), numrows)\n",
"xi, yi = np.meshgrid(xi, yi)\n",
"\n",
"# interpolate\n",
"x, y, z = data['projected_lon'].values, data['projected_lat'].values, data['Z'].values\n",
"zi = griddata(x, y, z, xi, yi)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 187
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# set up plot\n",
"plt.clf()\n",
"fig = plt.figure(figsize=(6.4, 5.12))\n",
"ax = fig.add_subplot(111, axisbg='w', frame_on=False)\n",
"\n",
"# draw map details\n",
"m.drawmapboundary(fill_color = 'white')\n",
"m.fillcontinents(color='#C0C0C0', lake_color='#7093DB')\n",
"m.drawcountries(\n",
" linewidth=.75, linestyle='solid', color='#000073',\n",
" antialiased=True,\n",
" ax=ax, zorder=3)\n",
"\n",
"m.drawparallels(\n",
" np.arange(lllat, urlat, 2.),\n",
" color = 'black', linewidth = 0.5,\n",
" labels=[True, False, False, False])\n",
"m.drawmeridians(\n",
" np.arange(lllon, urlon, 2.),\n",
" color = '0.25', linewidth = 0.5,\n",
" labels=[False, False, False, True])\n",
"\n",
"# contour plots\n",
"con = m.contour(xi, yi, zi, 15, zorder=4, linewidths=.25, linestyles='dashed', colors='k', alpha=0.6)\n",
"conf = m.contourf(xi, yi, zi, 15, zorder=4, alpha=0.6, cmap='RdPu')\n",
"# scatter plot - vmin/max for colormap compat\n",
"m.scatter(\n",
" data['projected_lon'],\n",
" data['projected_lat'],\n",
" color='#545454',\n",
" edgecolor='#ffffff',\n",
" alpha=.75,\n",
" s=50 * norm(data['Z']),\n",
" cmap='RdPu',\n",
" ax=ax,\n",
" vmin=zi.min(), vmax=zi.max(), zorder=4)\n",
"\n",
"# add colour bar, title, and scale\n",
"cbar = plt.colorbar(orientation='horizontal', fraction=.057, pad=0.05)\n",
"plt.title(\"Mean Rainfall\")\n",
"m.drawmapscale(\n",
" 24., -9., 28., -13,\n",
" 100,\n",
" units='km', fontsize=7,\n",
" yoffset=None,\n",
" barstyle='fancy', labelstyle='simple',\n",
" fillcolor1='w', fillcolor2='#000000',\n",
" fontcolor='#000000',\n",
" zorder=5)\n",
"\n",
"plt.savefig(\"rainfall.png\", format=\"png\", transparent=True, dpi=300)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"<matplotlib.figure.Figure at 0x109ed03d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
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cMe15PCsEuqnJwdjY/MaI6nnQtUUtjkNK6Au6+My/3EggkMSU6RVWxqLFU+lm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6HtqMwMy+UCA5SBAqXs5d7OwHSBXhhmmGG4Ay3QDlTt60dbCi0taQO0TdokTuymWRzHSezE8SLb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\nRlo6fZ4+s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"text": [
"<matplotlib.figure.Figure at 0x109c2be50>"
]
}
],
"prompt_number": 185
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also look at http://earthpy.org/interpolation_between_grids_with_basemap.html for grid interpolation"
]
}
],
"metadata": {}
}
]
}
# coding: utf-8
# In[144]:
import numpy as np
import pandas as pd
from matplotlib.mlab import griddata
from mpl_toolkits.basemap import Basemap, interp
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
mpl.rcParams['figure.figsize'] = (16, 12)
get_ipython().magic(u'matplotlib inline')
# In[96]:
dd = {"Lon": {0: 32.600000000000001,
1: 27.100000000000001,
2: 32.700000000000003,
3: 24.199999999999999,
4: 28.5,
5: 28.100000000000001,
6: 27.899999999999999,
7: 24.800000000000001,
8: 31.100000000000001,
9: 25.899999999999999,
10: 29.100000000000001,
11: 25.800000000000001,
12: 33.200000000000003,
13: 28.300000000000001,
14: 27.600000000000001,
15: 28.899999999999999,
16: 31.300000000000001,
17: 31.899999999999999,
18: 23.100000000000001,
19: 31.399999999999999,
20: 27.100000000000001,
21: 24.399999999999999,
22: 28.600000000000001,
23: 31.300000000000001,
24: 23.300000000000001,
25: 30.199999999999999,
26: 24.300000000000001,
27: 26.399999999999999,
28: 23.100000000000001},
"Lat": {0: -13.6,
1: -16.899999999999999,
2: -10.199999999999999,
3: -13.6,
4: -14.4,
5: -12.6,
6: -15.800000000000001,
7: -14.800000000000001,
8: -10.199999999999999,
9: -13.5,
10: -9.8000000000000007,
11: -17.800000000000001,
12: -12.300000000000001,
13: -15.4,
14: -16.100000000000001,
15: -11.1,
16: -8.9000000000000004,
17: -13.300000000000001,
18: -15.300000000000001,
19: -11.9,
20: -15.0,
21: -11.800000000000001,
22: -13.0,
23: -14.300000000000001,
24: -16.100000000000001,
25: -13.199999999999999,
26: -17.5,
27: -12.199999999999999,
28: -13.5},
"Z": {0: 41,
1: 43,
2: 46,
3: 33,
4: 43,
5: 33,
6: 46,
7: 44,
8: 35,
9: 24,
10: 10,
11: 39,
12: 44,
13: 46,
14: 47,
15: 31,
16: 39,
17: 45,
18: 31,
19: 39,
20: 42,
21: 15,
22: 39,
23: 44,
24: 39,
25: 38,
26: 32,
27: 23,
28: 27}}
# In[159]:
# uncomment to get from CSV
# data = pd.read_csv(
# 'means.tsv',
# delim_whitespace=True, header=None,
# names=["Lon", "Lat", "Z"])
data = pd.DataFrame(dd)
# In[160]:
# define map extent
lllon = 21
lllat = -18
urlon = 34
urlat = -8
# set up Basemap instance
m = Basemap(
projection = 'merc',
llcrnrlon = lllon, llcrnrlat = lllat, urcrnrlon = urlon, urcrnrlat = urlat,
resolution='h')
# In[187]:
# transform lon / lat coordinates to map projection
data['projected_lon'], data['projected_lat'] = m(*(data["Lon"].values, data["Lat"].values))
norm = Normalize()
# grid data
numcols, numrows = 1000, 1000
xi = np.linspace(data['projected_lon'].min(), data['projected_lon'].max(), numcols)
yi = np.linspace(data['projected_lat'].min(), data['projected_lat'].max(), numrows)
xi, yi = np.meshgrid(xi, yi)
# interpolate
x, y, z = data['projected_lon'].values, data['projected_lat'].values, data['Z'].values
zi = griddata(x, y, z, xi, yi)
# In[185]:
# set up plot
plt.clf()
fig = plt.figure(figsize=(6.4, 5.12))
ax = fig.add_subplot(111, axisbg='w', frame_on=False)
# draw map details
m.drawmapboundary(fill_color = 'white')
m.fillcontinents(color='#C0C0C0', lake_color='#7093DB')
m.drawcountries(
linewidth=.75, linestyle='solid', color='#000073',
antialiased=True,
ax=ax, zorder=3)
m.drawparallels(
np.arange(lllat, urlat, 2.),
color = 'black', linewidth = 0.5,
labels=[True, False, False, False])
m.drawmeridians(
np.arange(lllon, urlon, 2.),
color = '0.25', linewidth = 0.5,
labels=[False, False, False, True])
# contour plots
con = m.contour(xi, yi, zi, 15, zorder=4, linewidths=.25, linestyles='dashed', colors='k', alpha=0.6)
conf = m.contourf(xi, yi, zi, 15, zorder=4, alpha=0.6, cmap='RdPu')
# scatter plot - vmin/max for colormap compat
m.scatter(
data['projected_lon'],
data['projected_lat'],
color='#545454',
edgecolor='#ffffff',
alpha=.75,
s=50 * norm(data['Z']),
cmap='RdPu',
ax=ax,
vmin=zi.min(), vmax=zi.max(), zorder=4)
# add colour bar, title, and scale
cbar = plt.colorbar(orientation='horizontal', fraction=.057, pad=0.05)
plt.title("Mean Rainfall")
m.drawmapscale(
24., -9., 28., -13,
100,
units='km', fontsize=7,
yoffset=None,
barstyle='fancy', labelstyle='simple',
fillcolor1='w', fillcolor2='#000000',
fontcolor='#000000',
zorder=5)
plt.savefig("rainfall.png", format="png", transparent=True, dpi=300)
plt.show()
# Also look at http://earthpy.org/interpolation_between_grids_with_basemap.html for grid interpolation
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