Reference: http://matplotlib.org/basemap/users/examples.html
# Importing libraries we will need.
import numpy as np
import matplotlib.pyplot as plt
# Calculating X and Y coordinates
# linspace returns evenly spaced numbers over a specified interval.
x = np.linspace(-10,10,25)
# Raising x to third power, element-wise
y = x**3
# Make and show the plot
plt.plot(x,y)
plt.show()
# Importing libraries we will need.
import netCDF4
import matplotlib.pyplot as plt
# Open the netCDF file
f = netCDF4.Dataset('data/rtofs_glo_3dz_f006_6hrly_reg3.nc', 'r')
# Getting the n-dimensional data
tempv = f.variables['temperature']
depth = f.variables['Depth']
print("The temperature variable...\n")
# Note the temperature variable has time, depth, y and x dimensions
print(tempv)
print("The dimensions...\n")
print(tempv.dimensions)
The temperature variable... <type 'netCDF4.Variable'> float32 temperature(MT, Depth, Y, X) coordinates: Longitude Latitude Date standard_name: sea_water_potential_temperature units: degC _FillValue: 1.26765e+30 valid_range: [ -5.07860279 11.14989948] long_name: temp [90.9H] unlimited dimensions: MT current shape = (1, 10, 850, 712) The dimensions... (u'MT', u'Depth', u'Y', u'X')
# Continued from previous cell..
# Our goal is temperature as a function of depth so slicing along the depth axis
# at a specific time and place
temp = tempv[0,:,123,486]
# Masked arrays are arrays that have bad values idenitifed by the mask array.
print("The masked array containing the temperature data...")
print(repr(temp))
# trick for filtering out good values
x = temp[~temp.mask]
y = depth[~temp.mask]
print("Plotting...")
# plot and show data
plt.plot(y,x)
plt.show()
# close netCDF
f.close()
The masked array containing the temperature data... masked_array(data = [6.485864639282227 4.6258392333984375 4.010849952697754 3.8229074478149414 3.4448373317718506 2.8652758598327637 1.785945177078247 1.333146333694458 -- --], mask = [False False False False False False False False True True], fill_value = 1.26765e+30) Plotting...
Peruse matplotlib gallery and see and emulate what you like.
# Importing libraries we will need.
import netCDF4
import matplotlib.pyplot as plt
# Adding text, adjusting borders, and figure size
# Get figure hook to manipulate our plot
fig = plt.figure()
desc ='Figure 1. Temperature as a function of ocean depth as\n'\
'predicted by the RTOFS model'
# Adding our description
plt.figtext(.5,.15,desc,fontsize=12,ha='center')
#adjust margin
fig.subplots_adjust(bottom=0.35)
#adjust figure size
fig.set_size_inches(7,7)
# Improve axes
# Get axis hook to manipulate our plot
ax = fig.add_subplot(111)
# Add axis labels
ax.set_xlabel('Depth (m)', fontweight='bold')
ax.set_ylabel('Temperature ($^\circ$C)', fontweight='bold')
# Don't show top and right axis
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
# Define ticks
ax.tick_params(axis='both', direction='out')
ax.get_xaxis().tick_bottom() # remove unneeded ticks
ax.get_yaxis().tick_left()
# Getting the data as we did before
f = netCDF4.Dataset('data/rtofs_glo_3dz_f006_6hrly_reg3.nc', 'r')
tempv = f.variables['temperature']
depth = f.variables['Depth']
temp = tempv[0,:,123,486]
x = temp[~temp.mask] #trick for getting data
y = depth[~temp.mask]
# Plotting line with triangle markers, and red line color.
plt.plot(y,x, marker=r'^', color='r', markersize=10, clip_on=False,linewidth=2.0)
plt.show()
f.close()
#importing libraries
from mpl_toolkits.basemap import Basemap
import netCDF4
import matplotlib.pyplot as plt
import numpy as np
# Open and read netCDF variables
nc = netCDF4.Dataset('data/rtofs_glo_3dz_f006_6hrly_reg3.nc', 'r')
tempv = nc.variables['temperature']
lat = nc.variables['Latitude'][:]
lon = nc.variables['Longitude'][:]
data = tempv[0,0,:,:]
# Construct our basemap object with lat/lon bounds, resolution, projection,
m = Basemap(llcrnrlon=-179,llcrnrlat=20,urcrnrlon=-100,urcrnrlat=85,
resolution='l',projection='stere',
lat_0=60,lon_0=-120.)
# Setting the plot size and text
fig = plt.figure(figsize=(10,8))
plt.figtext(.5,.15,'Figure 1. Sea surface temperature as predicted by the RTOFS model',fontsize=12,ha='center')
# compute map proj coordinates.
x, y = m(lon,lat)
# define color map
cmap = plt.cm.hsv
#Coloring the data
cs = m.pcolormesh(x,y,data,shading='flat', cmap=cmap)
# Nice high-level, human-readable abstractions for dealing with maps.
m.drawcoastlines()
m.fillcontinents(color='#989865',lake_color='#336699')
m.drawmapboundary(fill_color='#336699')
m.drawparallels(np.arange(-80.,81.,20.),labels=[0,1,1,0])
m.drawmeridians(np.arange(-180.,180.,20.),labels=[1,0,0,1])
# Color bar
cbar = m.colorbar(cs,location='right',pad="10%")
cbar.set_label('$^{o}C$')
nc.close()
#importing libraries
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap, addcyclic
from scipy.ndimage.filters import minimum_filter, maximum_filter
from netCDF4 import Dataset
from pyudl.tds import get_latest_dods_url
# Function to determine local extrema based on a window size
def extrema(mat,mode='wrap',window=10):
"""find the indices of local extrema (min and max)
in the input array."""
mn = minimum_filter(mat, size=window, mode=mode)
mx = maximum_filter(mat, size=window, mode=mode)
# (mat == mx) true if pixel is equal to the local max
# (mat == mn) true if pixel is equal to the local in
# Return the indices of the maxima, minima
return np.nonzero(mat == mn), np.nonzero(mat == mx)
# Getting data from the TDS with our get_latest_dods_url helper function
gfs_data_url = \
"http://thredds.ucar.edu/thredds/catalog/grib/NCEP/GFS/Global_0p5deg/catalog.xml"
latest = get_latest_dods_url(gfs_data_url)
data = Dataset(latest)
# Not the best way to get time. Assumes wanting to get the first time.
# And making fragile assumptions on the appearance of the unit string.
# Is there a better way of getting the time stamp?
time = data.variables['time'].units.split()[2]
# read lats,lons.
lats = data.variables['lat'][:]
lons1 = data.variables['lon'][:]
nlats = len(lats)
nlons = len(lons1)
# read prmsl, convert to hPa (mb).
prmsl = 0.01*data.variables['Pressure_reduced_to_MSL_msl'][0]
# the window parameter controls the number of highs and lows detected.
# (higher value, fewer highs and lows)
local_min, local_max = extrema(prmsl, mode='wrap', window=50)
# create Basemap instance for the Miller Cylindrical projection
m = \
Basemap(llcrnrlon=0,llcrnrlat=-80,urcrnrlon=360,urcrnrlat=80,projection='mill')
# add wrap-around point in longitude.
prmsl, lons = addcyclic(prmsl, lons1)
# contour levels
clevs = np.arange(900,1100.,5.)
# find x,y of map projection grid.
# Meshgird returns coordinate matrices from two coordinate vectors.
lons, lats = np.meshgrid(lons, lats)
x, y = m(lons, lats)
# create figure.
fig=plt.figure(figsize=(8,4.5))
ax = fig.add_axes([0.05,0.05,0.9,0.85])
cs = m.contour(x,y,prmsl,clevs,colors='k',linewidths=1.)
m.drawcoastlines(linewidth=1.25)
m.fillcontinents(color='0.8')
m.drawparallels(np.arange(-80,81,20),labels=[1,1,0,0])
m.drawmeridians(np.arange(0,360,60),labels=[0,0,0,1])
xlows = x[local_min]; xhighs = x[local_max]
ylows = y[local_min]; yhighs = y[local_max]
lowvals = prmsl[local_min]; highvals = prmsl[local_max]
# plot lows as blue L's, with min pressure value underneath.
xyplotted = []
# don't plot if there is already a L or H within dmin meters.
yoffset = 0.022*(m.ymax-m.ymin)
dmin = yoffset
for x,y,p in zip(xlows, ylows, lowvals):
if x < m.xmax and x > m.xmin and y < m.ymax and y > m.ymin:
dist = [np.sqrt((x-x0)**2+(y-y0)**2) for x0,y0 in xyplotted]
if not dist or min(dist) > dmin:
plt.text(x,y,'L',fontsize=14,fontweight='bold',
ha='center',va='center',color='b')
plt.text(x,y-yoffset,repr(int(p)),fontsize=9,
ha='center',va='top',color='b',
bbox = dict(boxstyle="square",ec='None',fc=(1,1,1,0.5)))
xyplotted.append((x,y))
# plot highs as red H's, with max pressure value underneath.
xyplotted = []
for x,y,p in zip(xhighs, yhighs, highvals):
if x < m.xmax and x > m.xmin and y < m.ymax and y > m.ymin:
dist = [np.sqrt((x-x0)**2+(y-y0)**2) for x0,y0 in xyplotted]
if not dist or min(dist) > dmin:
plt.text(x,y,'H',fontsize=14,fontweight='bold',
ha='center',va='center',color='r')
plt.text(x,y-yoffset,repr(int(p)),fontsize=9,
ha='center',va='top',color='r',
bbox = dict(boxstyle="square",ec='None',fc=(1,1,1,0.5)))
xyplotted.append((x,y))
plt.title('Mean Sea-Level Pressure (with Highs and Lows) ' + time)
plt.show()