Develop Reference

How do I change a Cartopy contour plot into box plot

I have plotted a CartoPy contour plot which looks like this:
using the following script:
precip_full1 = xr.open_dataset('era_yr1979.nc')
precip_full2 = xr.open_dataset('era_yr1980.nc')
precip_full3 = xr.open_dataset('era_yr1981.nc')
precip_full4 = xr.open_dataset('era_yr1982.nc')
precip_full5 = xr.open_dataset('era_yr1983.nc')
precip_full6 = xr.open_dataset('era_yr1984.nc')
precip_full = xr.concat([precip_full1,precip_full2,precip_full3,precip_full4,precip_full5,precip_full6],dim = 'time')
output = []
for x in np.arange(6.5,10.25,0.25):
for y in np.arange(-15,-9.75,0.25):
precip = precip_full.where((precip_full.latitude==x)&(precip_full.longitude==y),drop=True)
roll = precip.rolling(time=6,center=False).sum()
annual = roll.groupby('time.year').max()
tab = annual.to_dataframe().rename(columns={'tp':6})
output = pd.concat(output,1)
mean = output.mean()
data_mean = pd.DataFrame(mean, columns=['mean'])
df = data_mean.to_numpy()
new = [df[i:i+21] for i in range(0,len(df),21)]
new = np.reshape(new, [-1, 21])
df = pd.DataFrame(data=new, dtype=object)
lon2d, lat2d = np.meshgrid(lon, lat)
plt.figure(figsize=(6,5))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([-15,-10,6.5,10])
ax.coastlines()
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.LAKES)
ax.add_feature(cfeature.RIVERS)
ax.add_feature(cfeature.BORDERS)
gl = ax.gridlines(draw_labels=True, xlocs=np.arange(-180,180,0.25), ylocs=np.arange(-90,90,0.25),linewidth=0.4)
gl.top_labels = False
gl.right_labels = False
plot = plt.contourf(lon2d, lat2d, df, cmap = 'jet', transform=ccrs.PlateCarree())
I've now realized I'd prefer a box plot, with one solid colour in each grid box, I no longer want interpolation between grid points.
I found that I can use pcolormesh instead of contour to do this. However, when I change the last line of code:
plot = plt.pcolormesh(lon2d, lat2d, df, cmap = 'jet', transform=ccrs.PlateCarree())
I get the following error:
TypeError: Dimensions of C (15, 21) are incompatible with X (15) and/or Y (15)
I can't see what this error means to know how to fix it. Has anyone done anything similar?

OK, since you've asked... here's a quick example how you can do it with EOmaps...
Note that data and coordinates can be provided as 1D or 2D arrays (or mixtures of 1D and 2D as below) or as a pandas.DataFrames.
It's also possible to plot directly from a NetCDFs (or GeoTIFFs) via m.new_layer_from_file.NetCDF(...)
from eomaps import Maps
import numpy as np
# create some data in a regular lon/lat grid (=epsg 4326)
x, dx = np.linspace(-45, 45, 55, retstep=True)
y, dy = np.linspace(-20, 30, 25, retstep=True)
vals = np.random.randint(0,100, (x.size, y.size))
# plot the data as lon/lat rectangles on a map displayed in Orthographic projection.
m = Maps(Maps.CRS.Orthographic())
m.add_feature.preset.coastline()
m.set_data(vals, x, y, crs=4326)
m.set_shape.rectangles(radius=(dx/2, dy/2), radius_crs=4326)
m.plot_map()

changing colorbar sensitivity

I re edited the question in order to make it more clear with what I need help. So I have the following code so far
#import the libraries
import netCDF4 as nc
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
#check what variables are included
# in our case : dict_keys(['time', 'time_bnds', 'lat', 'lat_bnds', 'lon', 'lon_bnds', 'height', 'tas'])
print(data.variables.keys())
#extract the data
lats = data.variables['lat'][:]
lons = data.variables['lon'][:]
time = data.variables['time'][:]
te = data.variables['tas'][:]-273.5
#extract the coordinates we want to plot (Italy)by inputting lower #left and top right coordinates
italy = Basemap(projection='merc',
#ITALY
llcrnrlon= 6.63,
llcrnrlat= 35.29,
urcrnrlon= 18.78,
urcrnrlat= 47.09,
resolution= 'f')
#create the plot
lon,lat =np.meshgrid(lons,lats)
x,y=italy(lon,lat)
c_scheme = italy.pcolor(x,y,np.squeeze(te[0,:,:]), cmap ='jet')
italy.drawcoastlines()
italy.drawstates()
italy.drawcounties()
cbar = italy.colorbar(c_scheme, location='right',pad='10%')
plt.title('KIOST-ESM')
plt.show()
which creates the following heat map
Now what I want is to make the plot identify(colour) smaller differences between the temperatures. However I do not know exactly how to find the min and max value of my area that I am investigating (this time it is Italy). My variable te that has the temperatures is of the type : numpy.ma.core.MaskedArray with shape (9125, 160, 320) [time,lon,lat]

Setting vmin and vmax values should solve this. please try
min_height = int(np.min(np.squeeze(te[0,:,:])))
max_height = int(np.max(np.squeeze(te[0,:,:])))
c_scheme = italy.pcolor(x,y,np.squeeze(te[0,:,:]), cmap ='jet', vmin=min_height, vmax=max_height)
In the case that the te array holds is bigger than the mesh in x and y, I would try doing the following:
min_height = int(np.min(np.squeeze(te[0,x,y])))
max_height = int(np.max(np.squeeze(te[0,x,y])))

How to plot a shapefile centered in the Pacific with Basemap?

When plotting with Basemap's readshapefile, if the defined map is centered anywhere else than the longitudinal center of the shapefile, only a portion of it it's plotted. Here's an example using Natural Earth's coastlines:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
shpf = './NaturalEarth/ne_50m_land/ne_50m_land'
fig, ax = plt.subplots(nrows=1, ncols=1, dpi=100)
m = Basemap(
ax = ax,
projection = 'cyl',
llcrnrlon = 0, llcrnrlat = -90,
urcrnrlon = 360, urcrnrlat = 90
)
m.readshapefile(shpf,'ne_50m_land')
m.drawmeridians(np.arange(0,360,45),labels=[True,False,False,True])
Which produces:
Is there a workaround for this with Basemap or Python? I know some people re-center the shapefile in QGIS or similar, but it seems unpractical to do so every time you create a new map, and my QGIS skills are extremely basic.

One way to do it would be to tell readshapefile not to plot the coastlines directly and then to manipulate the line segments before plotting them yourself. Here an example based on your use case:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
shpf = 'shapefiles/ne_50m_land'
fig, ax = plt.subplots(nrows=1, ncols=1, dpi=100)
m = Basemap(
ax = ax,
projection = 'cyl',
llcrnrlon = 0, llcrnrlat = -90,
urcrnrlon = 360, urcrnrlat = 90
)
m.readshapefile(shpf,'ne_50m_land', drawbounds = False)
boundary = 0.0
for info, shape in zip(m.ne_50m_land_info, m.ne_50m_land):
lons, lats = map(np.array, zip(*shape))
sep = (lons <= boundary).astype(int)
roots = np.where(sep[:-1]+sep[1:] == 1)[0]+1
lower = np.concatenate([[0],roots]).astype(int)
upper = np.concatenate([roots,[len(lons)]]).astype(int)
for low, high in zip(lower,upper):
lo_patch = lons[low:high]
la_patch = lats[low:high]
lo_patch[lo_patch<0] += 360
x,y = m(lo_patch,la_patch)
ax.plot(x,y,'k',lw=0.5)
m.drawmeridians(np.arange(0,360,45),labels=[True,False,False,True])
plt.show()
In the example above, I iterate through the line segments of the shape file the way it is explained in the Basemap documentation. First I thought it would be enough to just add 360 to each point with a longitude smaller 0, but then you would get horizontal lines whenever a coast line crosses the 0 degree line. So, instead, one has to cut the lines into smaller segments whenever such a crossing appears. This is quite easily accomplished with numpy. I then use the plot command to draw the coast lines. If you want to do something more complex have a look at the Basemap documentation.
The final result looks like this:
Hope this helps.

Lines not plotting on graph using Python/Basemap

Sorry if this question is simple I'm a newb to using Python and Basemap. Anyway I'm trying to plot the path of 20 hurricanes on a map (graph). The map itself and the legend show up perfectly but the paths of the hurricanes do not. Also I'm not getting any traceback messages but I think I have an idea of where my problem may be. Could someone please tell me where I went wrong.
Here's a sample of the csv file:
Year, Name, Type, Latitude, Longitude
1957,AUDREY,HU, 21.6, 93.3
1957,AUDREY,HU,22.0, 93.4
1957,AUDREY,HU,22.6, 93.5
1969,AUDREY,HU,28.2,99.6
1957,AUDREY,HU,26.5,93.8
1957,AUDREY,HU,27.9,93.8
1957,AUDREY,HU,29.3,95
1957,AUDREY,HU,27.9,93.8
1957,AUDREY,HU,29.3,93.8
1957,AUDREY,HU,30.7,93.5
1969,CAMILLE,HU, 21.6,99.3
1969,CAMILLE,HU,22.0,98.4
1969,CAMILLE,HU,22.6,90.5
1969,CAMILLE,HU,23.2,93.6
Here's the code I have so far:
import numpy as np
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import csv, os, scipy
import pandas
from PIL import *
data = np.loadtxt('louisianastormb.csv',dtype=np.str,delimiter=',',skiprows=1)
'''print data'''
fig = plt.figure(figsize=(12,12))
ax = fig.add_axes([0.1,0.1,0.8,0.8])
m = Basemap(llcrnrlon=-100.,llcrnrlat=0.,urcrnrlon=-20.,urcrnrlat=57.,
projection='lcc',lat_1=20.,lat_2=40.,lon_0=-60.,
resolution ='l',area_thresh=1000.)
m.bluemarble()
m.drawcoastlines(linewidth=0.5)
m.drawcountries(linewidth=0.5)
m.drawstates(linewidth=0.5)
# Creates parallels and meridians
m.drawparallels(np.arange(10.,35.,5.),labels=[1,0,0,1])
m.drawmeridians(np.arange(-120.,-80.,5.),labels=[1,0,0,1])
m.drawmapboundary(fill_color='aqua')
color_dict = {'AUDREY': 'red', 'ETHEL': 'white', 'BETSY': 'yellow','CAMILLE': 'blue', 'CARMEN': 'green','BABE': 'purple', }
colnames = ['Year','Name','Type','Latitude','Longitude']
data = pandas.read_csv('louisianastormb.csv', names=colnames)
names = list(data.Name)
lat = list(data.Latitude)
long = list(data.Longitude)
colorName = list(data.Name)
#print lat
#print long
lat.pop(0)
long.pop(0)
colorName.pop(0)
latitude= map(float, lat)
longitude = map(float, long)
x, y = m(latitude,longitude)
#Plots points on map
for colorName in color_dict.keys():
plt.plot(x,y,linestyle ='-',label=colorName,color=color_dict[colorName], linewidth=5 )
lg = plt.legend()
lg.get_frame().set_facecolor('grey')
plt.show()

two (okay I lied, should be there) problems with your code
i, your input longitude should be negative to be within the boundary you defined for your basemap, so add this after before converting to x and y
longitude = [-i for i in longitude]
ii, your coordinate conversion line is wrong, you should swap lon and lat in the argument list
x, y = m(longitude, latitude)
instead of
x, y = m(latitude,longitude)
EDIT:
okay, the second question that OP posted in the comments, please check the complete code below and please pay attention to the changes I've made compared to yours
# Last-modified: 21 Oct 2013 05:35:16 PM
import numpy as np
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import csv, os, scipy
import pandas
from PIL import *
data = np.loadtxt('louisianastormb.csv',dtype=np.str,delimiter=',',skiprows=1)
'''print data'''
fig = plt.figure(figsize=(12,12))
ax = fig.add_axes([0.1,0.1,0.8,0.8])
m = Basemap(llcrnrlon=-100.,llcrnrlat=0.,urcrnrlon=-20.,urcrnrlat=57.,
projection='lcc',lat_1=20.,lat_2=40.,lon_0=-60.,
resolution ='l',area_thresh=1000.)
m.drawcoastlines(linewidth=0.5)
m.drawcountries(linewidth=0.5)
m.drawstates(linewidth=0.5)
# m.bluemarble(ax=ax)
# Creates parallels and meridians
m.drawparallels(np.arange(10.,35.,5.),labels=[1,0,0,1])
m.drawmeridians(np.arange(-120.,-80.,5.),labels=[1,0,0,1])
m.drawmapboundary(fill_color='aqua')
color_dict = {'AUDREY': 'red', 'ETHEL': 'white', 'BETSY': 'yellow','CAMILLE': 'blue', 'CARMEN': 'green','BABE': 'purple', }
colnames = ['Year','Name','Type','Latitude','Longitude']
data = pandas.read_csv('louisianastormb.csv', names=colnames)
names = list(data.Name)
lat = list(data.Latitude)
long = list(data.Longitude)
colorNames = list(data.Name)
#print lat
#print long
lat.pop(0)
long.pop(0)
colorNames.pop(0)
latitude= map(float, lat)
longitude = map(float, long)
# added by nye17
longitude = [-i for i in longitude]
# x, y = m(latitude,longitude)
x, y = m(longitude,latitude)
# convert to numpy arrays
x = np.atleast_1d(x)
y = np.atleast_1d(y)
colorNames = np.atleast_1d(colorNames)
#Plots points on map
for colorName in color_dict.keys():
plt.plot(x[colorName == colorNames],y[colorName == colorNames],linestyle ='-',label=colorName,color=color_dict[colorName], linewidth=5 )
lg = plt.legend()
lg.get_frame().set_facecolor('grey')
plt.show()

I think your difficulty is not so much in Basemap as in the plotting. Instead of plotting the entire x/y data set you need to find the x/y points corresponding on hurricane Z. Then plot only those points in a certain color c. Then find the points corresponding to the next hurricane etc...
The below, while not using the Basemap data structure should provide a starting point for plotting subsets of points based on some selector vector.
#given a list of x,y coordinates with a label we'll plot each line individually
#first construct some points to plot
x1 = [1,1.1,1.2,1.3, 2.0,2.2,2.3, 4,3.9,3.8,3.7]
y1 = [5,5.1,5.2,5.3, 6.0,6.2,6.3, 2,2.1,2.2,2.3]
pointNames = []
#generate some labels
pointNames.extend(['a']*4)
pointNames.extend(['b']*3)
pointNames.extend(['c']*4)
#make things easy by casting to numpy arrays to allow for easier indexing
x1 = numpy.array(x1)
y1 = numpy.array(y1)
pointNames = numpy.array(pointNames)
for elem in ['a','b','c']:
selector = pointNames==elem
subsetX = x1[selector]
subsetY = y1[selector]
#now plot subsetX vs subsetY in color Z
plot(subsetX,subsetY,'*-')
show()

Matplotlib contour plot with intersecting contour lines

I am trying to make a contour plot of the following data using matplotlib in python. The data is of this form -
# x y height
77.23 22.34 56
77.53 22.87 63
77.37 22.54 72
77.29 22.44 88
The data actually consists of nearly 10,000 points, which I am reading from an input file. However the set of distinct possible values of z is small (within 50-90, integers), and I wish to have a contour lines for every such distinct z.
Here is my code -
import matplotlib
import numpy as np
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import csv
import sys
# read data from file
data = csv.reader(open(sys.argv[1], 'rb'), delimiter='|', quotechar='"')
x = []
y = []
z = []
for row in data:
try:
x.append(float(row[0]))
y.append(float(row[1]))
z.append(float(row[2]))
except Exception as e:
pass
#print e
X, Y = np.meshgrid(x, y) # (I don't understand why is this required)
# creating a 2D array of z whose leading diagonal elements
# are the z values from the data set and the off-diagonal
# elements are 0, as I don't care about them.
z_2d = []
default = 0
for i, no in enumerate(z):
z_temp = []
for j in xrange(i): z_temp.append(default)
z_temp.append(no)
for j in xrange(i+1, len(x)): z_temp.append(default)
z_2d.append(z_temp)
Z = z_2d
CS = plt.contour(X, Y, Z, list(set(z)))
plt.figure()
CB = plt.colorbar(CS, shrink=0.8, extend='both')
plt.show()
Here is the plot of a small sample of data -
Here is a close look to one of the regions of the above plot (note the overlapping/intersecting lines) -
I don't understand why it doesn't look like a contour plot. The lines are intersecting, which shouldn't happen. What can be possibly wrong? Please help.

Try to use the following code. This might help you -- it's the same thing which was in the Cookbook:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.mlab import griddata
# with this way you can load your csv-file really easy -- maybe you should change
# the last 'dtype' to 'int', because you said you have int for the last column
data = np.genfromtxt('output.csv', dtype=[('x',float),('y',float),('z',float)],
comments='"', delimiter='|')
# just an assigning for better look in the plot routines
x = data['x']
y = data['y']
z = data['z']
# just an arbitrary number for grid point
ngrid = 500
# create an array with same difference between the entries
# you could use x.min()/x.max() for creating xi and y.min()/y.max() for yi
xi = np.linspace(-1,1,ngrid)
yi = np.linspace(-1,1,ngrid)
# create the grid data for the contour plot
zi = griddata(x,y,z,xi,yi)
# plot the contour and a scatter plot for checking if everything went right
plt.contour(xi,yi,zi,20,linewidths=1)
plt.scatter(x,y,c=z,s=20)
plt.xlim(-1,1)
plt.ylim(-1,1)
plt.show()
I created a sample output file with an Gaussian distribution in 2D. My result with using the code from above:
NOTE:
Maybe you noticed that the edges are kind of cropped. This is due to the fact that the griddata-function create masked arrays. I mean the border of the plot is created by the outer points. Everything outside the border is not there. If your points would be on a line then you will not have any contour for plotting. This is kind of logical. I mention it, cause of your four posted data points. It seems likely that you have this case. Maybe you don't have it =)
UPDATE
I edited the code a bit. Your problem was probably that you didn't resolve the dependencies of your input-file correctly. With the following code the plot should work correctly.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.mlab import griddata
import csv
data = np.genfromtxt('example.csv', dtype=[('x',float),('y',float),('z',float)],
comments='"', delimiter=',')
sample_pts = 500
con_levels = 20
x = data['x']
xmin = x.min()
xmax = x.max()
y = data['y']
ymin = y.min()
ymax = y.max()
z = data['z']
xi = np.linspace(xmin,xmax,sample_pts)
yi = np.linspace(ymin,ymax,sample_pts)
zi = griddata(x,y,z,xi,yi)
plt.contour(xi,yi,zi,con_levels,linewidths=1)
plt.scatter(x,y,c=z,s=20)
plt.xlim(xmin,xmax)
plt.ylim(ymin,ymax)
plt.show()
With this code and your small sample I get the following plot:
Try to use my snippet and just change it a bit. For example, I had to change for the given sample csv-file the delimitter from | to ,. The code I wrote for you is not really nice, but it's written straight foreword.
Sorry for the late response.