This should be easy but I have just started toying with matplotlib and python. I can do a line or a scatter plot but i am not sure how to do a simple step function. Any help is much appreciated.
x = 1,2,3,4
y = 0.002871972681775004, 0.00514787917410944, 0.00863476098280219, 0.012003316194034325
It seems like you want step.
E.g.
import matplotlib.pyplot as plt
x = [1,2,3,4]
y = [0.002871972681775004, 0.00514787917410944,
0.00863476098280219, 0.012003316194034325]
plt.step(x, y)
plt.show()
If you have non-uniformly spaced data points, you can use the drawstyle keyword argument for plot:
x = [1,2.5,3.5,4]
y = [0.002871972681775004, 0.00514787917410944,
0.00863476098280219, 0.012003316194034325]
plt.plot(x, y, drawstyle='steps-pre')
Also available are steps-mid and steps-post.
New in matplotlib 3.4.0
There is a new plt.stairs method to complement plt.step:
plt.stairs and the underlying StepPatch provide a cleaner interface for plotting stepwise constant functions for the common case that you know the step edges.
This supersedes many use cases of plt.step, for instance when plotting the output of np.histogram.
Check out the official matplotlib gallery for how to use plt.stairs and StepPatch.
When to use plt.step vs plt.stairs
Use the original plt.step if you have reference points. Here the steps are anchored at [1,2,3,4] and extended to the left:
plt.step(x=[1,2,3,4], y=[20,40,60,30])
Use the new plt.stairs if you have edges. The previous [1,2,3,4] step points correspond to [1,1,2,3,4] stair edges:
plt.stairs(values=[20,40,60,30], edges=[1,1,2,3,4])
Using plt.stairs with np.histogram
Since np.histogram returns edges, it works directly with plt.stairs:
data = np.random.normal(5, 3, 3000)
bins = np.linspace(0, 10, 20)
hist, edges = np.histogram(data, bins)
plt.stairs(hist, edges)
I think you want pylab.bar(x,y,width=1) or equally pyplot's bar method. if not checkout the gallery for the many styles of plots you can do. Each image comes with example code showing you how to make it using matplotlib.
Draw two lines, one at y=0, and one at y=1, cutting off at whatever x your step function is for.
e.g. if you want to step from 0 to 1 at x=2.3 and plot from x=0 to x=5:
import matplotlib.pyplot as plt
# _
# if you want the vertical line _|
plt.plot([0,2.3,2.3,5],[0,0,1,1])
#
# OR:
# _
# if you don't want the vertical line _
#plt.plot([0,2.3],[0,0],[2.3,5],[1,1])
# now change the y axis so we can actually see the line
plt.ylim(-0.1,1.1)
plt.show()
In case someone just wants to stepify some data rather than actually plot it:
def get_x_y_steps(x, y, where="post"):
if where == "post":
x_step = [x[0]] + [_x for tup in zip(x, x)[1:] for _x in tup]
y_step = [_y for tup in zip(y, y)[:-1] for _y in tup] + [y[-1]]
elif where == "pre":
x_step = [_x for tup in zip(x, x)[:-1] for _x in tup] + [x[-1]]
y_step = [y[0]] + [_y for tup in zip(y, y)[1:] for _y in tup]
return x_step, y_step
Related
I have a file with 3 columns of data: Zenith (Z, from 0 to 90°) and Azimuth (A, from 0 to 360°). And radiance as the color variable.
I need to use python with matplotlib to plot this data into something resembling this:
This is my code so far (it returns an error):
import matplotlib.pyplot as plt
import numpy as np
# `data` has the following shape:
# [
# [Zenith value going from 0 to 90],
# [Azimuth values (0 to 365) increasing by 1 and looping back after 365],
# [radiance: floats that need to be mapped by the color value]
#]
data = [[6.000e+00 1.200e+01 1.700e+01 2.300e+01 2.800e+01 3.400e+01 3.900e+01
4.500e+01 5.000e+01 5.600e+01 6.200e+01 6.700e+01 7.300e+01 7.800e+01
8.400e+01 8.900e+01 3.934e+01 4.004e+01 4.054e+01 4.114e+01 4.154e+01
4.204e+01 4.254e+01 4.294e+01 4.334e+01 4.374e+01 4.414e+01 4.454e+01
4.494e+01 4.534e+01 4.564e+01 4.604e+01 4.644e+01 4.684e+01 4.714e+01
4.754e+01 4.794e+01 4.824e+01 4.864e+01 4.904e+01 4.944e+01 4.984e+01
5.014e+01 5.054e+01 5.094e+01 5.134e+01 5.174e+01 5.214e+01 5.264e+01
5.304e+01 5.344e+01 5.394e+01 5.444e+01 5.494e+01 5.544e+01 5.604e+01
5.674e+01 5.764e+01]
[1.960e+02 3.600e+01 2.360e+02 7.600e+01 2.760e+02 1.160e+02 3.160e+02
1.560e+02 3.560e+02 1.960e+02 3.600e+01 2.360e+02 7.600e+01 2.760e+02
1.160e+02 3.160e+02 6.500e+00 3.400e+00 3.588e+02 2.500e+00 3.594e+02
3.509e+02 5.000e-01 6.900e+00 1.090e+01 3.478e+02 1.250e+01 1.050e+01
7.300e+00 2.700e+00 3.571e+02 3.507e+02 1.060e+01 3.200e+00 3.556e+02
3.480e+02 7.300e+00 3.597e+02 3.527e+02 1.260e+01 6.600e+00 1.200e+00
3.570e+02 3.538e+02 3.520e+02 3.516e+02 3.528e+02 3.560e+02 1.200e+00
8.800e+00 3.567e+02 1.030e+01 6.800e+00 8.300e+00 3.583e+02 3.581e+02
3.568e+02 3.589e+02]
[3.580e-04 6.100e-04 3.220e-04 4.850e-04 4.360e-04 2.910e-04 1.120e-03
2.320e-04 4.300e-03 2.680e-04 1.700e-03 3.790e-04 7.460e-04 8.190e-04
1.030e-03 3.650e-03 3.050e-03 3.240e-03 3.340e-03 3.410e-03 3.490e-03
3.290e-03 3.630e-03 3.510e-03 3.320e-03 3.270e-03 3.280e-03 3.470e-03
3.720e-03 3.960e-03 3.980e-03 3.700e-03 3.630e-03 4.100e-03 4.080e-03
3.600e-03 3.990e-03 4.530e-03 4.040e-03 3.630e-03 4.130e-03 4.370e-03
4.340e-03 4.210e-03 4.100e-03 4.090e-03 4.190e-03 4.380e-03 4.460e-03
4.080e-03 4.420e-03 3.960e-03 4.230e-03 4.120e-03 4.440e-03 4.420e-03
4.370e-03 4.380e-03]]
rad = data[0]
azm = data[1]
# From what I understand, I need to create a meshgrid from the zenith and azimuth values
r, th = np.meshgrid(rad, azm)
z = data[2] # This doesn't work as `pcolormesh` expects this to be a 2d array
plt.subplot(projection="polar")
plt.pcolormesh(th, r, z, shading="auto")
plt.plot(azm, r, color="k", ls="none")
plt.show()
Note: my actual data goes on for 56k lines and looks like this (Ignore the 4th column):
The example data above is my attempt to reduce the resolution of this massive file, so I only used 1/500 of the lines of data. This might be the wrong way to reduce the resolution, please correct me if it is!
Every tutorial I've seen generate the z value from the r array generated by meshgrid. This is leaving me confused about how I would convert my z column into a 2d array that would properly map to the zenith and azimuth values.
They'll use something like this:
z = (r ** 2.0) / 4.0
So, taking the exact shape of r and applying a transformation to create the color.
The solution was in the data file all along. I needed to better understand what np.meshrid actually did. Turns out the data already is a 2d array, it just needed to be reshaped. I also found a flaw in the file, fixing it reduced its lines from 56k to 15k. This was small enough that I did not need to reduce the resolution.
Here's how I reshaped my data, and what the solution looked like:
import matplotlib.pyplot as plt
import numpy as np
with open("data.txt") as f:
lines = np.array(
[
[float(n) for n in line.split("\t")]
for i, line in enumerate(f.read().splitlines())
]
)
data = [np.reshape(a, (89, 180)) for a in lines.T]
rad = np.radians(data[1])
azm = data[0]
z = data[2]
plt.subplot(projection="polar")
plt.pcolormesh(rad, azm, z, cmap="coolwarm", shading="auto")
plt.colorbar()
plt.show()
The simplest way to plot the given data is with a polar scatter plot.
Using blue for low values and red for high values, it could look like:
import matplotlib.pyplot as plt
import numpy as np
data = [[6.000e+00, 1.200e+01, 1.700e+01, 2.300e+01, 2.800e+01, 3.400e+01, 3.900e+01, 4.500e+01, 5.000e+01, 5.600e+01, 6.200e+01, 6.700e+01, 7.300e+01, 7.800e+01, 8.400e+01, 8.900e+01, 3.934e+01, 4.004e+01, 4.054e+01, 4.114e+01, 4.154e+01, 4.204e+01, 4.254e+01, 4.294e+01, 4.334e+01, 4.374e+01, 4.414e+01, 4.454e+01, 4.494e+01, 4.534e+01, 4.564e+01, 4.604e+01, 4.644e+01, 4.684e+01, 4.714e+01, 4.754e+01, 4.794e+01, 4.824e+01, 4.864e+01, 4.904e+01, 4.944e+01, 4.984e+01, 5.014e+01, 5.054e+01, 5.094e+01, 5.134e+01, 5.174e+01, 5.214e+01, 5.264e+01, 5.304e+01, 5.344e+01, 5.394e+01, 5.444e+01, 5.494e+01, 5.544e+01, 5.604e+01, 5.674e+01, 5.764e+01],
[1.960e+02, 3.600e+01, 2.360e+02, 7.600e+01, 2.760e+02, 1.160e+02, 3.160e+02, 1.560e+02, 3.560e+02, 1.960e+02, 3.600e+01, 2.360e+02, 7.600e+01, 2.760e+02, 1.160e+02, 3.160e+02, 6.500e+00, 3.400e+00, 3.588e+02, 2.500e+00, 3.594e+02, 3.509e+02, 5.000e-01, 6.900e+00, 1.090e+01, 3.478e+02, 1.250e+01, 1.050e+01, 7.300e+00, 2.700e+00, 3.571e+02, 3.507e+02, 1.060e+01, 3.200e+00, 3.556e+02, 3.480e+02, 7.300e+00, 3.597e+02, 3.527e+02, 1.260e+01, 6.600e+00, 1.200e+00, 3.570e+02, 3.538e+02, 3.520e+02, 3.516e+02, 3.528e+02, 3.560e+02, 1.200e+00, 8.800e+00, 3.567e+02, 1.030e+01, 6.800e+00, 8.300e+00, 3.583e+02, 3.581e+02, 3.568e+02, 3.589e+02],
[3.580e-04, 6.100e-04, 3.220e-04, 4.850e-04, 4.360e-04, 2.910e-04, 1.120e-03, 2.320e-04, 4.300e-03, 2.680e-04, 1.700e-03, 3.790e-04, 7.460e-04, 8.190e-04, 1.030e-03, 3.650e-03, 3.050e-03, 3.240e-03, 3.340e-03, 3.410e-03, 3.490e-03, 3.290e-03, 3.630e-03, 3.510e-03, 3.320e-03, 3.270e-03, 3.280e-03, 3.470e-03, 3.720e-03, 3.960e-03, 3.980e-03, 3.700e-03, 3.630e-03, 4.100e-03, 4.080e-03, 3.600e-03, 3.990e-03, 4.530e-03, 4.040e-03, 3.630e-03, 4.130e-03, 4.370e-03, 4.340e-03, 4.210e-03, 4.100e-03, 4.090e-03, 4.190e-03, 4.380e-03, 4.460e-03, 4.080e-03, 4.420e-03, 3.960e-03, 4.230e-03, 4.120e-03, 4.440e-03, 4.420e-03, 4.370e-03, 4.380e-03]]
rad = np.radians(data[1])
azm = data[0]
z = data[2]
plt.subplot(projection="polar")
plt.scatter(rad, azm, c=z, cmap='coolwarm')
plt.colorbar()
plt.show()
Creating such a scatter plot with your real data gives an idea how it looks like. You might want to choose a different colormap, depending on what you want to convey. You also can choose a smaller dot size (for example plt.scatter(rad, azm, c=z, cmap='plasma', s=1, ec='none')) if there would be too many points.
A simple way to create a filled image from non-gridded data uses tricontourf with 256 colors (it looks quite dull with the given data, so I didn't add an example plot):
plt.subplot(projection="polar")
plt.tricontourf(rad, azm, z, levels=256, cmap='coolwarm')
I want to have the legend of the plot shown with the value in a list. But what I get is the element index but not the value itself. I dont know how to fix it. I'm referring to the plt.plot line. Thanks for the help.
import matplotlib.pyplot as plt
import numpy as np
x = np.random.random(1000)
y = np.random.random(1000)
n = len(x)
d_ij = []
for i in range(n):
for j in range(i+1,n):
a = np.sqrt((x[i]-x[j])**2+(y[i]-y[j])**2)
d_ij.append(a)
epsilon = np.linspace(0.01,1,num=10)
sigma = np.linspace(0.01,1,num=10)
def lj_pot(epsi,sig,d):
result = []
for i in range(len(d)):
a = 4*epsi*((sig/d[i])**12-(sig/d[i])**6)
result.append(a)
return result
for i in range(len(epsilon)):
for j in range(len(sigma)):
a = epsilon[i]
b = sigma[j]
plt.cla()
plt.ylim([-1.5, 1.5])
plt.xlim([0, 2])
plt.plot(sorted(d_ij),lj_pot(epsilon[i],sigma[j],sorted(d_ij)),label = 'epsilon = %d, sigma =%d' %(a,b))
plt.legend()
plt.savefig("epsilon_%d_sigma_%d.png" % (i,j))
plt.show()
Your code is a bit unpythonic, so I tried to clean it up to the best of my knowledge. numpy.random.random and numpy.random.uniform(0, 1) are basically the same, however, the latter also allows you to pass the shape of the return array that you would like to have, in this case an array with 1000 rows and two columns (1000, 2). I then use some magic to assign the two colums of the return array to x and y in the same line, respectively.
numpy.hypot does as the name suggests and calculates the hypothenuse of x and y. It can also do that for each entry of arrays with the same size, saving you the for loops, which you should try to aviod in Python since they are pretty slow.
You used plt for all your plotting, which is fine as long as you only have one figure, but I would recommend to be as explicit as possible, according to one of Python's key notions:
explicit is better than implicit.
I recommend you read through this guide, in particular the section called 'Stateful Versus Stateless Approaches'. I changed your commands accordingly.
It is also very unpythonic to loop over items of a list using the index of the item in the list like you did (for i in range(len(list)): item = list[i]). You can just reference the item directly (for item in list:).
Lastly I changed your formatted strings to the more convenient f-strings. Have a read here.
import matplotlib.pyplot as plt
import numpy as np
def pot(epsi, sig, d):
result = 4*epsi*((sig/d)**12 - (sig/d)**6)
return result
# I am not sure why you would create the independent variable this way,
# maybe you are simulating something. In that case, the code below is
# simpler than your version and should achieve the same.
# x, y = zip(*np.random.uniform(0, 1, (1000, 2)))
# d = np.array(sorted(np.hypot(x, y)))
# If you only want to plot your pot function then creating the value range
# like this is just fine.
d = np.linspace(0.001, 1, 1000)
epsilons = sigmas = np.linspace(0.01, 1, num=10)
fig, ax = plt.subplots()
ax.set_xlim([0, 2])
ax.set_ylim([-1.5, 1.5])
line = None
for epsilon in epsilons:
for sigma in sigmas:
if line is None:
line = ax.plot(
d, pot(epsilon, sigma, d),
label=f'epsilon = {epsilon}, sigma = {sigma}'
)[0]
fig.legend()
else:
line.set_data(d, pot(epsilon, sigma, d))
# plt.savefig(f"epsilon_{epsilon}_sigma_{sigma}.png")
fig.show()
I try to generate a graph and save an image of the graph in python. Although the "plotting" of the values seems ok and I can get my picture, the scale of the graph is badly shifted.
If you compare the correct graph from tutorial example with my bad graph generated from different dataset, the curves are cut at the bottom to early: Y-axis should start just above the highest values and I should also see the curves for the highest X-values (in my case around 10^3).
But honestly, I think that problem is the scale of the y-axis, but actually do not know what parameteres should I change to fix it. I tried to play with some numbers (see below script), but without any good results.
This is the code for calculation and generation of the graph image:
import numpy as np
hic_data = load_hic_data_from_reads('/home/besy/Hi-C/MOREX/TCC35_parsedV2/TCC35_V2_interaction_filtered.tsv', resolution=100000)
min_diff = 1
max_diff = 500
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12, 12))
for cnum, c in enumerate(hic_data.chromosomes):
if c in ['ChrUn']:
continue
dist_intr = []
for diff in xrange(min_diff, min((max_diff, 1 + hic_data.chromosomes[c]))):
beg, end = hic_data.section_pos[c]
dist_intr.append([])
for i in xrange(beg, end - diff):
dist_intr[-1].append(hic_data[i, i + diff])
mean_intrp = []
for d in dist_intr:
if len(d):
mean_intrp.append(float(np.nansum(d)) / len(d))
else:
mean_intrp.append(0.0)
xp, yp = range(min_diff, max_diff), mean_intrp
x = []
y = []
for k in xrange(len(xp)):
if yp[k]:
x.append(xp[k])
y.append(yp[k])
l = plt.plot(x, y, '-', label=c, alpha=0.8)
plt.hlines(mean_intrp[2], 3, 5.25 + np.exp(cnum / 4.3), color=l[0].get_color(),
linestyle='--', alpha=0.5)
plt.text(5.25 + np.exp(cnum / 4.3), mean_intrp[2], c, color=l[0].get_color())
plt.plot(3, mean_intrp[2], '+', color=l[0].get_color())
plt.xscale('log')
plt.yscale('log')
plt.ylabel('number of interactions')
plt.xlabel('Distance between bins (in 100 kb bins)')
plt.grid()
plt.ylim(2, 250)
_ = plt.xlim(1, 110)
fig.savefig('/home/besy/Hi-C/MOREX/TCC35_V2_results/filtered/TCC35_V2_decay.png', dpi=fig.dpi)
I think that problem is in scale I need y-axis to start from 10^-1 (0.1), in order to change this I tried this:
min_diff = 0.1
.
.
.
dist_intr = []
for diff in xrange(min_diff, min((max_diff, 0.1 + hic_data.chromosomes[c]))):
.
.
.
plt.ylim((0.1, 20))
But this values return: "integer argument expected, got float"
I also tried to play with:
max_diff, plt.ylim and plt.xlim parameters little bit, but nothing changed to much.
I would like to ask you what parameter/s and how I need change to generate image of the correctly focused graph. Thank you in advance.
How can I create a boxplot like the one below, in Python? I want to depict means and confidence bounds only (rather than proportions of IQRs, as in matplotlib boxplot).
I don't have any version constraints, and if your answer has some package dependency that's OK too. Thanks!
Use errorbar instead. Here is a minimal example:
import matplotlib.pyplot as plt
x = [2, 4, 3]
y = [1, 3, 5]
errors = [0.5, 0.25, 0.75]
plt.figure()
plt.errorbar(x, y, xerr=errors, fmt = 'o', color = 'k')
plt.yticks((0, 1, 3, 5, 6), ('', 'x3', 'x2', 'x1',''))
Note that boxplot is not the right approach; the conf_intervals parameter only controls the placement of the notches on the boxes (and we don't want boxes anyway, let alone notched boxes). There is no way to customize the whiskers except as a function of IQR.
Thanks to America, I propose a way to automatize this kind of graph a little bit.
Below an example of code generating 20 arrays from a normal distribution with mean=0.25 and std=0.1.
I used the formula W = t * s / sqrt(n), to calculate the margin of error of the confidence interval, with t the constant from the t distribution (see scipy.stats.t), s the standard deviation and n the number of values in an array.
list_samples=list() # making a list of arrays
for i in range(20):
list.append(np.random.normal(loc=0.25, scale=0.1, size=20))
def W_array(array, conf=0.95): # function that returns W based on the array provided
t = stats.t(df = len(array) - 1).ppf((1 + conf) /2)
W = t * np.std(array, ddof=1) / np.sqrt(len(array))
return W # the error
W_list = list()
mean_list = list()
for i in range(len(list_samples)):
W_list.append(W_array(list_samples[i])) # makes a list of W for each array
mean_list.append(np.mean(list_samples[i])) # same for the means to plot
plt.errorbar(x=mean_list, y=range(len(list_samples)), xerr=W_list, fmt='o', color='k')
plt.axvline(.25, ls='--') # this is only to demonstrate that 95%
# of the 95% CI contain the actual mean
plt.yticks([])
plt.show();
I need to add several rectangles in my basemap. I nee four rectangles with lat and log ranges as below.
1) llcrnrlon=-10, urcrnrlon=10, llcrnrlat=35,urcrnrlat=60
2) llcrnrlon=10.5, urcrnrlon=35, llcrnrlat=35,urcrnrlat=60
3) llcrnrlon=35.5, urcrnrlon=52, llcrnrlat=30,urcrnrlat=55
4) llcrnrlon=-20, urcrnrlon=35, llcrnrlat=20,urcrnrlat=34.5
My script is below. I found "polygon" packages to add lines but I do not exactly know how to do. Please help me!! Thanks a lot for your help in advance!
from mpl_toolkits.basemap import Basemap
m=basemaputpart.Basemap(llcrnrlon=-60, llcrnrlat=20, urcrnrlon=60, urcrnrlat=70, resolution='i', projection='cyl', lon_0=0, lat_0=45)
lon1=np.array([[-180.+j*0.5 for j in range(721)] for i in range(181)])
lat1=np.array([[i*0.5 for j in range(721)] for i in range(181) ])
Nx1,Ny1=m(lon1,lat1,inverse=False)
toplot=data[:,:]
toplot[data==0]=np.nan
toplot=np.ma.masked_invalid(toplot)
plt.pcolor(Nx1,Ny1,np.log(toplot),vmin=0, vmax=5)
cbar=plt.colorbar()
m.drawcoastlines(zorder=2)
m.drawcountries(zorder=2)
llcrnrlon = -10
urcrnrlon = 10
llcrnrlat = 35
urcrnrlat = 60
lower_left = (llcrnrlon, llcrnrlat)
lower_right= (urcrnrlon, llcrnrlat)
upper_left = (llcrnrlon, urcrnrlat)
upper_right= (urcrnrlon, urcrnrlat)
plot_rec(m, lower_left, upper_left, lower_right, upper_right)
Then I see "Type Error: 'tuple' object is not callable"
Instead of this part, I added one you suggested first.
..
m.drawcoastlines(zorder=2)
m.drawcountries(zorder=2)
def plot_rec(m, lower_left, upper_left, lower_right, upper_right):
xs = [lower_left[-10], upper_left[-10],
lower_right[10], upper_right[10]]
ys = [lower_left[35], upper_left[60],
lower_right[35], upper_right[60]]
m.plot(xs,ys,latlon=True)
plt.show()
Then I do not see any box in my plot. I have to put another, not plt.show()??
Also, would you let me know how to put number in the box (e.g., 1 in the upper left conner of the box) ? How to get sum of values in all points of my data and get percentage of (sume of values in the box) over (sum of values in all points of my data)? I ask too much.. Just let me know what you can give me, it would be anyway great!!!
THanks a lot!!
The tricky thing about this is that a 'rectangle' isn't really a 'rectangle' on many, many projection types. So when you say 'rectangle', do you mean an actual rectangle in map-space, or simply a rectangle in pixel-space? The two require very different
But let's assume you want it in map-space. The quickest way is to just use Basemap's plot method, like so:
def plot_rec(bmap, lower_left, upper_left, lower_right, upper_right):
xs = [lower_left[0], upper_left[0],
lower_right[0], upper_right[0],
lower_left[0], lower_right[0],
upper_left[0], upper_right[0]]
ys = [lower_left[1], upper_left[1],
lower_right[1], upper_right[1],
lower_left[1], lower_right[1],
upper_left[1], upper_right[1]]
bmap.plot(xs, ys, latlon = True)
where bmap is your map, and lower_left etc. are lon-lat tuples at those corners.
Update with use examples:
You asked for a usage example, so here you go:
m=basemaputpart.Basemap(llcrnrlon=-60, llcrnrlat=20, urcrnrlon=60, urcrnrlat=70, resolution='i', projection='cyl', lon_0=0, lat_0=45)
# your other setting up the map code here
# here I draw the first rectangle
llcrnrlon = -10
urcrnrlon = 10
llcrnrlat = 35
urcrnrlat = 60
lower_left = (llcrnrlon, llcrnrlat)
lower_right= (urcrnrlon, llcrnrlat)
upper_left = (llcrnrlon, urcrnrlat)
upper_right= (urcrnrlon, urcrnrlat)
plot_rec(m, lower_left, upper_left, lower_right, upper_right) # This calls the function I defined before
# Rinse and repeat for the other lat/lon combos
plt.show()
You can definitely do this more elegantly using list comprehensions to generate the correct corner-point sets, but this should get you started.
Update 2
So it appears there is some confusion here. plot_rec is a function. It should be placed somewhere not inline with the rest of your script. By itself, it doesn't do anything. It does when you call it here:
...
upper_left = (llcrnrlon, urcrnrlat)
upper_right= (urcrnrlon, urcrnrlat)
# This next line is where we call the function
plot_rec(m, lower_left, upper_left, lower_right, upper_right)
This function bellow should do what you need. bmap is your basemap object, and lonmin,lonmax,latmin,latmax defines your domain in latitude/longitude terms. You need to call the plot_rectangle function after bmap has been generated in your code by a call to Basemap(...).
def plot_rectangle(bmap, lonmin,lonmax,latmin,latmax):
xs = [lonmin,lonmax,lonmax,lonmin,lonmin]
ys = [latmin,latmin,latmax,latmax,latmin]
bmap.plot(xs, ys,latlon = True)
If, instead, your are looking for rectangles in the map space, whose borders follows paralleles and meridians, the following function works for me. m is the basemap object.
def draw_screen_poly( minlat, maxlat, minlon, maxlon, m):
lons=np.hstack((np.repeat(minlon,10),\
np.linspace(minlon,maxlon, num=10),\
np.repeat(maxlon,10),\
np.linspace(maxlon,minlon, num=10)))
lats=np.hstack((np.linspace(minlat,maxlat, num=10),\
np.repeat(maxlat,10),\
np.linspace(maxlat,minlat, num=10),
np.repeat(minlat,10)))
m.plot(y=lats,x=lons,latlon=True, lw=2, color='navy', alpha=0.8)
x, y = m(lons, lats)
xy = zip(x,y)
poly = Polygon( np.asarray(xy), linewidth=3)
ax.add_patch(poly)