Recursively select elements in numpy array - python-2.7

Question

I have a text file containing latitude and temperature values for points around the globe. I would like to take the average of all the temperature points between a specified latitude interval (i.e. every degree, from the South to the North Pole). This is the code I have so far:
data_in = genfromtxt('temperatures.txt', usecols = (0,1))
lat = data_in[:,0]
temp = data_in[:,1]
in_1N = np.where((lat>=0) & (lat<=1)) # outputs an array of indexes for all latitudes between 0°-1° North
temp_1N = temp[in_1N] # outputs an array of temperature values between 0°-1° North
avg_1N = np.nanmean(temp_1N) # works out the average of temperatures between 0°-1° North
plt.scatter(1, avg_1N) # plots the average temperature against the latitude interval
plt.show()
How could I improve this code, so it can be implemented 180 times to cover the Earth between -90°S and 90°N?
Thanks

Answer 1

You could use np.histogram to put the latitudes into bins. Usually, np.histogram would merely count the number of latitudes in each bin. But if you weight the latitudes by the associated temp value, then instead of a count you get the sum of the temps. If you divide the sum of temps by the bin count, you get the average temp in each bin:
import numpy as np
import matplotlib.pyplot as plt
# N = 100
# lat = np.linspace(-90, 90, N)
# temp = 50*(1-np.cos(np.linspace(0, 2*np.pi, N)))
# temp[::5] = np.nan
# np.savetxt(filename, np.column_stack([lat, temp]))
lat, temp = np.genfromtxt('temperatures.txt', usecols = (0,1), unpack=True)
valid = np.isfinite(temp)
lat = lat[valid]
temp = temp[valid]
grid = np.linspace(-90, 90, 40)
count, bin_edges = np.histogram(lat, bins=grid)
temp_sum, bin_edges = np.histogram(lat, bins=grid, weights=temp)
temp_avg = temp_sum / count
plt.plot(bin_edges[1:], temp_avg, 'o')
plt.show()
Note that if you have scipy installed, then you could replace
the two calls to np.histogram:
count, bin_edges = np.histogram(lat, bins=grid)
temp_sum, bin_edges = np.histogram(lat, bins=grid, weights=temp)
with one call to stats.binned_statistic:
import scipy.stats as stats
temp_avg, bin_edges, binnumber = stats.binned_statistic(
x=lat, values=temp, statistic='mean', bins=grid)