Jamie Mitchell <jamiemitchell1...@gmail.com> writes:
> You were right Christian I wanted a shape (2,150).
>
> Thank you Rustom and Steven your suggestion has worked.
>
> Unfortunately the data doesn't plot as I imagined.
>
> What I would like is:
>
> X-axis - hs_con_sw
> Y-axis - te_con_sw
> Z-axis - Frequency
>
> What I would like is for the Z-axis to contour the frequency or
> amount of times that the X-axis data and Y-axis data meet at a
> particular point or bin.
>
> Does anyone know what function or graph could best show this?
in my understanding, you have 3 arrays of data that describe 3D data
points, and you want to draw a 2D contour plot...
in this case you have to interpolate the z-values on a regular grid,
that's very easy if you already know what to do ;-)
here I assume that data is in a .csv file
% cat a.csv
0 ≤ x ≤ 10, 0 ≤ y ≤ 10, z = cos(sqrt((x-5)**2_(y-5)**2))
1.922065,5.827944,-0.998953
7.582322,0.559370,0.411861
5.001753,3.279957,-0.148694
...
of course my z's are different from yours, but this shouldn't be a
real problem --- and here it is my *tested* solution (tested on python
2.7, that is), please feel free to adapt to your needs
hth, ciao
g
% cat contour.py
from numpy import loadtxt, linspace
from matplotlib.mlab import griddata
import matplotlib.pyplot as pl
# open 'a.csv', specify the delimiter, specify how many header rows,
# slurp the data
temp_array = loadtxt(open('a.csv'),delimiter=',',skiprows=1)
# the shape of temp_array is (N,3), we want its transpose
temp_array = temp_array.transpose()
# now the shape is (3,N) and we can do "unpack and assignment:
x, y, z = temp_array
# now the tricky part,
# 1: create two arrays with 101 (arbitrary number) equispaced values
# between 0 and 10 --- that is the ranges of data x and data y
xi = linspace(0,10,101)
yi = linspace(0,10,101)
# 2: create, by interpolation, the 2D array that contourf so eagerly
# awaited!
print griddata.__doc__
zi = griddata(x,y,z, xi,yi)
# eventually, lets plot the stuff...
# see http://matplotlib.org/examples/pylab_examples/griddata_demo.html
# for further details and ideas
pl.contour (xi,yi,zi,11,linewidths=1,colors='black')
pl.contourf(xi,yi,zi); pl.colorbar()
# optional
pl.gca().set_aspect('equal', 'box')
pl.show()
% python contour.py
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