Re: Matplotlib Contour Plots
On Tuesday, August 19, 2014 10:21:48 PM UTC+1, pec...@pascolo.net wrote:
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
This is great and works very well - thank you!!
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Re: Matplotlib Contour Plots
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? Thanks for all your help, Jamie -- https://mail.python.org/mailman/listinfo/python-list
Re: Matplotlib Contour Plots
On Friday, August 15, 2014 4:13:26 PM UTC+1, Steven D'Aprano wrote: Jamie Mitchell wrote: I created the 2D array which read as: That's not a 2D array. When the amount of data you have is too big to clearly see what it happening, replace it with something smaller. Instead of 30 items per sub-array, try it with 5 items per sub-array. Instead of eight decimal places, try it with single-digit integers. Anything to make it small enough to see clearly. When I do that with your data, instead of this: array([[[ 2.0886, 2.29400015, 2.00400019, 1.8811, 2.0480001 , 2.16800022, 2.0480001 , 1.8829, 1.9586, 2.0029, 2.02800012, 1.8124, 1.9505, 1.96200013, 1.95200014, 1.99800014, 2.0717, 1.8829, 1.9849, 2.1346, 2.1148, 1.8945, 2.0519, 2.0198, 2.03400016, 2.16600013, 2.0099, 1.86200011, 2.19800019, 2.0128]], [[ 8.515 , 8.8811, 8.5519, 7.9481, 8.6066, 8.515 , 8.8019, 8.1311, 8.6858, 8.7254, 8.4754, 8.25 , 8.4085, 8.4358, 8.3839, 8.3566, 8.6339, 8.5123, 8.3689, 8.6981, 8.5273, 8.1339, 8.3689, 8.4208, 8.5547, 8.7254, 9.0915, 8.1858, 8.7623, 8.5396]]], dtype=float32) I get this: array([[[ 2, 2, 2, 1, 2]], [[ 8, 8, 8, 7, 8]]], dtype=float32) which is much easier to work with. See the difference between that smaller example, and my earlier explanation of the difference between a 1D and 2D array? One dimensional arrays are made from a single list of numbers: [...] Two dimensional arrays are made from a list of lists: [ [...], [...] ] *Three* dimensional arrays are made from a list of lists of lists: [ [ [...], [...] ] ] *Four* dimensional arrays are made from a list of lists of lists of lists: [ [ [ [...], [...] ] ] ] and so on. You have a 3D array, with dimensions 2 x 1 x 30. You can check the dimensions by storing the array into a variable like this: py a = numpy.array([[[ 2, 2, 2, 1, 2]], [[ 8, 8, 8, 7, 8]]]) py a.shape (2, 1, 5) -- Steven Thanks for your suggestions Steven. Unfortunately I still can't make the plot I'm looking for. Do you mind if I go back to the start? Sorry I'm probably not explaining what I need very well. So I have two 1D arrays: 1st array - ([8, 8.8,8.5,7.9,8.6 ...], dtype=float32) It has a shape (150,) 2nd array - ([2, 2.2, 2.5, 2.3, ...],dtype=float32) It has a shape (150,) What I want to do is create a 2D array which merges the 1st and 2nd array so that I would have: ([[8, 8.8,8.5,7.9,8.6 ...],[2,2,2,2,5,2.3, ...]], dtype=float32) that would have a shape (150,150) In this form I could then plot a 2D contour. Thanks for your patience. Jamie -- https://mail.python.org/mailman/listinfo/python-list
Re: Matplotlib Contour Plots
I forgot to mention that when I try: a=np.array([[hs_con_sw],[te_con_sw]]) I get a 3D shape for some reason - (2,1,150) which is not what I'm after. Thanks, Jamie -- https://mail.python.org/mailman/listinfo/python-list
Re: Matplotlib Contour Plots
On Thursday, August 14, 2014 5:53:09 PM UTC+1, Steven D'Aprano wrote: Jamie Mitchell wrote: Hello all, I want to contour a scatter plot but I don't know how. Can anyone help me out? Certainly. Which way did you come in? :-) Sorry, I couldn't resist. It took me literally 20 seconds to find this by googling for matplotlib contour plot, and it only took that long because I misspelled contour the first time. http://matplotlib.org/examples/pylab_examples/contour_demo.html Does this help? If not, please explain what experience you have with matplotlib, what you have tried, what you expected it to do, and what it did instead. -- Steven Yep I've seen that thanks but I can't get it to work. I don't have much experience with matplotlib or programming in general. I just want to get a contour plot of two numpy arrays. When I call plt.contour on my data I get input must be a 2D array An example of one of my arrays: array([ 2.0886, 2.29400015, 2.00400019, 1.8811, 2.0480001 , 2.16800022, 2.0480001 , 1.8829, 1.9586, 2.0029, 2.02800012, 1.8124, 1.9505, 1.96200013, 1.95200014, 1.99800014, 2.0717, 1.8829, 1.9849, 2.1346, 2.1148, 1.8945, 2.0519, 2.0198, 2.03400016, 2.16600013, 2.0099, 1.86200011, 2.19800019, 2.0128], dtype=float32) How do I get the above array in to the right format for a contour plot? Thanks, Jamie -- https://mail.python.org/mailman/listinfo/python-list
Re: Matplotlib Contour Plots
On Friday, August 15, 2014 2:23:25 PM UTC+1, Steven D'Aprano wrote: Jamie Mitchell wrote: [...] I just want to get a contour plot of two numpy arrays. When I call plt.contour on my data I get input must be a 2D array You are providing a 1D array, or possibly a 3D array. So the question you really want to ask is not How do I do contour plots but how do I make a 2D array? An example of one of my arrays: array([ 2.0886, 2.29400015, 2.00400019, 1.8811, 2.0480001 , 2.16800022, 2.0480001 , 1.8829, 1.9586, 2.0029, 2.02800012, 1.8124, 1.9505, 1.96200013, 1.95200014, 1.99800014, 2.0717, 1.8829, 1.9849, 2.1346, 2.1148, 1.8945, 2.0519, 2.0198, 2.03400016, 2.16600013, 2.0099, 1.86200011, 2.19800019, 2.0128], dtype=float32) How do I get the above array in to the right format for a contour plot? Here's an example of making a 2D array: py import numpy py a = numpy.array([1.2, 2.5, 3.7, 4.8]) # One dimensional array py a array([ 1.2, 2.5, 3.7, 4.8]) py b = numpy.array([ [1.2, 2.5, 3.7, 4.8], ... [9.5, 8.1, 7.0, 6.2] ]) # Two dimensional array py b array([[ 1.2, 2.5, 3.7, 4.8], [ 9.5, 8.1, 7. , 6.2]]) One dimensional arrays are made from a single list of numbers: [...] Two dimensional arrays are made from a list of lists: [ [...], [...] ] -- Steven Thank you Steven. I created the 2D array which read as: array([[[ 2.0886, 2.29400015, 2.00400019, 1.8811, 2.0480001 , 2.16800022, 2.0480001 , 1.8829, 1.9586, 2.0029, 2.02800012, 1.8124, 1.9505, 1.96200013, 1.95200014, 1.99800014, 2.0717, 1.8829, 1.9849, 2.1346, 2.1148, 1.8945, 2.0519, 2.0198, 2.03400016, 2.16600013, 2.0099, 1.86200011, 2.19800019, 2.0128]], [[ 8.515 , 8.8811, 8.5519, 7.9481, 8.6066, 8.515 , 8.8019, 8.1311, 8.6858, 8.7254, 8.4754, 8.25 , 8.4085, 8.4358, 8.3839, 8.3566, 8.6339, 8.5123, 8.3689, 8.6981, 8.5273, 8.1339, 8.3689, 8.4208, 8.5547, 8.7254, 9.0915, 8.1858, 8.7623, 8.5396]]], dtype=float32) Unfortunately when I called plt.contour on this, it said again Input must be a 2D array. Is there something I have missed? Thanks, Jamie -- https://mail.python.org/mailman/listinfo/python-list
Matplotlib Contour Plots
Hello all, I want to contour a scatter plot but I don't know how. Can anyone help me out? Cheers, Jamie -- https://mail.python.org/mailman/listinfo/python-list
Contouring a 2D histogram
Hi all,
I have plotted a 2D histogram like so:
python2.7
import netCDF4
import iris
import iris.palette
import numpy as np
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from matplotlib.colors import from_levels_and_colors
fig=plt.figure()
nbins=20
nice_cmap=plt.get_cmap('brewer_RdYlBu_11')
colors=nice_cmap([5,6,7,8,9,10])
levels=[1,2,3,4,5]
cmap, norm=from_levels_and_colors(levels, colors, extend='both')
H, xedges, yedges=np.histogram2d(te_Q0_con_sw,hs_Q0_con_sw,bins=nbins)
Hmasked=np.ma.masked_where(H==0,H)
plt.pcolormesh(xedges,yedges,Hmasked,cmap=cmap,norm=norm,label='Q0 control')
# From this I get a 'scattered' 2D histogram.
Does anyone know how I can contour that scatter?
Thanks,
Jamie
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Matplotlib Colouring outline of histogram
Hi folks, Instead of colouring the entire bar of a histogram i.e. filling it, I would like to colour just the outline of the histogram. Does anyone know how to do this? Version - Python2.7 Cheers, Jamie -- https://mail.python.org/mailman/listinfo/python-list
Problem with numpy 2D Histogram
Hi folks,
I'm trying to plot a 2D histogram but I'm having some issues:
from pylab import *
import numpy as np
import netCDF4
hist,xedges,yedges=np.histogram2d(x,y,bins=10)
extent=[xedges[0],xedges[-1],yedges[0],yedges[-1]]
imshow(hist.T,extent=extent,interpolation='nearest')
colorbar()
show()
After the first line of code I get:
TypeError: Cannot cast array data from dtype('O') to dtype('float64') according
to the rule 'safe'
I'm using python2.7, x and y are type 'numpy.ndarray'
Cheers,
Jamie
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Re: Problem with numpy 2D Histogram
On Friday, June 20, 2014 10:25:44 AM UTC+1, Peter Otten wrote:
Jamie Mitchell wrote:
Hi folks,
I'm trying to plot a 2D histogram but I'm having some issues:
from pylab import *
import numpy as np
import netCDF4
hist,xedges,yedges=np.histogram2d(x,y,bins=10)
extent=[xedges[0],xedges[-1],yedges[0],yedges[-1]]
imshow(hist.T,extent=extent,interpolation='nearest')
colorbar()
show()
After the first line of code I get:
TypeError: Cannot cast array data from dtype('O') to dtype('float64')
according to the rule 'safe'
I'm using python2.7, x and y are type 'numpy.ndarray'
The error message complains about the dtype, i. e. the type of the elements
in the array, not the array itself. Make sure the elements are floating
point numbers or something compatible, not arbitrary Python objects.
As a baseline the following works
from pylab import *
import numpy as np
x, y = np.random.randn(2, 100)
print x, type(x), x.dtype
print y, type(y), y.dtype
hist, xedges, yedges = np.histogram2d(x, y, bins=10)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
imshow(hist.T, extent=extent, interpolation='nearest')
colorbar()
show()
while this doesn't:
#...
x, y = np.random.randn(2, 100)
import decimal
y = np.array([decimal.Decimal.from_float(v) for v in y])
#...
Thanks Peter.
I have changed my x and y data to float64 types but I am still getting the same
error message?
Cheers,
Jamie
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Re: Problem with numpy 2D Histogram
On Friday, June 20, 2014 12:00:15 PM UTC+1, Peter Otten wrote:
Jamie Mitchell wrote:
I have changed my x and y data to float64 types but I am still getting the
same error message?
Please double-check by adding
assert x.dtype == np.float64
assert y.dtype == np.float64
If none of these assertions fail try to make a minimal script including some
data that provokes the TypeError and post it here.
OK this is my code:
swh_Q0_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q0/swh/controlperiod/south_west/swhcontrol_swest_annavg.nc','r')
hs_Q0_con_sw=swh_Q0_con_sw.variables['hs'][:]
x=hs_Q0_con_sw.astype(float64)
# When I print the dtype of x here it says 'float64'
mwp_Q0_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q0/mean_wave_period/south_west/controlperiod/mwpcontrol_swest_annavg1D.nc','r')
te_Q0_con_sw=mwp_Q0_con_sw.variables['te'][:]
y=te_Q0_con_sw.astype(float64)
If I try assert x.dtype == np.float64 I get:
AssertionError
hist,xedges,yedges=np.histogram2d(x,y,bins=10)
TypeError: Cannot cast array data from dtype('O') to dtype('float64') according
to the rule 'safe'
Thanks,
Jamie
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Re: Problem with numpy 2D Histogram
On Friday, June 20, 2014 9:46:29 AM UTC+1, Jamie Mitchell wrote:
Hi folks,
I'm trying to plot a 2D histogram but I'm having some issues:
from pylab import *
import numpy as np
import netCDF4
hist,xedges,yedges=np.histogram2d(x,y,bins=10)
extent=[xedges[0],xedges[-1],yedges[0],yedges[-1]]
imshow(hist.T,extent=extent,interpolation='nearest')
colorbar()
show()
After the first line of code I get:
TypeError: Cannot cast array data from dtype('O') to dtype('float64')
according to the rule 'safe'
I'm using python2.7, x and y are type 'numpy.ndarray'
Cheers,
Jamie
Thanks for your help Peter.
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Re: Matplotlib Colouring outline of histogram
On Friday, June 20, 2014 2:47:03 PM UTC+1, Jason Swails wrote:
On Fri, Jun 20, 2014 at 4:10 AM, Jamie Mitchell jamiemit...@gmail.com wrote:
Hi folks,
Instead of colouring the entire bar of a histogram i.e. filling it, I would
like to colour just the outline of the histogram. Does anyone know how to do
this?
Version - Python2.7
Look at the matplotlib.pyplot.hist function documentation:
http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist
In addition to the listed parameters, you'll see the Other Parameters taken
are those that can be applied to the created Patch objects (which are the
actual rectangles). For the Patch keywords, see the API documentation on the
Patch object
(http://matplotlib.org/api/artist_api.html#matplotlib.patches.Patch). So you
can do one of two things:
1) Pass the necessary Patch keywords to effect what you want
e.g. (untested):
import matplotlib.pyplot as plt
plt.hist(dataset, bins=10, range=(-5, 5), normed=True,
edgecolor='b', linewidth=2, facecolor='none', # Patch options
)
plt.show()
2) Iterate over the Patch instances returned by plt.hist() and set the
properties you want.
e.g. (untested):
import matplotlib.pyplot as plt
n, bins, patches = plt.hist(dataset, bins=10, range=(-5, 5), normed=True)
for patch in patches:
patch.set_edgecolor('b') # color of the lines around each bin
patch.set_linewidth(2) # Set width of bin edge
patch.set_facecolor('none') # set no fill
# Anything else you want to do
plt.show()
Approach (1) is the easy way, and is there to satisfy the majority of use
cases. However, approach (2) is _much_ more flexible. Suppose you wanted to
highlight a particular region of your data with a specific facecolor or
edgecolor -- you can apply the features you want to individual patches using
approach (2). Or if you wanted to highlight a specific bin with thicker
lines.
This is a common theme in matplotlib -- you can use keywords to apply the
same features to every part of a plot or you can iterate over the drawn
objects and customize them individually. This is a large part of what makes
matplotlib nice to me -- it has a simple mode as well as a predictable API
for customizing a plot in almost any way you could possibly want.
HTH,
Jason
--
Jason M. Swails
BioMaPS,
Rutgers University
Postdoctoral Researcher
That's great Jason thanks for the detailed response, I went with the easier
option 1!
I am also trying to put hatches on my histograms like so:
plt.hist(dataset,bins=10,hatch=['*'])
When it comes to plt.show() I get the following error message:
File
/usr/local/sci/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-linux-x86_64.egg/matplotlib/backends/backend_gtk.py,
line 435, in expose_event
self._render_figure(self._pixmap, w, h)
File
/usr/local/sci/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-linux-x86_64.egg/matplotlib/backends/backend_gtkagg.py,
line 84, in _render_figure
FigureCanvasAgg.draw(self)
File
/usr/local/sci/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-linux-x86_64.egg/matplotlib/backends/backend_agg.py,
line 451, in draw
self.figure.draw(self.renderer)
File
/usr/local/sci/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-linux-x86_64.egg/matplotlib/artist.py,
line 55, in draw_wrapper
draw(artist, renderer, *args, **kwargs)
File
/usr/local/sci/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-linux-x86_64.egg/matplotlib/figure.py,
line 1034, in draw
func(*args)
File
/usr/local/sci/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-linux-x86_64.egg/matplotlib/artist.py,
line 55, in draw_wrapper
draw(artist, renderer, *args, **kwargs)
File
/usr/local/sci/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-linux-x86_64.egg/matplotlib/axes.py,
line 2086, in draw
a.draw(renderer)
File
/usr/local/sci/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-linux-x86_64.egg/matplotlib/artist.py,
line 55, in draw_wrapper
draw(artist, renderer, *args, **kwargs)
File
/usr/local/sci/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-linux-x86_64.egg/matplotlib/patches.py,
line 429, in draw
renderer.draw_path(gc, tpath, affine, rgbFace)
File
/usr/local/sci/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-linux-x86_64.egg/matplotlib/backends/backend_agg.py,
line 145, in draw_path
self._renderer.draw_path(gc, path, transform, rgbFace)
File
/usr/local/sci/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-linux-x86_64.egg/matplotlib/backend_bases.py,
line 1010, in get_hatch_path
return Path.hatch(self._hatch, density)
File
/usr/local/sci/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-linux-x86_64.egg/matplotlib/path.py,
line 888, in hatch
hatch_path = cls._hatch_dict.get((hatchpattern, density))
TypeError: unhashable type: 'list'
Traceback (most recent call
Re: Matplotlib Colouring outline of histogram
On Friday, June 20, 2014 9:10:58 AM UTC+1, Jamie Mitchell wrote: Hi folks, Instead of colouring the entire bar of a histogram i.e. filling it, I would like to colour just the outline of the histogram. Does anyone know how to do this? Version - Python2.7 Cheers, Jamie Great thanks again Jason. -- https://mail.python.org/mailman/listinfo/python-list
Re: Matplotlib - specifying bin widths
On Thursday, June 5, 2014 4:54:16 PM UTC+1, Jamie Mitchell wrote: Hello all! Instead of setting the number of bins I want to set the bin width. I would like my bins to go from 1.7 to 2.4 in steps of 0.05. How do I say this in the code? Cheers, Jamie That's great thanks Mark. -- https://mail.python.org/mailman/listinfo/python-list
Overlaying a boxplot onto a time series figure
Hi there,
I would like to overlay some boxplots onto a time series.
I have tried pylab.hold(True) in between the two plots in my code but this
hasn't worked.
The problem is that the x-axes of the boxplots and the time series are not the
same.
Code for time series:
python2.7
import netCDF4
import matplotlib.pyplot as plt
import numpy as np
swh_Q0_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q0/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q0_con_sw=swh_Q0_con_sw.variables['hs'][:]
year_con=swh_Q0_con_sw.variables['year'][:]
swh_Q3_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q3/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q3_con_sw=swh_Q3_con_sw.variables['hs'][:]
swh_Q4_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q4/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q4_con_sw=swh_Q4_con_sw.variables['hs'][:]
swh_Q14_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q14/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q14_con_sw=swh_Q14_con_sw.variables['hs'][:]
swh_Q16_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q16/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q16_con_sw=swh_Q16_con_sw.variables['hs'][:]
swh_Q0_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q0/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q0_fut_sw=swh_Q0_fut_sw.variables['hs'][:]
year_fut=swh_Q0_fut_sw.variables['year'][:]
swh_Q3_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q3/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q3_fut_sw=swh_Q3_fut_sw.variables['hs'][:]
swh_Q4_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q4/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q4_fut_sw=swh_Q4_fut_sw.variables['hs'][:]
swh_Q14_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q14/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q14_fut_sw=swh_Q14_fut_sw.variables['hs'][:]
swh_Q16_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q16/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q16_fut_sw=swh_Q16_fut_sw.variables['hs'][:]
fit_Q0_con_sw=np.polyfit(year_con,hs_Q0_con_sw,1)
fit_fn_Q0_con_sw=np.poly1d(fit_Q0_con_sw)
plt.plot(year_con,hs_Q0_con_sw,'g.')
plt.plot(year_con,fit_fn_Q0_con_sw(year_con),'g',label='Q0 no pert')
fit_Q3_con_sw=np.polyfit(year_con,hs_Q3_con_sw,1)
fit_fn_Q3_con_sw=np.poly1d(fit_Q3_con_sw)
plt.plot(year_con,hs_Q3_con_sw,'b.')
plt.plot(year_con,fit_fn_Q3_con_sw(year_con),'b',label='Q3 low sens')
fit_Q4_con_sw=np.polyfit(year_con,hs_Q4_con_sw,1)
fit_fn_Q4_con_sw=np.poly1d(fit_Q4_con_sw)
plt.plot(year_con,hs_Q4_con_sw,'y.')
plt.plot(year_con,fit_fn_Q4_con_sw(year_con),'y',label='Q4 low sens')
fit_Q14_con_sw=np.polyfit(year_con,hs_Q14_con_sw,1)
fit_fn_Q14_con_sw=np.poly1d(fit_Q14_con_sw)
plt.plot(year_con,hs_Q14_con_sw,'r.')
plt.plot(year_con,fit_fn_Q14_con_sw(year_con),'r',label='Q14 high sens')
fit_Q16_con_sw=np.polyfit(year_con,hs_Q16_con_sw,1)
fit_fn_Q16_con_sw=np.poly1d(fit_Q16_con_sw)
plt.plot(year_con,hs_Q16_con_sw,'c.')
plt.plot(year_con,fit_fn_Q16_con_sw(year_con),'c',label='Q16 high sens')
fit_Q0_fut_sw=np.polyfit(year_fut,hs_Q0_fut_sw,1)
fit_fn_Q0_fut_sw=np.poly1d(fit_Q0_fut_sw)
plt.plot(year_fut,hs_Q0_fut_sw,'g.')
plt.plot(year_fut,fit_fn_Q0_fut_sw(year_fut),'g')
fit_Q3_fut_sw=np.polyfit(year_fut,hs_Q3_fut_sw,1)
fit_fn_Q3_fut_sw=np.poly1d(fit_Q3_fut_sw)
plt.plot(year_fut,hs_Q3_fut_sw,'b.')
plt.plot(year_fut,fit_fn_Q3_fut_sw(year_fut),'b')
fit_Q4_fut_sw=np.polyfit(year_fut,hs_Q4_fut_sw,1)
fit_fn_Q4_fut_sw=np.poly1d(fit_Q4_fut_sw)
plt.plot(year_fut,hs_Q4_fut_sw,'y.')
plt.plot(year_fut,fit_fn_Q4_fut_sw(year_fut),'y')
fit_Q14_fut_sw=np.polyfit(year_fut,hs_Q14_fut_sw,1)
fit_fn_Q14_fut_sw=np.poly1d(fit_Q14_fut_sw)
plt.plot(year_fut,hs_Q14_fut_sw,'r.')
plt.plot(year_fut,fit_fn_Q14_fut_sw(year_fut),'y')
fit_Q16_fut_sw=np.polyfit(year_fut,hs_Q16_fut_sw,1)
fit_fn_Q16_fut_sw=np.poly1d(fit_Q16_fut_sw)
plt.plot(year_fut,hs_Q16_fut_sw,'c.')
plt.plot(year_fut,fit_fn_Q16_fut_sw(year_fut),'c')
plt.legend(loc='best')
plt.xlabel('Year')
plt.ylabel('Significant Wave Height annual averages SW England')
plt.title('Time series of Significant Wave Height')
plt.show()
Code for boxplots:
python2.7
from pylab import *
import netCDF4
data=(hs_Q0_con_sw,hs_Q3_con_sw,hs_Q4_con_sw,hs_Q14_con_sw,hs_Q16_con_sw)
figure(1)
boxplot(data)
labels=('QO no pert','Q3 low sens','Q4 low sens','Q14 high sens','Q16 high
sens')
xticks(range(1,6),labels,rotation=15)
xlabel('Ensemble Member')
ylabel('Significant Wave Height Annual Average')
title('Significant Wave Height SW England 1981-2010')
show()
If anybody knows how I could integrate these two plots I would be eternally
grateful!
Thanks,
Jamie
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Matplotlib - specifying bin widths
Hello all! Instead of setting the number of bins I want to set the bin width. I would like my bins to go from 1.7 to 2.4 in steps of 0.05. How do I say this in the code? Cheers, Jamie -- https://mail.python.org/mailman/listinfo/python-list
Re: Adding R squared value to scatter plot
On Wednesday, May 21, 2014 1:30:16 PM UTC+1, Jason Swails wrote:
On Wed, May 21, 2014 at 7:59 AM, Jamie Mitchell jamiemit...@gmail.com wrote:
I have made a plot using the following code:
python2.7
import netCDF4
import matplotlib.pyplot as plt
import numpy as np
swh_Q0_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q0/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q0_con_sw=swh_Q0_con_sw.variables['hs'][:]
swh_Q3_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q3/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q3_con_sw=swh_Q3_con_sw.variables['hs'][:]
swh_Q4_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q4/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q4_con_sw=swh_Q4_con_sw.variables['hs'][:]
swh_Q14_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q14/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q14_con_sw=swh_Q14_con_sw.variables['hs'][:]
swh_Q16_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q16/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q16_con_sw=swh_Q16_con_sw.variables['hs'][:]
swh_Q0_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q0/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q0_fut_sw=swh_Q0_fut_sw.variables['hs'][:]
swh_Q3_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q3/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q3_fut_sw=swh_Q3_fut_sw.variables['hs'][:]
swh_Q4_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q4/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q4_fut_sw=swh_Q4_fut_sw.variables['hs'][:]
swh_Q14_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q14/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q14_fut_sw=swh_Q14_fut_sw.variables['hs'][:]
swh_Q16_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q16/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q16_fut_sw=swh_Q16_fut_sw.variables['hs'][:]
fit_Q0_sw=np.polyfit(hs_Q0_con_sw,hs_Q0_fut_sw,1)
fit_fn_Q0_sw=np.poly1d(fit_Q0_sw)
plt.plot(hs_Q0_con_sw,hs_Q0_fut_sw,'g.')
plt.plot(hs_Q0_con_sw,fit_fn_Q0_sw(hs_Q0_con_sw),'g',label='Q0 no pert')
fit_Q3_sw=np.polyfit(hs_Q3_con_sw,hs_Q3_fut_sw,1)
fit_fn_Q3_sw=np.poly1d(fit_Q3_sw)
plt.plot(hs_Q3_con_sw,hs_Q3_fut_sw,'b.')
plt.plot(hs_Q3_con_sw,fit_fn_Q3_sw(hs_Q3_con_sw),'b',label='Q3 low sens')
fit_Q4_sw=np.polyfit(hs_Q4_con_sw,hs_Q4_fut_sw,1)
fit_fn_Q4_sw=np.poly1d(fit_Q4_sw)
plt.plot(hs_Q4_con_sw,hs_Q4_fut_sw,'y.')
plt.plot(hs_Q4_con_sw,fit_fn_Q4_sw(hs_Q4_con_sw),'y',label='Q4 low sens')
fit_Q14_sw=np.polyfit(hs_Q14_con_sw,hs_Q14_fut_sw,1)
fit_fn_Q14_sw=np.poly1d(fit_Q14_sw)
plt.plot(hs_Q14_con_sw,hs_Q14_fut_sw,'r.')
plt.plot(hs_Q14_con_sw,fit_fn_Q14_sw(hs_Q14_con_sw),'r',label='Q14 high sens')
fit_Q16_sw=np.polyfit(hs_Q16_con_sw,hs_Q16_fut_sw,1)
fit_fn_Q16_sw=np.poly1d(fit_Q16_sw)
plt.plot(hs_Q16_con_sw,hs_Q16_fut_sw,'c.')
plt.plot(hs_Q16_con_sw,fit_fn_Q16_sw(hs_Q16_con_sw),'c',label='Q16 high sens')
plt.legend(loc='best')
plt.xlabel('Significant Wave Height annual averages NW Scotland 1981-2010')
plt.ylabel('Significant Wave Height annual averages NW Scotland 2040-2069')
plt.title('Scatter plot of Significant Wave Height')
plt.show()
--
What I would like to do is display the R squared value next to the line of
best fits that I have made.
Does anyone know how to do this with matplotlib?
You can add plain text or annotations with arrows using any of the API
functions described here: http://matplotlib.org/1.3.1/users/text_intro.html
(information specifically regarding the text call is here:
http://matplotlib.org/1.3.1/api/pyplot_api.html#matplotlib.pyplot.text)
You can also use LaTeX typesetting here, so you can make the text something
like r'$R^2$' to display R^2 with nice typesetting. (I typically use raw
strings for matplotlib text strings with LaTeX formulas in them since LaTeX
makes extensive use of the \ character.)
The onus is on you, the programmer, to determine _where_ on the plot you want
the text to appear. Since you know what you are plotting, you can write a
quick helper function that will compute the optimal (to you) location for the
label to occur based on where things are drawn on the canvas. There is a
_lot_ of flexibility here so you should be able to get your text looking
exactly how (and where) you want it.
Hope this helps,
Jason
--
Jason M. Swails
BioMaPS,
Rutgers University
Postdoctoral Researcher
Hi Jason,
Thank you for your swift response - you solved my problem!
Sorry I took a while to get back to you.
Thanks again,
Jamie
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Adding R squared value to scatter plot
I have made a plot using the following code:
python2.7
import netCDF4
import matplotlib.pyplot as plt
import numpy as np
swh_Q0_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q0/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q0_con_sw=swh_Q0_con_sw.variables['hs'][:]
swh_Q3_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q3/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q3_con_sw=swh_Q3_con_sw.variables['hs'][:]
swh_Q4_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q4/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q4_con_sw=swh_Q4_con_sw.variables['hs'][:]
swh_Q14_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q14/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q14_con_sw=swh_Q14_con_sw.variables['hs'][:]
swh_Q16_con_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q16/swh/controlperiod/south_west/swhcontrol_swest_annavg1D.nc','r')
hs_Q16_con_sw=swh_Q16_con_sw.variables['hs'][:]
swh_Q0_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q0/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q0_fut_sw=swh_Q0_fut_sw.variables['hs'][:]
swh_Q3_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q3/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q3_fut_sw=swh_Q3_fut_sw.variables['hs'][:]
swh_Q4_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q4/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q4_fut_sw=swh_Q4_fut_sw.variables['hs'][:]
swh_Q14_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q14/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q14_fut_sw=swh_Q14_fut_sw.variables['hs'][:]
swh_Q16_fut_sw=netCDF4.Dataset('/data/cr1/jmitchel/Q16/swh/2050s/south_west/swh2050s_swest_annavg1D.nc','r')
hs_Q16_fut_sw=swh_Q16_fut_sw.variables['hs'][:]
fit_Q0_sw=np.polyfit(hs_Q0_con_sw,hs_Q0_fut_sw,1)
fit_fn_Q0_sw=np.poly1d(fit_Q0_sw)
plt.plot(hs_Q0_con_sw,hs_Q0_fut_sw,'g.')
plt.plot(hs_Q0_con_sw,fit_fn_Q0_sw(hs_Q0_con_sw),'g',label='Q0 no pert')
fit_Q3_sw=np.polyfit(hs_Q3_con_sw,hs_Q3_fut_sw,1)
fit_fn_Q3_sw=np.poly1d(fit_Q3_sw)
plt.plot(hs_Q3_con_sw,hs_Q3_fut_sw,'b.')
plt.plot(hs_Q3_con_sw,fit_fn_Q3_sw(hs_Q3_con_sw),'b',label='Q3 low sens')
fit_Q4_sw=np.polyfit(hs_Q4_con_sw,hs_Q4_fut_sw,1)
fit_fn_Q4_sw=np.poly1d(fit_Q4_sw)
plt.plot(hs_Q4_con_sw,hs_Q4_fut_sw,'y.')
plt.plot(hs_Q4_con_sw,fit_fn_Q4_sw(hs_Q4_con_sw),'y',label='Q4 low sens')
fit_Q14_sw=np.polyfit(hs_Q14_con_sw,hs_Q14_fut_sw,1)
fit_fn_Q14_sw=np.poly1d(fit_Q14_sw)
plt.plot(hs_Q14_con_sw,hs_Q14_fut_sw,'r.')
plt.plot(hs_Q14_con_sw,fit_fn_Q14_sw(hs_Q14_con_sw),'r',label='Q14 high sens')
fit_Q16_sw=np.polyfit(hs_Q16_con_sw,hs_Q16_fut_sw,1)
fit_fn_Q16_sw=np.poly1d(fit_Q16_sw)
plt.plot(hs_Q16_con_sw,hs_Q16_fut_sw,'c.')
plt.plot(hs_Q16_con_sw,fit_fn_Q16_sw(hs_Q16_con_sw),'c',label='Q16 high sens')
plt.legend(loc='best')
plt.xlabel('Significant Wave Height annual averages NW Scotland 1981-2010')
plt.ylabel('Significant Wave Height annual averages NW Scotland 2040-2069')
plt.title('Scatter plot of Significant Wave Height')
plt.show()
--
What I would like to do is display the R squared value next to the line of best
fits that I have made.
Does anyone know how to do this with matplotlib?
Thanks,
Jamie
--
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Saving a file as netCDF4 in Python
Dear all,
Apologies as this sounds like a very simple question but I can't find an answer
anywhere.
I have loaded a netCDF4 file into python as follows:
swh=netCDF4.Dataset('path/to/netCDFfile,'r')
I then isolate the variables I wish to plot:
hs=swh.variables['hs']
year=swh.variables['year']
I would then like to save these hs and year variables so that I don't have to
isolate them every time I want to plot them.
Any help would be much appreciated.
Cheers,
Jamie
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len() of unsized object - ks test
Hello all,
I am trying to perform a Kolmogorov-Smirnov test in Python but I'm having a few
difficulties.
# My files are netCDF so I import them as follows:
control=netCDF4.Dataset('/data/cr1/jmitchel/Q0/swh/controlperiod/south_west/swhcontrol_swest_concatannavg_1D.nc','r')
# The string is simply a 1D array
# Then performing the ks test:
kstest(control,'norm')
# I then get the following error:
File stdin, line 1, in module
File /usr/local/sci/lib/python2.7/site-packages/scipy/stats/stats.py, line
3413, in kstest
N = len(vals)
TypeError: len() of unsized object
Any ideas on why this isn't working would be great.
Thanks,
Jamie
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Re: len() of unsized object - ks test
On Friday, April 25, 2014 3:07:54 PM UTC+1, Jamie Mitchell wrote:
Hello all,
I am trying to perform a Kolmogorov-Smirnov test in Python but I'm having a
few difficulties.
# My files are netCDF so I import them as follows:
control=netCDF4.Dataset('/data/cr1/jmitchel/Q0/swh/controlperiod/south_west/swhcontrol_swest_concatannavg_1D.nc','r')
# The string is simply a 1D array
# Then performing the ks test:
kstest(control,'norm')
# I then get the following error:
File stdin, line 1, in module
File /usr/local/sci/lib/python2.7/site-packages/scipy/stats/stats.py,
line 3413, in kstest
N = len(vals)
TypeError: len() of unsized object
Any ideas on why this isn't working would be great.
Thanks,
Jamie
Thanks for your help.
Steven your right I wasn't reading in the file on netCDF4.Dataset, I was just
opening it. I have rectified it now - a silly mistake!
Thanks again,
Jamie
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Line of best fit
I am new to python so apologies for the ignorance with this question.
How would I apply a line of best fit to a plot?
My data are netCDF4 data files and this is essentially what I have done so far:
swh1=netCDF4.Dataset('filename','r')
hs1=swh1.variables['hs']
swh2=netCDF4.Dataset('filename'.'r')
hs2=swh2.variables['hs']
plt.plot(hs1,hs2,'.')
Cheers,
Jamie
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Memory error
Hello all,
I'm afraid I am new to all this so bear with me...
I am looking to find the statistical significance between two large netCDF data
sets.
Firstly I've loaded the two files into python:
swh=netCDF4.Dataset('/data/cr1/jmitchel/Q0/swh/controlperiod/averages/swh_control_concat.nc',
'r')
swh_2050s=netCDF4.Dataset('/data/cr1/jmitchel/Q0/swh/2050s/averages/swh_2050s_concat.nc',
'r')
I have then isolated the variables I want to perform the pearson correlation on:
hs=swh.variables['hs']
hs_2050s=swh_2050s.variables['hs']
Here is the metadata for those files:
print hs
type 'netCDF4.Variable'
int16 hs(time, latitude, longitude)
standard_name: significant_height_of_wind_and_swell_waves
long_name: significant_wave_height
units: m
add_offset: 0.0
scale_factor: 0.002
_FillValue: -32767
missing_value: -32767
unlimited dimensions: time
current shape = (86400, 350, 227)
print hs_2050s
type 'netCDF4.Variable'
int16 hs(time, latitude, longitude)
standard_name: significant_height_of_wind_and_swell_waves
long_name: significant_wave_height
units: m
add_offset: 0.0
scale_factor: 0.002
_FillValue: -32767
missing_value: -32767
unlimited dimensions: time
current shape = (86400, 350, 227)
Then to perform the pearsons correlation:
from scipy.stats.stats import pearsonr
pearsonr(hs,hs_2050s)
I then get a memory error:
Traceback (most recent call last):
File stdin, line 1, in module
File /usr/local/sci/lib/python2.7/site-packages/scipy/stats/stats.py, line
2409, in pearsonr
x = np.asarray(x)
File /usr/local/sci/lib/python2.7/site-packages/numpy/core/numeric.py, line
321, in asarray
return array(a, dtype, copy=False, order=order)
MemoryError
This also happens when I try to create numpy arrays from the data.
Does anyone know how I can alleviate theses memory errors?
Cheers,
Jamie
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Re: Memory error
On Monday, March 24, 2014 11:32:31 AM UTC, Jamie Mitchell wrote:
Hello all,
I'm afraid I am new to all this so bear with me...
I am looking to find the statistical significance between two large netCDF
data sets.
Firstly I've loaded the two files into python:
swh=netCDF4.Dataset('/data/cr1/jmitchel/Q0/swh/controlperiod/averages/swh_control_concat.nc',
'r')
swh_2050s=netCDF4.Dataset('/data/cr1/jmitchel/Q0/swh/2050s/averages/swh_2050s_concat.nc',
'r')
I have then isolated the variables I want to perform the pearson correlation
on:
hs=swh.variables['hs']
hs_2050s=swh_2050s.variables['hs']
Here is the metadata for those files:
print hs
type 'netCDF4.Variable'
int16 hs(time, latitude, longitude)
standard_name: significant_height_of_wind_and_swell_waves
long_name: significant_wave_height
units: m
add_offset: 0.0
scale_factor: 0.002
_FillValue: -32767
missing_value: -32767
unlimited dimensions: time
current shape = (86400, 350, 227)
print hs_2050s
type 'netCDF4.Variable'
int16 hs(time, latitude, longitude)
standard_name: significant_height_of_wind_and_swell_waves
long_name: significant_wave_height
units: m
add_offset: 0.0
scale_factor: 0.002
_FillValue: -32767
missing_value: -32767
unlimited dimensions: time
current shape = (86400, 350, 227)
Then to perform the pearsons correlation:
from scipy.stats.stats import pearsonr
pearsonr(hs,hs_2050s)
I then get a memory error:
Traceback (most recent call last):
File stdin, line 1, in module
File /usr/local/sci/lib/python2.7/site-packages/scipy/stats/stats.py,
line 2409, in pearsonr
x = np.asarray(x)
File /usr/local/sci/lib/python2.7/site-packages/numpy/core/numeric.py,
line 321, in asarray
return array(a, dtype, copy=False, order=order)
MemoryError
This also happens when I try to create numpy arrays from the data.
Does anyone know how I can alleviate theses memory errors?
Cheers,
Jamie
Just realised that obviously pearson correlation requires two 1D arrays and
mine are 3D, silly mistake!
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