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
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
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 "", line 1, in
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
--
https://mail.python.org/mailman/listinfo/python-list
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
>
>
>
> 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
>
>
>
> 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 "", line 1, in
>
> 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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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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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 "", line 1, in
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
--
https://mail.python.org/mailman/listinfo/python-list
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 "", line 1, in
>
> 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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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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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
--
https://mail.python.org/mailman/listinfo/python-list
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 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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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 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 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-x
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
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 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
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
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
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 Tuesday, August 19, 2014 10:21:48 PM UTC+1, pec...@pascolo.net wrote:
> Jamie Mitchell 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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