royalosyin / python-practical-application-on-climate-variability-studies Goto Github PK

Hello, I'm a college student and saw your bivariate choropleth map,
Very cool chart, can you refer to the source code of the drawing? Because when I am drawing, involving numpy.nan values, the color of the plot is always handled poorly? Hope you can give some advice, thanks again

Hi,
I am a beginner in learning Python ,and there is something wrong when i testing ex01.
the error is taking place at "np.savez('skt.so.mon.mean.npz',skt_so=skt_so,lat_so=lat_so,lon_so=lon_so)",
the information about error :
Traceback (most recent call last):
File "./readNC.py", line 28, in
np.savez('skt.so.mon.mean.npz',skt_so=skt_so,lat_so=lat_so,lon_so=lon_so)
File "/software/anaconda3/lib/python3.6/site-packages/numpy/lib/npyio.py", line 593, in savez
_savez(file, args, kwds, False)
File "/software/anaconda3/lib/python3.6/site-packages/numpy/lib/npyio.py", line 703, in _savez
pickle_kwargs=pickle_kwargs)
File "/software/anaconda3/lib/python3.6/site-packages/numpy/lib/format.py", line 587, in write_array
array.tofile(fp)
File "/software/anaconda3/lib/python3.6/site-packages/numpy/ma/core.py", line 5934, in tofile
raise NotImplementedError("MaskedArray.tofile() not implemented yet.")
NotImplementedError: MaskedArray.tofile() not implemented yet.

Can you help me to solve this problem? Look forward for your reply.

In'Continuous Wavelet Spectrum - Example for NINO3 SST ',Here lag1 = 0.72,both α1 and α2 >0. TC98 says explicitly that "this red-noise was estimated from( α1+√α2) /2 where α1and α2 are the lag-1 and lag-2 autocorrelations of the Niño3 SST". So this is NOT a constant value, valid for each and every time series, but one which must be estimated.
when α1<0,especially α2<0,how to compute it? Look forward for your reply.

Hi, I am a beginner in learning Python. In 'Power Spectral Density’.In my opinion,for monthly data, the results are divided by 12 to calculate annual cycles like this 'plt.plot(1.0/f1[1:47]/12, pxx1[1:47], label='welch')',but for annual data,how to deal with it？I tried not to divide by 12，but
the maximum period of abscissa is 250 years，this may be something wrong .Could you help me to solve this problem? Look forward for your reply.

Hello,

I was checking your notebook (very good btw, thank you). And I was wondering how you would go about obtaining the trend uncertainty for global values.

Thank you,
Carolina

Hi, I am working with the SPI code. The code is running fine until it computes the dim_spi_n. I am getting the following error

When it read the probability values in lmoments.quannor and goes to the function quanor(F,para), here it is getting the F values which are not iterable. Any suggestions why is it happening. I am working with the sample netcdf file as used in your code.

Thank you for all of your contributions. Your notebooks were really helpful for climate analysis. I was not able to understand how you came up with reshaping dataset at "421". will it be possible for you to provide more context? Thank you

ncset= netcdf(r'data/sst.mnmean.nc')
lons = ncset['lon'][:]
lats = ncset['lat'][:]
sst = ncset['sst'][1:421,:,:] # 1982-2016 to make it divisible by 12
nctime = ncset['time'][1:421]
t_unit = ncset['time'].units

try :
t_cal =ncset['time'].calendar
except AttributeError : # Attribute doesn't exist
t_cal = u"gregorian" # or standard

nt, nlat, nlon = sst.shape
ngrd = nlon*nlat