Re: [Matplotlib-users] Warning: converting a masked element to nan

[Benjamin R. <ben...@ou...>]

I have seen situations when using datetimes for the x axis where if all the
data being viewed is masked (or nans, or whatever) then it errors out
because the axis range can't be determined.  Maybe that is what is
happening here?

Ben Root

On Wed, Nov 2, 2011 at 11:48 AM, Michael Droettboom <md...@st...> wrote:

>  matplotlib should handle both masked arrays and arrays with NaNs and
> treat both the same.  Can you reduce the script to something that can be
> run independently without data?  It's not clear to me yet why this is
> failing.
>
> Mike
>
>
> On 11/01/2011 05:24 PM, questions anon wrote:
>
> Hi All,
> I am trying to plot time against mean daily temperature values. The
> problem is temperature contains no data in a few areas. I want to be able
> to ignore this and continue plotting.
> When I run the below script on my data that has all normal numbers it
> works fine but when I run the script on my data that has sections of 'no
> data' I receive the below error and the graph will not plot the trendline.
> Any suggestions on how I could fix this.
> Thanks
>
> *Warning (from warnings module):
>   File "C:\Python27\lib\site-packages\numpy\ma\core.py", line 3785
>     warnings.warn("Warning: converting a masked element to nan.")
> UserWarning: Warning: converting a masked element to nan.*
>
>
> from netCDF4 import Dataset
> import matplotlib.pyplot as plt
> import numpy as N
> from mpl_toolkits.basemap import Basemap
> from netcdftime import utime
> from datetime import datetime
> import os
> from numpy import *
> import matplotlib.dates as mdates
> from numpy import ma as MA
>
> TSFCmeanall=[]
> timeall=[]
> time_intall=[]
>
> MainFolder=r"E:/GriddedData/T_SFC/1987/"
> for (path, dirs, files) in os.walk(MainFolder):
>                         for dir in dirs:
>                                 print dir
>                         path=path+'/'
>
>                         for ncfile in files:
>                                 if ncfile[-3:]=='.nc':
>                                     ncfile=os.path.join(path,ncfile)
>                                     ncfile=Dataset(ncfile, 'r+', 'NETCDF4')
>                                     TSFC=ncfile.variables['T_SFC'][0:20]
>                                     TIME=ncfile.variables['time'][0:20]
>
> fillvalue=ncfile.variables['T_SFC']._FillValue
>                                     TSFC=MA.masked_values(TSFC, fillvalue)
>                                     ncfile.close()
>
>                                     for TSFC, TIME in
> zip((TSFC[:]),(TIME[:])):
>                                         cdftime=utime('seconds since
> 1970-01-01 00:00:00')
>                                         ncfiletime=cdftime.num2date(TIME)
>                                         timestr=str(ncfiletime)
>                                         d = datetime.strptime(timestr,
> '%Y-%m-%d %H:%M:%S')
>                                         date_string =
> d.strftime('%Y%m%d%H')
>                                         time_int=int(date_string)
>
>                                         TSFCmean=N.mean(TSFC)
>
>                                         TSFCmeanall.append(TSFCmean)
>                                         timeall.append(ncfiletime)
>                                         time_intall.append(time_int)
>
> x=timeall
> y=TSFCmeanall
> x2=time_intall
>
> fig, ax=plt.subplots(1)
>
> z=N.polyfit(x2,y,1)
> p=N.poly1d(z)
>
> plt.plot(x,y)
> plt.plot(x,p(x2),'r--') #add trendline to plot
>
> fig.autofmt_xdate()
> ax.fmt_xdata=mdates.DateFormatter('%Y-%m-%d %H:%M:%S')
> plt.ylabel("Temperature C")
> plt.title("Mean Daily Temp")
> plt.show()
>
>
>
>
>
>
>
>
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