def process_fname(fname_col_str): fname,col_str = fname_col_str resultsdir = '/u/paige/maye/WWW/noise' print "Preparing data..." df = prep_data(fname) print "Done." fig = plt.figure() ax = fig.add_subplot(111) print "Plotting channels." for i in range(9): series = get_channel_mean(df,col_str,i+1) series[series==-9999.0]=np.nan # attention! series[series<(series.mean()-3*series.std())]=np.nan # attention off print i,series.min() ax.plot(series,label=str(i+1)) print "Done plotting channels. Plotting csunzen." csunzen = get_channel_mean(df,'csunzen',1) csunzen[csunzen < -360]=np.nan ax.plot(csunzen,label='csunzen') ax.legend(loc='best',ncol=5, mode='expand') datasetname = splitext(split(fname)[1])[0] ax.set_title(datasetname+'_'+col_str) basename = '{0}_{1}.png'.format(datasetname,'tb') resfname = os.path.join(resultsdir,basename) print "Result filename: ",resfname plt.savefig(resfname)
def plot_csunzen(ax, df): csunzen = div.get_channel_mean(df, 'csunzen', 1) # csunzen[csunzen < -360]=np.nan ax2 = ax.twinx() ax2.plot(csunzen, label='csunzen', color='blue') ax2.axhline(y=90, color='black') for tl in ax2.get_yticklabels(): tl.set_color('blue') ax2.set_ylabel('csunzen')
def plot_csunzen(ax, df): csunzen = div.get_channel_mean(df,'csunzen',1) # csunzen[csunzen < -360]=np.nan ax2=ax.twinx() ax2.plot(csunzen,label='csunzen',color='blue') ax2.axhline(y=90,color='black') for tl in ax2.get_yticklabels(): tl.set_color('blue') ax2.set_ylabel('csunzen')
def plot_channel_means(ax, df, col_str, ch_start=1, ch_end=9): for i in range(ch_start, ch_end + 1): series = div.get_channel_mean(df, col_str, i) if debug: print(i, series.min()) ax.plot(series, label=str(i)) ax.set_ylabel('c_mean(' + col_str + ')')
def plot_channel_means(ax, df,col_str, ch_start=1, ch_end=9): for i in range(ch_start,ch_end+1): series = div.get_channel_mean(df, col_str, i) if debug: print(i,series.min()) ax.plot(series, label=str(i)) ax.set_ylabel('c_mean('+col_str+')')