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+')')
