<jupyter_start><jupyter_text>load trimmed data, concatenate into a single dataframe<jupyter_code>datapath = '../../data/trimdat/'
fPaths = os.listdir(datapath)

for ifile, file in enumerate(fPaths):
    loadfile = os.path.join(datapath,file)
    
    temp = pd.read_csv(loadfile)
    
    if ifile==0:
        metdat = temp
    else:
        metdat = metdat.append(temp)

# Reindex with date
metdat.set_index('Date', inplace=True)
catinfo = MET.get_catinfo(metdat)
test = eventsdf.groupby([eventsdf.index.month, eventsdf.index.hour]).mean()
temp = test['EOG'].unstack().transpose()
colors = utils.get_colors(12)
colors = colors[6:]+colors[0:5]
temp.plot(colors=colors)
monthnames = utils.monthnames()
eventsdf['ECD'].plot()<jupyter_output><empty_output><jupyter_text>get metdat that corresponds to times of a particular event<jupyter_code>ECD = metdat.merge(eventsdf[eventsdf['ECD'] > 1],right_index=True, left_index=True)

fig,ax = vis.hist(metdat, catinfo, 'ti', vertloc=100)
dat = metdat['Cup Equivalent Ti (sonic_100m)'].dropna()
ax.hist(dat, 35, color = 'r', alpha=0.5,weights = np.ones(len(dat))/len(dat))
catinfo['columns']['ti']<jupyter_output><empty_output>
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