def read_folder():
# this function is usefull for amplitudes
file_folder = '../data/SLICED_171020141500_130420150600/amplitude/'
river_wl_fname = 'W_1_amplitude.all'
wtype = "amplitude"
wtype = None
file_folder = os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), file_folder) )
fnames = [ f for f in os.listdir(file_folder) if f.endswith(".all") ]
path = os.path.dirname(sys.argv[0])
y2 = read_waterlevel_values(os.path.abspath(os.path.join(path, file_folder, river_wl_fname) ), wtype=wtype, usecols=3)
for fname in fnames:
data_fname = os.path.abspath(os.path.join(path, file_folder, fname) )
print data_fname
y = read_waterlevel_values(data_fname, wtype=wtype, usecols=3)
fig_name = os.path.abspath(os.path.join(path, file_folder, 'distribution_LOWTIDE_'+os.path.splitext(fname)[0]+'___-2.5_2.0.jpg') )
plt_title = '{0}: Original signal LOW TIDE'.format(fname)
with sns.axes_style("whitegrid"):
plot_pandas.plot_statistical_analysis(y, data2=y2, save=True, figurename=fig_name, plot_title=plt_title, ylims=[-2.5, 2.0],
ylabel1="m AMSL", xlabel1="number of data points",
ylabel2="Normal PDF", xlabel2="m AMSL",
ylabel3="Normal CDF", xlabel3="m AMSL",
papersize='A4',
axeslabel_fontsize=18., title_fontsize=20., axesvalues_fontsize=18., annotation_fontsize=18., legend_fontsize=18.)
def read_file():
# this function is usefull for hydrographs from 1n file
file_folder = '../data/SLICED_171020141500_130420150600/hydrographs/'
file_name = 'Farge-ALL_10min.all'
wtype = "waterlevel"
usecols = [1, 2, 3, 4, 5, 6, 7]
colnames = ['GW1', 'GW2', 'GW3', 'GW4', 'GW5', 'GW6', 'W1']
wtype = "waterlevel"
path = os.path.dirname(sys.argv[0])
fname = os.path.abspath(os.path.join(path, file_folder, file_name) )
print fname
y2 = read_waterlevel_values(fname, usecols=[7], skiprows=2, delimiter=';')
for col, name in zip(usecols, colnames):
print name, col
fig_name = os.path.abspath(os.path.join(path, file_folder, 'distribution_hydrograph_'+name+'__-3._5.0.pdf') )
#y = read_waterlevel_values(fname, usecols=[col], skiprows=2, delimiter=';')
y = process2pandas.read_hydrographs_into_pandas(fname, datetime_indexes=True, log=False, delimiter=';', usecols=[0, col], skiprows=1)
#print y
#print 'mean, std:', plot_pandas.calculate_mean_std(y)
if not col == 7:
data_river = y2
pass
else:
data_river = None
data_river = None
plt_title = '{0}: Measured waterlevel'.format(name)
with sns.axes_style("whitegrid"):
plot_pandas.plot_statistical_analysis(y, data2=data_river, save=False, figurename=fig_name, plot_title=plt_title, ylims=[-3., 5.0],
ylabel1="m AMSL", xlabel1="number of data points",
ylabel2="Normal PDF", xlabel2="m AMSL",
ylabel3="Normal CDF", xlabel3="m AMSL",
papersize='A4',
axeslabel_fontsize=18., title_fontsize=20., axesvalues_fontsize=18., annotation_fontsize=18., legend_fontsize=18.)
path = os.path.dirname(sys.argv[0])
fname = os.path.abspath(os.path.join(path, file_folder, file_name) )
#-------------------------------------------------------------------
# WORKING WITH EXCEL SHEET
#-------------------------------------------------------------------
print 'reading data from XLX'
data = process2pandas.read_xlx_into_pandas(fname, sheetname='Sheet1')
#-------------------------------------------------------------------
# ACTUALLY PLOTTING
#-------------------------------------------------------------------
for well in save_fnames:
print 'plotting... >>>', well
fign = os.path.abspath(os.path.join(path, 'out', 'timelag_cyclic_statistics_'+well+'.pdf'))
with sns.axes_style("whitegrid"):
plot_pandas.plot_statistical_analysis(data[well], data2=None, save=False, figurename=fign, plot_title='Timelag calculated for each out of 344 tidal cycles: {0}'.format(well),
ylims=None,
ylabel1="Timelag [min]", xlabel1="Tidal cycles",
ylabel2="Normal PDF", xlabel2="Timelag [min]",
ylabel3="Normal CDF", xlabel3="Timelag [min]",
papersize='A4',
axeslabel_fontsize=18., title_fontsize=20., axesvalues_fontsize=18., annotation_fontsize=18., legend_fontsize=18.)
