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.)
figure_name = 'out/figure.pdf'
ampl_fnames = ['GW_1_amplitude.all', 'GW_2_amplitude.all', 'GW_3_amplitude.all', 'GW_4_amplitude.all', 'GW_5_amplitude.all', 'GW_6_amplitude.all', 'W_1_amplitude.all']
# ---------------------------------------
# END user inputs END
# ---------------------------------------
path = os.path.dirname(sys.argv[0])
fname = os.path.abspath(os.path.join(path, file_folder, file_name) )
fign = os.path.abspath(os.path.join(path, figure_name))
# read extremum-data for all wells into pd.dataframe and save it to list...
AMPLITUDES = list()
for ampl_fn in ampl_fnames:
a_fn = os.path.abspath(os.path.join(path, amplitudes_folder, ampl_fn))
ht, lt, amp = process2pandas.read_amplitudes_into_pandas(a_fn)
AMPLITUDES.append([ht, lt])
# read hydrograph-data into pd.dataframe
data = process2pandas.read_hydrographs_into_pandas(fname, datetime_indexes=True)
# now plot figure...
# ---------------------
#AMPLITUDES = None
#fign = None
with sns.axes_style("whitegrid"):
plot(data, saveName=fign, extrem=AMPLITUDES,
axeslabel_fontsize=18., title_fontsize=20., axesvalues_fontsize=18., annotation_fontsize=18., legend_fontsize=18.)
def method_2_from_cycle_std(savemode=None):
'''
savemod =
None - Default. No output will be saved
'excel' or 'xls' - output will be saved into EXCEL spreedsheet
'csv' - output will be saved into CSV text file
Functions caclulates tidal effcicency for each t.cycle Ei in two ways:
Ei = std(Hydrograph_gw_i)/std(Hydrograph_w_i)
Ei = amplitude(Hydrograph_gw_i)/amplitude(Hydrograph_w_i)
and them saves all values into output file
'''
# ---------------------------------------
# user inputs
# ---------------------------------------
file_folder = '../data/SLICED_171020141500_130420150600/hydrographs/'
amplitudes_folder = '../data/SLICED_171020141500_130420150600/amplitude/'
file_name = 'Farge-ALL_10min.all'
path_out = 'out/'
fname_out = 'output_tidal_efficiency'
ampl_fnames = ['GW_1_amplitude.all', 'GW_2_amplitude.all', 'GW_3_amplitude.all', 'GW_4_amplitude.all', 'GW_5_amplitude.all', 'GW_6_amplitude.all']
river_ampl_fname = 'W_1_amplitude.all'
# ---------------------------------------
# END user inputs END
# ---------------------------------------
path = os.path.dirname(sys.argv[0])
fname = os.path.abspath(os.path.join(path, file_folder, file_name) )
# checking how to save output
if savemode in ['xls', 'xlsx', 'excel', 'msexcel', 'Excel', 'XLS', 'XLSX']:
outputfname = os.path.abspath(os.path.join(path, path_out, fname_out+'.xls'))
writer = pd.ExcelWriter(outputfname)
elif savemode in ['CSV', 'csv']:
writer = None
else:
writer = None
# read hydrographs into pandas dataframe
data = process2pandas.read_hydrographs_into_pandas(fname, log=False)
print '-'*50
print 'Standart deviation all hydrographs'
for n in ['GW_1', 'GW_2', 'GW_3', 'GW_4', 'GW_5', 'GW_6', 'W_1', ]:
print '\t', n, data[n].std()
print '-'*50
# read amplitude&high/low-tide into pandas dataframe
river_hightide, river_lowtide, river_amp = process2pandas.read_amplitudes_into_pandas(os.path.join(path, amplitudes_folder, river_ampl_fname))
# -------------------------------------------------------------------------------
# (1) WORKING WITH River data............................
# -------------------------------------------------------------------------------
W_1 = pd.DataFrame() # creating new dataframe
W_1['Datetime High Tide'] = river_hightide['datetime']
W_1['Datetime Low Tide'] = river_lowtide['datetime']
W_1['High Tide [m AMSL]'] = river_hightide['y']
W_1['Low Tide [m AMSL]'] = river_lowtide['y']
W_1['Amplitude_W [m]'] = river_amp
W_1['STD_W_i'] = 0
W_1['H_gw1_at_W_low[m AMSL]'] = 0 # waterlevel at GW wells at the moment of River peaks
W_1['H_gw2_at_W_low[m AMSL]'] = 0 # waterlevel at GW wells at the moment of River peaks
W_1['H_gw3_at_W_low[m AMSL]'] = 0 # waterlevel at GW wells at the moment of River peaks
W_1['H_gw4_at_W_low[m AMSL]'] = 0 # waterlevel at GW wells at the moment of River peaks
W_1['H_gw5_at_W_low[m AMSL]'] = 0 # waterlevel at GW wells at the moment of River peaks
W_1['H_gw6_at_W_low[m AMSL]'] = 0 # waterlevel at GW wells at the moment of River peaks
W_1['H_gw1_at_W_high[m AMSL]'] = 0 # waterlevel at GW wells at the moment of River peaks
W_1['H_gw2_at_W_high[m AMSL]'] = 0 # waterlevel at GW wells at the moment of River peaks
W_1['H_gw3_at_W_high[m AMSL]'] = 0 # waterlevel at GW wells at the moment of River peaks
W_1['H_gw4_at_W_high[m AMSL]'] = 0 # waterlevel at GW wells at the moment of River peaks
W_1['H_gw5_at_W_high[m AMSL]'] = 0 # waterlevel at GW wells at the moment of River peaks
W_1['H_gw6_at_W_high[m AMSL]'] = 0 # waterlevel at GW wells at the moment of River peaks
# now loop over tidal-cycles, picking values at corresponding H/L-tides in wells
nCycle = 0
for t_ht_w, t_lt_w in zip(river_hightide["datetime"], river_lowtide["datetime"]):
i_ht_w = data[data['datetime'] == t_ht_w].index[0] # should return index of an element <t_ht_gw> in series <data>
i_start_cycle_w = i_ht_w - 18 # here we go 180min before highpeak
i_end_cycle_w = i_start_cycle_w + 73 # here we go 730min after beggining of cycle
std_w = data.ix[i_start_cycle_w:i_end_cycle_w, "W_1"].std() # calculate standart deviation of current tidal-cycle for river
W_1.ix[nCycle, 'STD_W_i'] = std_w # save value
W_1.ix[nCycle, 'H_gw1_at_W_high[m AMSL]'] = data.ix[i_ht_w, 'GW_1']
W_1.ix[nCycle, 'H_gw2_at_W_high[m AMSL]'] = data.ix[i_ht_w, 'GW_2']
W_1.ix[nCycle, 'H_gw3_at_W_high[m AMSL]'] = data.ix[i_ht_w, 'GW_3']
W_1.ix[nCycle, 'H_gw4_at_W_high[m AMSL]'] = data.ix[i_ht_w, 'GW_4']
W_1.ix[nCycle, 'H_gw5_at_W_high[m AMSL]'] = data.ix[i_ht_w, 'GW_5']
W_1.ix[nCycle, 'H_gw6_at_W_high[m AMSL]'] = data.ix[i_ht_w, 'GW_6']
i_lt_w = data[data['datetime'] == t_lt_w].index[0] # should return index of an element <t_lt_gw> in series <data>
W_1.ix[nCycle, 'H_gw1_at_W_low[m AMSL]'] = data.ix[i_lt_w, 'GW_1']
W_1.ix[nCycle, 'H_gw2_at_W_low[m AMSL]'] = data.ix[i_lt_w, 'GW_2']
W_1.ix[nCycle, 'H_gw3_at_W_low[m AMSL]'] = data.ix[i_lt_w, 'GW_3']
W_1.ix[nCycle, 'H_gw4_at_W_low[m AMSL]'] = data.ix[i_lt_w, 'GW_4']
W_1.ix[nCycle, 'H_gw5_at_W_low[m AMSL]'] = data.ix[i_lt_w, 'GW_5']
W_1.ix[nCycle, 'H_gw6_at_W_low[m AMSL]'] = data.ix[i_lt_w, 'GW_6']
nCycle += 1
# calculate overheads (ovepressure?) for each well
W_1['Overhead_gw1_at_W_low[m]'] = W_1['H_gw1_at_W_low[m AMSL]']-W_1['Low Tide [m AMSL]']
W_1['Overhead_gw2_at_W_low[m]'] = W_1['H_gw2_at_W_low[m AMSL]']-W_1['Low Tide [m AMSL]']
W_1['Overhead_gw3_at_W_low[m]'] = W_1['H_gw3_at_W_low[m AMSL]']-W_1['Low Tide [m AMSL]']
W_1['Overhead_gw4_at_W_low[m]'] = W_1['H_gw4_at_W_low[m AMSL]']-W_1['Low Tide [m AMSL]']
W_1['Overhead_gw5_at_W_low[m]'] = W_1['H_gw5_at_W_low[m AMSL]']-W_1['Low Tide [m AMSL]']
W_1['Overhead_gw6_at_W_low[m]'] = W_1['H_gw6_at_W_low[m AMSL]']-W_1['Low Tide [m AMSL]']
W_1['Overhead_gw1_at_W_high[m]'] = W_1['H_gw1_at_W_high[m AMSL]']-W_1['High Tide [m AMSL]']
W_1['Overhead_gw2_at_W_high[m]'] = W_1['H_gw2_at_W_high[m AMSL]']-W_1['High Tide [m AMSL]']
W_1['Overhead_gw3_at_W_high[m]'] = W_1['H_gw3_at_W_high[m AMSL]']-W_1['High Tide [m AMSL]']
W_1['Overhead_gw4_at_W_high[m]'] = W_1['H_gw4_at_W_high[m AMSL]']-W_1['High Tide [m AMSL]']
W_1['Overhead_gw5_at_W_high[m]'] = W_1['H_gw5_at_W_high[m AMSL]']-W_1['High Tide [m AMSL]']
W_1['Overhead_gw6_at_W_high[m]'] = W_1['H_gw6_at_W_high[m AMSL]']-W_1['High Tide [m AMSL]']
# SAVING......
W_1['STD_W_i'] = W_1['STD_W_i'].map(lambda x: '%.3f' % x)
if savemode in ['xls', 'xlsx', 'excel', 'msexcel', 'Excel', 'XLS', 'XLSX']:
W_1.to_excel(writer, 'RIVER', na_rep='---', index=False)
elif savemode in ['CSV', 'csv']:
outputfname = os.path.abspath(os.path.join(path, path_out, 'output_'+'river_data'+'.csv'))
datetime_fmt = '%d.%m.%Y %H:%M'
with open(outputfname, 'w') as csv:
header = '\n'.join(
[unicode(line, 'utf8') for line in
[ 'This File is created by script "calculate_tidal_efficiencies.py',
'In order to convert it to EXCEL use parameter savemode="xlsx"',
'It is possible to save all files to different excel-sheets',
'-'*100, '', # Ends up being the header name for the index.
]
]
)
for line in header:
csv.write(line)
hightide.to_csv(csv, sep=';', na_rep='---', index=False, encoding='utf-8', date_format=datetime_fmt, float_format='%.3f')
csv.close()
print "File created:", outputfname
# -------------------------------------------------------------------------------
# (2) WORKING WITH GW data............................
# -------------------------------------------------------------------------------
# Loop over files with amplitude data for gw well...
for ampl_fn in ampl_fnames:
a_fn = os.path.abspath(os.path.join(path, amplitudes_folder, ampl_fn))
hightide, lowtide, amp = process2pandas.read_amplitudes_into_pandas(a_fn) # read amplitude&high/low-tide into pandas dataframe
#add columns....
hightide['Amplitude_GW [m]'] = amp
hightide['Amplitude_W [m]'] = river_amp
hightide['STD_GW_i'] = 0.
hightide['STD_W_i'] = 0.
hightide['E_i (amplitude ratio)'] = hightide['Amplitude_GW [m]']/hightide['Amplitude_W [m]']
hightide['E_i (std ratio)'] = 0.
# now loop trough all tidal-cycles (more precisely - hightide datetimes)
for nCycle, t_ht_gw in enumerate(hightide["datetime"]): # nCycle - number of current tidal-cycle
# find indexes in hydrograph series for gw...
i_ht_gw = data[data['datetime'] == t_ht_gw].index[0] # should return index of an element <t_ht_gw> in series <data>
i_start_cycle_gw = i_ht_gw - 18 # (same values as in River section above) here we go 180min before highpeak
i_end_cycle_gw = i_start_cycle_gw + 73 # (same values as in River section above) here we go 730min after beggining of cycle
# find indexes in hydrograph series for w...
t_ht_w = river_hightide.ix[nCycle, "datetime"]
i_ht_w = data[data['datetime'] == t_ht_w].index[0] # should return index of an element <t_ht_w> in series <data>
i_start_cycle_w = i_ht_w - 18 # (same values as in River section above) here we go 180min before highpeak
i_end_cycle_w = i_start_cycle_w + 73 # (same values as in River section above) here we go 730min after beggining of cycle
# calculate STD for current tidal-cycle
std_w = data.ix[i_start_cycle_w:i_end_cycle_w, "W_1"].std()
std_gw = data.ix[i_start_cycle_gw:i_end_cycle_gw, ampl_fn[:4]].std()
E_i = std_gw/std_w
#print ampl_fn[:4], '\te_cycle:', E_i, std_w, std_gw
hightide.ix[nCycle, 'STD_GW_i'] = std_gw # save to dataframe
hightide.ix[nCycle, 'STD_W_i'] = std_w # save to dataframe
hightide.ix[nCycle, 'E_i (std ratio)'] = E_i # save to dataframe
#--------------------------------------------------------------------------------
# at this moment everything is already caclculated and stays in memory....
#--------------------------------------------------------------------------------
# Now create nice file....
col_names = list(hightide.columns.values)
col_names[0] = "Datetime High Tide"
col_names[1] = "High Tide [m AMSL]"
hightide.columns = col_names
hightide.insert(1, 'Datetime Low Tide', lowtide["datetime"])
hightide.insert(3, 'Low Tide [m AMSL]', lowtide["y"])
E1 = hightide['E_i (std ratio)'].mean()
E2 = hightide['E_i (amplitude ratio)'].mean()
print ampl_fn
print 'mean_E(std), mean_E(ampl)', E1, E2
# change precision to 3 digits
hightide['STD_GW_i'] = hightide['STD_GW_i'].map(lambda x: '%.3f' % x)
hightide['STD_W_i'] = hightide['STD_W_i'].map(lambda x: '%.3f' % x)
hightide['E_i (std ratio)'] = hightide['E_i (std ratio)'].map(lambda x: '%.3f' % x)
hightide['E_i (amplitude ratio)'] = hightide['E_i (amplitude ratio)'].map(lambda x: '%.3f' % x)
# Write to file
if savemode in ['xls', 'xlsx', 'excel', 'msexcel', 'Excel', 'XLS', 'XLSX']:
hightide.to_excel(writer, ampl_fn[:4], na_rep='---', index=False)
elif savemode in ['CSV', 'csv']:
outputfname = os.path.abspath(os.path.join(path, path_out, 'output_'+ampl_fn[:-4]+'.csv'))
datetime_fmt = '%d.%m.%Y %H:%M'
with open(outputfname, 'w') as csv:
header = '\n'.join(
[unicode(line, 'utf8') for line in
[ 'This File is created by script "calculate_tidal_efficiencies.py',
'In order to convert it to EXCEL use parameter savemode="xlsx"',
'It is possible to save all files to different excel-sheets',
'-'*100, '', # Ends up being the header name for the index.
]
]
)
for line in header:
csv.write(line)
hightide.to_csv(csv, sep=';', na_rep='---', index=False, encoding='utf-8', date_format=datetime_fmt, float_format='%.3f')
csv.close()
print "File created:", outputfname
# close XLS writer if exist
if savemode and writer:
writer.save()
print "File created:", outputfname
