def __init__(self, filename, azimuth=180, datasetname=''):
self.filename = filename
if datasetname:
self.datasetname = datasetname
else:
self.datasetname = filename
self.df = make_df_from_columndata(filename)
self.df = self._slice_azm(azimuth)
def _readtodataframe(self):
df_list = []
for scenario in self.filenames:
df = pd.DataFrame()
print("Reading scenario: ", scenario)
print("From file: ", self.filenames[scenario])
df = make_df_from_columndata(self.filenames[scenario])
df['Scenario'] = scenario
df_list.append(df)
df_combined = pd.concat(df_list)
return df_combined
def _readtodataframe(self):
df_list = []
for configuration in self.filenames:
df = pd.DataFrame()
print("Reading configuration: ", configuration)
print("From file: ", self.filenames[configuration])
df = make_df_from_columndata(self.filenames[configuration])
df['Configuration'] = configuration
df = df[(df['Offset'] >= self.minoffset)
& (df['Offset'] <= self.maxoffset)]
df_list.append(df)
df_combined = pd.concat(df_list)
print(f"Offsets limited to [{self.minoffset}, {self.maxoffset}]m")
return df_combined.reset_index()
def read_col_data_depth_twt(self, filename, timeunit='ms'):
if timeunit == 's':
c = 1
elif timeunit == 'ms':
c = 1000
else:
print('Wrong time unit given. Defaulting to miliseconds')
c = 1000
df_temp = make_df_from_columndata(filename).reset_index().drop(
columns=['numRows'])
df_temp.columns = ['Depth', 'TWT']
df_temp['Vp'] = 2 * c * df_temp['Depth'] / df_temp['TWT']
print(df_temp)
df_temp = df_temp.interpolate(limit_direction='backward')
print(df_temp)
self.df_vels = df_temp[['Depth', 'Vp']]
print(
'Velocity calculated is average, use method "velconvert_depth_avg_to_depth_int" before model building'
)
def read_col_data(self, filename):
self.df_vels = make_df_from_columndata(filename).reset_index().drop(
columns=['numRows'])
self.df_vels.columns = ['Depth', 'Vp']
self.df_vels = self.df_vels[['Depth', 'Vp']]