def avg_range(self):
"""
Return the average range (depth to the bottom). This will determine the good values
for the range and average them together.
:return: Average range.
"""
# Average the range
return Ensemble.get_avg_range(self.Range)
def accum_rt_range(self, awc):
"""
Create the Range Tracking dataframe. This takes all the information
from the Range Tracking and creates a row in the dataframe for each bin,beam value.
:param awc: Average data.
:return:
"""
# Convert the east array to df
# params: vel_array, dt, ss_code, ss_config, blank, bin_size
# DF Columns: Index, time_stamp, ss_code, ss_config, bin_num, beam_num, bin_depth, value
df = Ensemble.array_beam_1d_to_df(
awc[AverageWaterColumn.INDEX_RANGE_TRACK],
awc[AverageWaterColumn.INDEX_LAST_TIME],
awc[AverageWaterColumn.INDEX_SS_CODE],
awc[AverageWaterColumn.INDEX_SS_CONFIG],
awc[AverageWaterColumn.INDEX_FIRST_ENS_NUM],
awc[AverageWaterColumn.INDEX_LAST_ENS_NUM])
# Store the range results
if self.df_rt_range.empty:
self.df_rt_range = df
else:
self.df_rt_range = pd.concat([self.df_rt_range, df],
ignore_index=True)
# Average the Range Track Range
avg_range = Ensemble.get_avg_range(
awc[AverageWaterColumn.INDEX_RANGE_TRACK])
# Create a dict entry
dict_result = {}
dict_result[0] = {
'time_stamp': awc[AverageWaterColumn.INDEX_LAST_TIME],
'ss_code': awc[AverageWaterColumn.INDEX_SS_CODE],
'ss_config': awc[AverageWaterColumn.INDEX_SS_CONFIG],
'bin_num': 0,
'beam_num': 0,
'bin_depth': avg_range,
'first_ens_num': awc[AverageWaterColumn.INDEX_FIRST_ENS_NUM],
'last_ens_num': awc[AverageWaterColumn.INDEX_LAST_ENS_NUM],
'value': avg_range
}
# Create the dataframe from the dictionary
# important to set the 'orient' parameter to "index" to make the keys as rows
df = pd.DataFrame.from_dict(dict_result, "index")
# Store the range results
if self.df_avg_rt_range.empty:
self.df_avg_rt_range = df
else:
self.df_avg_rt_range = pd.concat([self.df_avg_rt_range, df],
ignore_index=True)
def avg_range(self):
# Average the ranges
#range_count = 0
#range_accum = 0.0
#for beam in range(int(self.NumBeams)):
# if self.Range[beam] > 0.0:
# range_count += 1
# range_accum += self.Range[beam]
#if range_count > 0:
# return range_accum / range_count
#else:
# return 0.0
return Ensemble.get_avg_range(self.Range)
