General Tools

phyto_photo_utils._tools.calculate_blank_FIRe(file_)

Calculates the blank by averaging the fluorescence yield for the saturation phase.

Parameters:file (str) – The path directory to the raw blank file.
Returns:res – The blank results: blank, datetime
Return type:pandas.DataFrame

Example

>>> ppu.calculate_blank_FIRe(file_)
phyto_photo_utils._tools.calculate_blank_FastOcean(file_, seq_len=100, delimiter=', ')

Calculates the blank by averaging the fluorescence yield for the saturation phase.

Parameters:
  • file (str) – The path directory to the raw blank file in csv format.
  • seq_len (int, default=100) – The length of the measurement sequence.
  • delimiter (str, default=',') – Specify the delimiter to be used by Pandas.read_csv for loading the raw files.
Returns:

res – The blank results.
Return type:

pandas.DataFrame

Example

>>> ppu.calculate_blank_FastOcean(file_, seq_len=100)
phyto_photo_utils._tools.correct_fire_instrument_bias(df, pos=1, sat_len=100)

Corrects for instrumentation bias in the relaxation phase by calculating difference between flashlet 0 the relaxation phase & flashlet[pos]. This bias is then added to the relaxation phase.

Parameters:
  • df (pandas.DataFrame) – A dataframe of the raw data, can either be imported from pandas.read_csv or the output from phyto_photo_utils.load
  • pos (int, default=1) – The flashlet number after the start of the phase, either saturation or relaxation, to calculate difference between.
  • sat_len (int, default=100) – The length of saturation measurements.
Returns:

df – A dataframe of FIRe data corrected for the instrument bias.
Return type:

pandas.DataFrame

Example

>>> ppu.correct_fire_bias_correction(df, pos=1, sat_len=100)
phyto_photo_utils._tools.remove_outlier_from_time_average(df, time=4, multiplier=3)

Remove outliers when averaging transients before performing the fitting routines, used to improve the signal to noise ratio in low biomass systems.

The function sets a time window to average over, using upper and lower limits for outlier detection. The upper and lower limits are determined by mean ± std * [1]. The multiplier [1] can be adjusted by the user.

Parameters:
  • df (pandas.DataFrame) – A dataframe of the raw data, can either be imported from pandas.read_csv or the output from phyto_photo_utils.load
  • time (int, default=4) – The time window to average over, e.g. 4 = 4 minute averages
  • multiplier (int, default=3) – The multiplier to apply to the standard deviation for determining the upper and lower limits.
Returns:

df – A dataframe of the time averaged data with outliers excluded.
Return type:

pandas.DataFrame

Example

>>> ppu.remove_outlier_from_time_average(df, time=2, multiplier=3)