fact.analysis package

fact.analysis.li_ma_significance(n_on, n_off, alpha=0.2)

Calculate the Li&Ma significance for given observations data

Parameters:

• n_on (integer or array like) – Number of events for the on observations
• n_off (integer of array like) – Number of events for the off observations
• alpha (float) – Scaling factor for the off observations, for wobble observations this is 1 / number of off regions

fact.analysis.ontime_binning(runs, bin_width_minutes=20)

Calculate bin numbers for given runs. A new bin is created if either a bin would have more ontime than bin_width_minutes or run_start of the next run is more than bin_width_minutes after run_stop of the last run.

Parameters:

• runs (pd.DataFrame) – DataFrame containing analysis results and meta data for each run
• bin_width_minutes (number) – The desired amount of ontime in each bin. Note: The ontime in each bin will allways be slightly less than bin_width_minutes

fact.analysis.qla_binning(data, bin_width_minutes=20)

The binning algorithm as used by lightcurve.c

fact.analysis.groupby_observation_blocks(runs)

Groupby for consecutive runs of the same source

fact.analysis.bin_runs(runs, alpha=0.2, binning_function=<function ontime_binning>, **kwargs)

Bin runs using binning_function to assign bins to the individual runs. Calculates n_on, n_off, ontime, n_excess, excess_rate_per_h, excess_rate_err, li_ma_significance and bin_width

Parameters:

• runs (pandas.DataFrame) – The analysis results and necessary metadata for each run. Required are: ontime, n_on, n_off, run_start, run_stop, source
• alpha (float) – The weight for the off regions, e.g. 1 / number of off regions
• binning_function (function) – A function that takes the run df and returns a pd.Series containing bin ids with the index of the origininal dataframe
• **kwargs are passed to the binning function (All) –

fact.analysis.calc_run_summary_source_independent(events, runs, prediction_threshold, theta2_cut, prediction_key='signal_prediction', theta_key='Theta_deg', theta_off_keys=('Theta_Off_1_deg', 'Theta_Off_2_deg', 'Theta_Off_3_deg', 'Theta_Off_4_deg', 'Theta_Off_5_deg'))

Calculate run summaries for the given theta^2 and signal prediction cuts. This function requires, that no source dependent features, like Theta were used in the classification.

Parameters:

• events (pd.DataFrame) – DataFrame with event data, needs to contain the columns ‘night’, ‘run’, theta_key and the theta_off_keys
• prediction_threshold (float) – Threshold for the classifier prediction
• theta2_cut (float) – Selection cut for theta^2 in deg^2
• prediction_key (str:) – Key to the classifier prediction
• theta_key (str) – Column name of the column containing theta in degree
• theta_off_keys (list[str]) – Column names of the column containing theta in degree for all off regions

fact.analysis.calc_run_summary_source_dependent(events, runs, prediction_threshold, on_prediction_key='signal_prediction', off_prediction_keys=('background_prediction_1', 'background_prediction_2', 'background_prediction_3', 'background_prediction_4', 'background_prediction_5'))

Calculate run summaries for the given signal prediction cuts. This function needs to be used, if source dependent features like Theta were used for the classification.

Parameters:

• events (pd.DataFrame) – DataFrame with event data, needs to contain the columns ‘night’, ‘run’, theta_key and the theta_off_keys
• prediction_threshold (float) – Threshold for the signalness prediction
• on_prediction_key (str) – Key to the classifier prediction for the on region
• off_prediction_keys (list[str]) – Iterable of keys to the classifier predictions for the off regions

fact.analysis.split_on_off_source_dependent(events, prediction_threshold, on_prediction_key='signal_prediction', off_prediction_keys=('background_prediction_1', 'background_prediction_2', 'background_prediction_3', 'background_prediction_4', 'background_prediction_5'))

Split events dataframe into on and off region For the off regions, keys are renamed to their “on” equivalents and the “off” keys are dropped.

Parameters:

• events (pd.DataFrame) – DataFrame containing event information, required are theta_key and theta_off_keys.
• prediction_threshold (float) – Threshold for the signalness prediction
• on_prediction_key (str) – Key to the classifier prediction for the on region
• off_prediction_keys (list[str]) – Iterable of keys to the classifier predictions for the off regions

fact.analysis.split_on_off_source_independent(events, theta2_cut, theta_key='Theta_deg', theta_off_keys=('Theta_Off_1_deg', 'Theta_Off_2_deg', 'Theta_Off_3_deg', 'Theta_Off_4_deg', 'Theta_Off_5_deg'))

Split events dataframe into on and off region

Parameters:

• events (pd.DataFrame) – DataFrame containing event information, required are theta_key and theta_off_keys.
• theta2_cut (float) – Selection cut for theta^2 in deg^2
• theta_key (str) – Column name of the column containing theta in degree
• theta_off_keys (list[str]) – Column names of the column containing theta in degree for all off regions

Submodules

fact.analysis.binning module

fact.analysis.binning.bin_runs(runs, alpha=0.2, binning_function=<function ontime_binning>, **kwargs)

Bin runs using binning_function to assign bins to the individual runs. Calculates n_on, n_off, ontime, n_excess, excess_rate_per_h, excess_rate_err, li_ma_significance and bin_width

Parameters:

• runs (pandas.DataFrame) – The analysis results and necessary metadata for each run. Required are: ontime, n_on, n_off, run_start, run_stop, source
• alpha (float) – The weight for the off regions, e.g. 1 / number of off regions
• binning_function (function) – A function that takes the run df and returns a pd.Series containing bin ids with the index of the origininal dataframe
• **kwargs are passed to the binning function (All) –

fact.analysis.binning.groupby_observation_blocks(runs)

Groupby for consecutive runs of the same source

fact.analysis.binning.nightly_binning(runs)

fact.analysis.binning.ontime_binning(runs, bin_width_minutes=20)

Calculate bin numbers for given runs. A new bin is created if either a bin would have more ontime than bin_width_minutes or run_start of the next run is more than bin_width_minutes after run_stop of the last run.

Parameters:

• runs (pd.DataFrame) – DataFrame containing analysis results and meta data for each run
• bin_width_minutes (number) – The desired amount of ontime in each bin. Note: The ontime in each bin will allways be slightly less than bin_width_minutes

fact.analysis.binning.qla_binning(data, bin_width_minutes=20)

The binning algorithm as used by lightcurve.c

fact.analysis.core module

fact.analysis.core.calc_run_summary_source_dependent(events, runs, prediction_threshold, on_prediction_key='signal_prediction', off_prediction_keys=('background_prediction_1', 'background_prediction_2', 'background_prediction_3', 'background_prediction_4', 'background_prediction_5'))

Calculate run summaries for the given signal prediction cuts. This function needs to be used, if source dependent features like Theta were used for the classification.

Parameters:

• events (pd.DataFrame) – DataFrame with event data, needs to contain the columns ‘night’, ‘run’, theta_key and the theta_off_keys
• prediction_threshold (float) – Threshold for the signalness prediction
• on_prediction_key (str) – Key to the classifier prediction for the on region
• off_prediction_keys (list[str]) – Iterable of keys to the classifier predictions for the off regions

fact.analysis.core.calc_run_summary_source_independent(events, runs, prediction_threshold, theta2_cut, prediction_key='signal_prediction', theta_key='Theta_deg', theta_off_keys=('Theta_Off_1_deg', 'Theta_Off_2_deg', 'Theta_Off_3_deg', 'Theta_Off_4_deg', 'Theta_Off_5_deg'))

Calculate run summaries for the given theta^2 and signal prediction cuts. This function requires, that no source dependent features, like Theta were used in the classification.

Parameters:

• events (pd.DataFrame) – DataFrame with event data, needs to contain the columns ‘night’, ‘run’, theta_key and the theta_off_keys
• prediction_threshold (float) – Threshold for the classifier prediction
• theta2_cut (float) – Selection cut for theta^2 in deg^2
• prediction_key (str:) – Key to the classifier prediction
• theta_key (str) – Column name of the column containing theta in degree
• theta_off_keys (list[str]) – Column names of the column containing theta in degree for all off regions

fact.analysis.core.drop_off_columns(df, off_region, inplace=False)

Replace the “On” column with the column of the respective off region. For example for off_region = 1, Theta is replaced by Theta_Off_1 and all Theta_Off_<N> columns are dropped. Same for all other columns, containing the pattern _Off_<N>

fact.analysis.core.split_on_off_source_dependent(events, prediction_threshold, on_prediction_key='signal_prediction', off_prediction_keys=('background_prediction_1', 'background_prediction_2', 'background_prediction_3', 'background_prediction_4', 'background_prediction_5'))

Split events dataframe into on and off region For the off regions, keys are renamed to their “on” equivalents and the “off” keys are dropped.

Parameters:

• events (pd.DataFrame) – DataFrame containing event information, required are theta_key and theta_off_keys.
• prediction_threshold (float) – Threshold for the signalness prediction
• on_prediction_key (str) – Key to the classifier prediction for the on region
• off_prediction_keys (list[str]) – Iterable of keys to the classifier predictions for the off regions

fact.analysis.core.split_on_off_source_independent(events, theta2_cut, theta_key='Theta_deg', theta_off_keys=('Theta_Off_1_deg', 'Theta_Off_2_deg', 'Theta_Off_3_deg', 'Theta_Off_4_deg', 'Theta_Off_5_deg'))

Split events dataframe into on and off region

Parameters:

• events (pd.DataFrame) – DataFrame containing event information, required are theta_key and theta_off_keys.
• theta2_cut (float) – Selection cut for theta^2 in deg^2
• theta_key (str) – Column name of the column containing theta in degree
• theta_off_keys (list[str]) – Column names of the column containing theta in degree for all off regions

fact.analysis.statistics module

fact.analysis.statistics.li_ma_significance(n_on, n_off, alpha=0.2)

Calculate the Li&Ma significance for given observations data

Parameters:

• n_on (integer or array like) – Number of events for the on observations
• n_off (integer of array like) – Number of events for the off observations
• alpha (float) – Scaling factor for the off observations, for wobble observations this is 1 / number of off regions