Index: branches/trigger_burst_research/sketch.py
===================================================================
--- branches/trigger_burst_research/sketch.py	(revision 18289)
+++ branches/trigger_burst_research/sketch.py	(revision 18289)
@@ -0,0 +1,107 @@
+# coding: utf-8
+from collections import namedtuple
+from sqlalchemy import create_engine
+import pandas as pd
+import numpy as np
+from astropy.io import fits
+import fact
+
+night_int = 20150721
+
+def create_DB_connection():
+    from ConfigParser import SafeConfigParser
+    config = SafeConfigParser()
+    config.optionxform = str  # this make the parsing case sensitive
+    config.read('config.ini')
+
+    factdb = create_engine(
+        "mysql+mysqldb://{user}:{pw}@{host}/{db}".format(
+            user=config.get('database', 'user'),
+            pw=config.get('database', 'password'),
+            host=config.get('database', 'host'),
+            db=config.get('database', 'database'),
+        )
+    )
+
+    return factdb
+
+
+def get_data_runs_from_run_db(night_int, factdb):
+    keys = [
+        'fRunID',
+        'fNight',
+        'fRunStart',
+        'fRunStop',
+        ]
+
+    sql_query = """SELECT {comma_sep_keys}
+        FROM RunInfo WHERE fRunTypeKey=1 AND fNight={night:d};
+        """
+    sql_query = sql_query.format(
+        comma_sep_keys=', '.join(keys),
+        night=night_int,
+        )
+    
+    data_runs = pd.read_sql_query(
+        sql_query,
+        factdb,
+        parse_dates=['fRunStart', 'fRunStop'],
+    )
+
+    return data_runs
+
+
+def get_trigger_rates(night_int, base_path='other_aux_data/'):
+    fits_file = fits.open(
+        base_path+'{0}.FTM_CONTROL_TRIGGER_RATES.fits'.format(night_int)
+        )
+    trigger_rates = fits_file[1].data
+    return trigger_rates
+
+
+def add_ratio_and_more_to_dataframe(data_runs, trigger_rates):
+
+    # create new columns and pre-assign some hopefully good NULL-like-values
+    data_runs['number_of_measurements'] = 0
+    data_runs['median_of_board_rates'] = np.nan
+    data_runs['std_dev_of_board_rates'] = np.nan
+    data_runs['fBoardTriggerRateRatioAboveThreshold'] = np.nan
+
+    for dataframe_index in data_runs.index:
+        this_run = data_runs.ix[dataframe_index]
+        mask = (
+            (trigger_rates['Time'] > fact.fjd(this_run['fRunStart'])) *
+            (trigger_rates['Time'] < fact.fjd(this_run['fRunStop']))
+            )
+        this_board_rates = trigger_rates['BoardRate'][mask]
+
+        N = this_board_rates.shape[0]
+        data_runs.ix[dataframe_index, 'number_of_measurements'] = N
+        
+        left, med, right = np.percentile(
+            this_board_rates,
+            [50-20, 50, 50+20])
+        something_like_width = (right - left)/2.
+        # 40% of the area of the normal distrubution
+        # fall between -0.52*sigma and +0.52*sigma.
+        # The estimation of sigma in a distorted CDF 
+        # is less affected near the median. (but of course less accurate as well.)
+        std_dev = something_like_width / 0.52
+        
+        median_board_rates = np.median(this_board_rates, axis=1)
+        over = (median_board_rates > med+3*std_dev).sum()
+
+        data_runs.ix[dataframe_index, 'median_of_board_rates'] = med
+        data_runs.ix[dataframe_index, 'std_dev_of_board_rates'] = std_dev
+        data_runs.ix[dataframe_index, 'fBoardTriggerRateRatioAboveThreshold'] = float(over)/N
+
+def main(night_int):
+    factdb = create_DB_connection()
+    data_runs = get_data_runs_from_run_db(night_int, factdb)
+    trigger_rates = get_trigger_rates(night_int)
+    add_ratio_and_more_to_dataframe(data_runs, trigger_rates)
+    return data_runs
+
+if __name__ == '__main__':
+    data_runs = main(night_int)
+    data_runs.to_csv('foo.bar', index=False)