def load_ztf_data(
phot_path='/media/biswajit/drive/Kilonova_datasets/ZTF_20190512/ZTF_MSIP_MODEL64/ZTF_MSIP_NONIaMODEL0-0001_PHOT.FITS',
drop_separators=True):
df_header, df_phot = read_fits(phot_path)
df_header = Table.from_pandas(df_header)
df_phot = Table.from_pandas(df_phot)
df_phot['FLT'][df_phot['FLT'] == b'g '] = 'g'
df_phot['FLT'][df_phot['FLT'] == b'r '] = 'r'
data_ob = Data(df_metadata=df_header,
df_data=df_phot,
object_id_col_name='SNID',
time_col_name='MJD',
target_col_name='SNTYPE',
band_col_name='FLT',
flux_col_name='FLUXCAL',
flux_err_col_name='FLUXCALERR',
band_map={
'g': 'g',
'r': 'r'
},
bands=['g', 'r'])
if drop_separators:
data_ob.df_data = data_ob.df_data[
data_ob.df_data[data_ob.time_col_name] != -777]
return data_ob
def create_alert_data_obj(data, bands):
band_map = {}
for item in bands:
band_map[item] = item
data_ob = Data(df_metadata=data,
df_data=data,
object_id_col_name='SNID',
time_col_name='MJD',
band_col_name='FLT',
flux_col_name='FLUXCAL',
flux_err_col_name='FLUXCALERR',
band_map=band_map,
bands=bands)
return data_ob
def load_ztf_test_data(
head_path='/media/biswajit/drive/Kilonova_datasets/ZTF_20190512/test_HEAD.FITS',
phot_path='/media/biswajit/drive/Kilonova_datasets/ZTF_20190512/test_PHOT.FITS'
):
df_header = Table.read(head_path, format='fits')
df_phot = Table.read(phot_path, format='fits')
data_ob = Data(df_metadata=df_header,
df_data=df_phot,
object_id_col_name='SNID',
time_col_name='MJD',
target_col_name='SNTYPE',
band_col_name='FLT',
flux_col_name='FLUXCAL',
flux_err_col_name='FLUXCALERR',
band_map={
'g': 'g',
'r': 'r'
})
return data_ob
def load_RESSPECT_data(
phot_df_file_path="/media/biswajit/drive/Kilonova_datasets/RESSPECT"
"/RESSPECT_PERFECT_LIGHTCURVE.csv",
meta_df_file_path="/media/biswajit/drive/Kilonova_datasets/RESSPECT/RESSPECT_PERFECT_HEAD.csv"
):
df_meta_data = Table.read(meta_df_file_path, delimiter=",")
df_data = Table.read(phot_df_file_path)
data_ob = Data(df_metadata=df_meta_data,
df_data=df_data,
object_id_col_name='SNID',
time_col_name='MJD',
band_col_name='FLT',
flux_col_name='FLUXCAL',
flux_err_col_name='FLUXCALERR',
band_map={
'u': 'u',
'g': 'g',
'r': 'r',
'i': 'i',
'z': 'z',
'Y': 'y'
})
return data_ob
def load_PLAsTiCC_data(
phot_df_file_path="/media/biswajit/drive/Kilonova_datasets/PLAsTiCC_data/training_set.csv",
meta_df_file_path="/media/biswajit/drive/Kilonova_datasets/PLAsTiCC_data/training_set_metadata.csv"
):
df_meta_data = Table.read(meta_df_file_path, delimiter=",")
df_data = Table.read(phot_df_file_path)
data_ob = Data(df_metadata=df_meta_data,
df_data=df_data,
object_id_col_name='object_id',
time_col_name='mjd',
band_col_name='passband',
flux_col_name='flux',
flux_err_col_name='flux_err',
target_col_name='target',
band_map={
0: 'u',
1: 'g',
2: 'r',
3: 'i',
4: 'z',
5: 'y'
})
return data_ob
time[stamp.timestamp.hour][stamp.sender] += 1
return time
def day_of_week(self):
man, woman = config.config['MAN'], config.config['WOMAN']
weekday = OrderedDict([('Monday',{man:0, woman:0}), ('Tuesday',{man:0, woman:0}), ('Wednesday',{man:0, woman:0}),
('Thursday',{man:0, woman:0}), ('Friday',{man:0, woman:0}), ('Saturday',{man:0, woman:0}), ('Sunday',{man:0, woman:0})])
for index, stamp in self.df.iterrows():
weekday[stamp.timestamp.day_name()][stamp.sender] += 1
return weekday
def messages_per_month(self):
man, woman = config.config['MAN'], config.config['WOMAN']
month = OrderedDict([("January",{man:0, woman:0}),("February",{man:0, woman:0}),("March",{man:0, woman:0}),
("April",{man:0, woman:0}),("May",{man:0, woman:0}),("June",{man:0, woman:0}),("July",{man:0, woman:0}),
("August",{man:0, woman:0}),("September",{man:0, woman:0}),("October",{man:0, woman:0}),
("November",{man:0, woman:0}), ("December",{man:0, woman:0})])
for index, stamp in self.df.iterrows():
month[stamp.timestamp.month_name()][stamp.sender] += 1
return month
if __name__ == '__main__':
data = Data(config.config['FILE_NAME'])
df = data.parse_file()
analysed_data = Analyse(df)
print(analysed_data.words_from_each())
# print(analysed_data.messages_from_each())
# print(analysed_data.average_character_length())