def load():
df = pd.read_csv(os.path.join(basedir, 'Businesses_Registered_in_San_Francisco_-_Active.csv'))
df.rename(columns = lambda x: re.sub(' ', '_', x), inplace = True)
df['Class_Code'] = np.int32(df.Class_Code)
df['PBC_Code'] = np.int32(df.PBC_Code)
'''Convert lat, long from string to float'''
p = re.compile('\d*\.\d*')
LocX = []; LocY = []; ix = []
for i, j in df.iterrows():
if pd.notnull(j['Location']):
x, y = np.float32(p.findall(j['Location']))
LocX.append(x); LocY.append(y)
ix.append(i)
df['LocX'] = pd.Series(LocX, index = ix)
df['LocY'] = pd.Series(LocY, index = ix)
'''Add description field from PBC code descriptions'''
pbc2descr = dict(np.loadtxt('pbc_codes.csv', 'S', delimiter = ','))
Descr = []
for i in df.PBC_Code:
try:
Descr.append(pbc2descr[str(i)])
except:
Descr.append('UNKNOWN')
df['Descript'] = Descr
'''Add founded date from str to datetime'''
tmp = [(i, misc.str2date(str(j['DBA_Start_Date']), delimiter = '', format = 'YYYYMMDD')) for i, j in df.iterrows()]
ix, dates = zip(*tmp)
df['Founded'] = pd.Series(dates, index = ix)
return df
def fileconvert_all(studydir):
'''
Converts all of the text file outputs in */Gap/data and converts them to pandas formatted hdf5 files.
Stores the results in */Gap/fileconversion
'''
# dobs = pd.read_csv(os.path.join(studydir, 'dobs.csv'))
pairpulsedir = os.path.join(studydir, 'pairpulse')
if not os.path.exists(pairpulsedir):
os.mkdir(pairpulsedir)
animalpaths = glob.glob(os.path.join(studydir, 'data', 'PPI', '[0-9]*'))
for animalpath in animalpaths:
fpaths = glob.glob(os.path.join(animalpath, '[A-Za-z]*.txt'))
for fpath in fpaths:
absol, relat = os.path.split(fpath)
animalID, gen, condition, mo, da, yr, _ = relat.split('_')
animalinfo = get_animalinfo(animalID, studydir)
dob_str = animalinfo.DOB.values[0]
dob = misc.str2date(dob_str, delimiter = '/', format = 'MMDDYYYY')
if hasattr(animalinfo, 'date1'):
date1_str = animalinfo.date1.values[0]
date1 = misc.str2date(date1_str, delimiter = '/', format = 'MMDDYYYY')
mo = '%2.2u' % int(mo)
da = '%2.2u' % int(da)
yr = '%4.4u' % int(yr)
sess_str = '_'.join((yr, mo, da))
sess_date = misc.str2date(sess_str, delimiter = '_', format = 'YYYYMMDD')
age = (sess_date - dob).days
if hasattr(animalinfo, 'date1'):
postdate1 = (sess_date - date1).days
outpath = os.path.join(pairpulsedir, '%s.csv' % '_'.join((animalID, gen, condition, yr, mo, da)))
if not os.path.exists(outpath):
gapratio = fileconvert(fpath)
if hasattr(animalinfo, 'date1'):
df = pd.DataFrame(dict(gapratio = gapratio, animalID = animalID, gen = gen, condition = condition, sess = sess_str, age = age, postdate1 = postdate1))
else:
df = pd.DataFrame(dict(gapratio = gapratio, animalID = animalID, gen = gen, condition = condition, sess = sess_str, age = age))
df.to_csv(outpath)
def fileconvert_processed(studydir):
freqs = [5000, 7071, 10000, 14142, 20000, 28284]
gapdetectiondir = os.path.join(studydir, 'gapdetection')
if not os.path.exists(gapdetectiondir):
os.mkdir(gapdetectiondir)
cagepaths = glob.glob(os.path.join(studydir, 'data', 'Gap', '[0-9]*'))
dobs = pd.read_csv(os.path.join(studydir, 'dobs.csv'))
for cagepath in cagepaths:
absol, cageID = os.path.split(cagepath)
animalIDs = [i for i in dobs.animalID if cageID in i]
fpaths = glob.glob(os.path.join(cagepath, '*.txt'))
for fpath in fpaths:
df = pd.read_csv(fpath, usecols=range(6), sep='\t', header=None, nrows=len(animalIDs))
df.index = animalIDs
absol, relat = os.path.split(fpath)
if relat.startswith('_'):
_, gen, condition, mo, da, yr, _ = relat[1:].split('_')
else:
_, gen, condition, mo, da, yr, _ = relat.split('_')
for animalID in animalIDs:
newrelat = '%s.csv' % '_'.join((animalID, gen, condition, yr, mo, da))
if relat.startswith('_'):
newrelat = '_' + newrelat
outpath = os.path.join(studydir, 'gapdetection', newrelat)
# skip if the file already exists
if os.path.exists(outpath): continue
gapratio = df.ix[animalID].values
# start building the dataframe dict
d = OrderedDict()
# add required fields (gapratio, animalID, genotype, condition, session date, age)
d.update(dict(freq=freqs, gapratio=gapratio, animalID=animalID, gen=gen, condition=condition))
# load the animal DOB, group, etc. from the dobs.csv file
animalinfo = get_animalinfo(animalID, studydir)
# get the date of birth
dob_str = animalinfo.DOB.values[0]
dob = misc.str2date(dob_str, delimiter = '/', format = 'MMDDYYYY')
animalinfo['DOB'] = dob
# how old was the animal when this session was run?
sess_str = '_'.join((yr, mo, da))
sess_date = misc.str2date(sess_str, delimiter = '_', format = 'YYYYMMDD')
age = (sess_date - dob).days
d.update(dict(sess = sess_date, age = age))
# how many days was this session from each "date" column in the animalinfo?
dateinfo = animalinfo.filter(regex='date*')
d_postdate = OrderedDict()
for key, value in dateinfo.iteritems():
date = misc.str2date(value.values[0], delimiter='/', format='MMDDYYYY')
d_postdate.update({'post'+key: (sess_date-date).days})
d.update(d_postdate)
# add all supplementary animalinfo fields
for key, value in animalinfo.iteritems():
d.update({key: value.values[0]})
pd.DataFrame(d).to_csv(outpath)
def fileconvert_all(studydir):
'''
Converts all of the text file outputs in */Gap/data and converts them to pandas formatted hdf5 files.
Stores the results in */Gap/fileconversion
'''
gapdetectiondir = os.path.join(studydir, 'gapdetection')
if not os.path.exists(gapdetectiondir):
os.mkdir(gapdetectiondir)
# loop through all animals
animalpaths = glob.glob(os.path.join(studydir, 'data', 'Gap', '[0-9]*'))
for animalpath in animalpaths:
fpaths = glob.glob(os.path.join(animalpath, '*.txt'))
for fpath in fpaths:
absol, relat = os.path.split(fpath)
if relat.startswith('_'):
animalID, gen, condition, mo, da, yr, _ = relat[1:].split('_')
else:
animalID, gen, condition, mo, da, yr, _ = relat.split('_')
newrelat = '%s.csv' % '_'.join((animalID, gen, condition, yr, mo, da))
if relat.startswith('_'):
newrelat = '_' + newrelat
outpath = os.path.join(studydir, 'gapdetection', newrelat)
# skip if the file already exists
if os.path.exists(outpath): continue
# calculate gapratio
df = fileconvert(fpath)
df['animalID'] = animalID
df['gen'] = gen
df['condition'] = condition
# load the animal DOB, group, etc. from the dobs.csv file
animalinfo = get_animalinfo(animalID, studydir)
# get the date of birth
dob_str = animalinfo.DOB.values[0]
dob = misc.str2date(dob_str, delimiter = '/', format = 'MMDDYYYY')
animalinfo['DOB'] = dob
# how old was the animal when this session was run?
sess_str = '_'.join((yr, mo, da))
sess_date = misc.str2date(sess_str, delimiter = '_', format = 'YYYYMMDD')
age = (sess_date - dob).days
df['sess'] = sess_date
df['age'] = age
# d.update(dict(sess = sess_date, age = age))
# how many days was this session from each "date" column in the animalinfo?
dateinfo = animalinfo.filter(regex='date*')
d_postdate = OrderedDict()
for key, value in dateinfo.iteritems():
date = misc.str2date(value.values[0], delimiter='/', format='MMDDYYYY')
df['post'+key] = (sess_date-date).days
# add all supplementary animalinfo fields
for key, value in animalinfo.iteritems():
df[key] = value.values[0]
df.to_csv(outpath)
