def collapse(a, keepcols, collapsecols, stderr=0, ns=0, cfcn=None):
"""Averages data in collapsecol, keeping all unique items in keepcols
(using unique, which keeps unique LISTS of column numbers), retaining
the unique sets of values in keepcols, the mean for each. If the sterr or
N of the mean are desired, set either or both parameters to 1.
Returns: unique 'conditions' specified by the contents of columns specified
by keepcols, abutted with the mean(s,axis=0) of column(s) specified by
collapsecols
"""
if cfcn is None:
cfcn = stats.mean
a = asarray(a)
if keepcols == []:
avgcol = colex(a, collapsecols)
means = cfcn(avgcol)
return means
else:
if type(keepcols) not in [ListType, TupleType, np.ndarray]:
keepcols = [keepcols]
values = colex(a, keepcols) # so that "item" can be appended (below)
uniques = unique(values) # get a LIST, so .sort keeps rows intact
uniques.sort()
newlist = []
for item in uniques:
if type(item) not in [ListType, TupleType, np.ndarray]:
item = [item]
tmprows = linexand(a, keepcols, item)
for col in collapsecols:
avgcol = colex(tmprows, col)
item.append(cfcn(avgcol))
if stderr:
if len(avgcol) > 1:
item.append(stats.stderr(avgcol))
else:
item.append('N/A')
if ns:
item.append(len(avgcol))
newlist.append(item)
try:
new_a = np.array(newlist)
except TypeError:
new_a = np.array(newlist, 'O')
return new_a
def collapse(a, keepcols, collapsecols, stderr=0, ns=0, cfcn=None):
"""Averages data in collapsecol, keeping all unique items in keepcols
(using unique, which keeps unique LISTS of column numbers), retaining
the unique sets of values in keepcols, the mean for each. If the sterr or
N of the mean are desired, set either or both parameters to 1.
Returns: unique 'conditions' specified by the contents of columns specified
by keepcols, abutted with the mean(s,axis=0) of column(s) specified by
collapsecols
"""
if cfcn is None:
cfcn = stats.mean
a = asarray(a)
if keepcols == []:
avgcol = colex(a,collapsecols)
means = cfcn(avgcol)
return means
else:
if type(keepcols) not in [ListType,TupleType,np.ndarray]:
keepcols = [keepcols]
values = colex(a,keepcols) # so that "item" can be appended (below)
uniques = unique(values) # get a LIST, so .sort keeps rows intact
uniques.sort()
newlist = []
for item in uniques:
if type(item) not in [ListType,TupleType,np.ndarray]:
item =[item]
tmprows = linexand(a,keepcols,item)
for col in collapsecols:
avgcol = colex(tmprows,col)
item.append(cfcn(avgcol))
if stderr:
if len(avgcol)>1:
item.append(stats.stderr(avgcol))
else:
item.append('N/A')
if ns:
item.append(len(avgcol))
newlist.append(item)
try:
new_a = np.array(newlist)
except TypeError:
new_a = np.array(newlist,'O')
return new_a
def collapse(a, keepcols, collapsecols, stderr=0, ns=0, cfcn=None):
"""Averages data in collapsecol, keeping all unique items in keepcols
(using unique, which keeps unique LISTS of column numbers), retaining
the unique sets of values in keepcols, the mean for each. If the sterr or
N of the mean are desired, set either or both parameters to 1.
Returns: unique 'conditions' specified by the contents of columns specified
by keepcols, abutted with the mean(s,axis=0) of column(s) specified by
collapsecols
Examples
--------
import numpy as np
from scipy import stats
xx = np.array([[ 0., 0., 1.],
[ 1., 1., 1.],
[ 2., 2., 1.],
[ 0., 3., 1.],
[ 1., 4., 1.],
[ 2., 5., 1.],
[ 0., 6., 1.],
[ 1., 7., 1.],
[ 2., 8., 1.],
[ 0., 9., 1.]])
>>> stats._support.collapse(xx, (0), (1,2), stderr=0, ns=0, cfcn=None)
array([[ 0. , 4.5, 1. ],
[ 0. , 4.5, 1. ],
[ 1. , 4. , 1. ],
[ 1. , 4. , 1. ],
[ 2. , 5. , 1. ],
[ 2. , 5. , 1. ]])
>>> stats._support.collapse(xx, (0), (1,2), stderr=1, ns=1, cfcn=None)
array([[ 0. , 4.5 , 1.93649167, 4. , 1. ,
0. , 4. ],
[ 0. , 4.5 , 1.93649167, 4. , 1. ,
0. , 4. ],
[ 1. , 4. , 1.73205081, 3. , 1. ,
0. , 3. ],
[ 1. , 4. , 1.73205081, 3. , 1. ,
0. , 3. ],
[ 2. , 5. , 1.73205081, 3. , 1. ,
0. , 3. ],
[ 2. , 5. , 1.73205081, 3. , 1. ,
0. , 3. ]])
"""
if cfcn is None:
cfcn = lambda (x): np.mean(x, axis=0)
a = asarray(a)
if keepcols == []:
avgcol = colex(a, collapsecols)
means = cfcn(avgcol)
return means
else:
if type(keepcols) not in [ListType, TupleType, np.ndarray]:
keepcols = [keepcols]
values = colex(a, keepcols) # so that "item" can be appended (below)
uniques = unique(values).tolist() # get a LIST, so .sort keeps rows intact
uniques.sort()
newlist = []
for item in uniques:
if type(item) not in [ListType, TupleType, np.ndarray]:
item = [item]
tmprows = linexand(a, keepcols, item)
for col in collapsecols:
avgcol = colex(tmprows, col)
item.append(cfcn(avgcol))
if stderr:
if len(avgcol) > 1:
item.append(stats.stderr(avgcol))
else:
item.append("N/A")
if ns:
item.append(len(avgcol))
newlist.append(item)
try:
new_a = np.array(newlist)
except TypeError:
new_a = np.array(newlist, "O")
return new_a
def write_travel_times(fileName="output.csv"):
outputFile = open(fileName, "w")
result = get_rides_fastest()
print "**** Writing results to file ****"
length = len(result.keys())
for i, key in enumerate(result.keys()):
outputFile.write(str(key[0]) + ";" + str(key[1]) + ",")
outputFile.write("%.02f" % avg(result[key]) + "," + "%.02f" % stddev(result[key]) + "," + "%.02f" % stderr(result[key]) + "," + str(len(result[key])))
# outputFile.write(",")
# outputFile.write(",".join([str(element) for element in result[key]]))
outputFile.write("\n")
if i % 400000 == 0:
progress = 100.0*i/length
if progress > 105:
break
if i > 0:
sys.stdout.write('\r')
sys.stdout.write("Progress: " + "%.01f" % progress + "%" + " completed.")
sys.stdout.flush()
sys.stdout.write("\rProgress: " + "%.01f" % 100.0 + "%" + " completed.\n")
sys.stdout.flush()
outputFile.close()
return None
def collapse(a, keepcols, collapsecols, stderr=0, ns=0, cfcn=None):
"""Averages data in collapsecol, keeping all unique items in keepcols
(using unique, which keeps unique LISTS of column numbers), retaining
the unique sets of values in keepcols, the mean for each. If the sterr or
N of the mean are desired, set either or both parameters to 1.
Returns: unique 'conditions' specified by the contents of columns specified
by keepcols, abutted with the mean(s,axis=0) of column(s) specified by
collapsecols
Examples
--------
import numpy as np
from scipy import stats
xx = np.array([[ 0., 0., 1.],
[ 1., 1., 1.],
[ 2., 2., 1.],
[ 0., 3., 1.],
[ 1., 4., 1.],
[ 2., 5., 1.],
[ 0., 6., 1.],
[ 1., 7., 1.],
[ 2., 8., 1.],
[ 0., 9., 1.]])
>>> stats._support.collapse(xx, (0), (1,2), stderr=0, ns=0, cfcn=None)
array([[ 0. , 4.5, 1. ],
[ 0. , 4.5, 1. ],
[ 1. , 4. , 1. ],
[ 1. , 4. , 1. ],
[ 2. , 5. , 1. ],
[ 2. , 5. , 1. ]])
>>> stats._support.collapse(xx, (0), (1,2), stderr=1, ns=1, cfcn=None)
array([[ 0. , 4.5 , 1.93649167, 4. , 1. ,
0. , 4. ],
[ 0. , 4.5 , 1.93649167, 4. , 1. ,
0. , 4. ],
[ 1. , 4. , 1.73205081, 3. , 1. ,
0. , 3. ],
[ 1. , 4. , 1.73205081, 3. , 1. ,
0. , 3. ],
[ 2. , 5. , 1.73205081, 3. , 1. ,
0. , 3. ],
[ 2. , 5. , 1.73205081, 3. , 1. ,
0. , 3. ]])
"""
if cfcn is None:
cfcn = lambda (x): np.mean(x, axis=0)
a = asarray(a)
if keepcols == []:
avgcol = colex(a, collapsecols)
means = cfcn(avgcol)
return means
else:
if type(keepcols) not in [ListType, TupleType, np.ndarray]:
keepcols = [keepcols]
values = colex(a, keepcols) # so that "item" can be appended (below)
uniques = unique(
values).tolist() # get a LIST, so .sort keeps rows intact
uniques.sort()
newlist = []
for item in uniques:
if type(item) not in [ListType, TupleType, np.ndarray]:
item = [item]
tmprows = linexand(a, keepcols, item)
for col in collapsecols:
avgcol = colex(tmprows, col)
item.append(cfcn(avgcol))
if stderr:
if len(avgcol) > 1:
item.append(stats.stderr(avgcol))
else:
item.append('N/A')
if ns:
item.append(len(avgcol))
newlist.append(item)
try:
new_a = np.array(newlist)
except TypeError:
new_a = np.array(newlist, 'O')
return new_a