prompt
large_stringlengths 72
9.34k
| completion
large_stringlengths 0
7.61k
|
|---|---|
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
<|fim_middle|>
else:
print('N_PEAKS = %d!?' % npeaks)
sys.exit(1)
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
if read:
break
else:
read = 1
if read == 1:
ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
labels.append(labelnicks.get(label, label))
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs)
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
mybpz.add('stel', mycat.stel)
elif 'stellarity' in mycat.labels:
mybpz.add('stel', mycat.stellarity)
if 'maxsigisoaper' in mycat.labels:
mybpz.add('sig', mycat.maxsigisoaper)
if 'sig' in mycat.labels:
mybpz.assign('sig', mycat.maxsigisoaper)
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
if 'zspec' in mycat.labels:
mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
mybpz.add('zqual', mycat.zqual)
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq') |
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq')
else:
<|fim_middle|>
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
if read:
break
else:
read = 1
if read == 1:
ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
labels.append(labelnicks.get(label, label))
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs)
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
mybpz.add('stel', mycat.stel)
elif 'stellarity' in mycat.labels:
mybpz.add('stel', mycat.stellarity)
if 'maxsigisoaper' in mycat.labels:
mybpz.add('sig', mycat.maxsigisoaper)
if 'sig' in mycat.labels:
mybpz.assign('sig', mycat.maxsigisoaper)
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
if 'zspec' in mycat.labels:
mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
mybpz.add('zqual', mycat.zqual)
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | print('N_PEAKS = %d!?' % npeaks)
sys.exit(1) |
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq')
else:
print('N_PEAKS = %d!?' % npeaks)
sys.exit(1)
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
<|fim_middle|>
else:
read = 1
if read == 1:
ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
labels.append(labelnicks.get(label, label))
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs)
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
mybpz.add('stel', mycat.stel)
elif 'stellarity' in mycat.labels:
mybpz.add('stel', mycat.stellarity)
if 'maxsigisoaper' in mycat.labels:
mybpz.add('sig', mycat.maxsigisoaper)
if 'sig' in mycat.labels:
mybpz.assign('sig', mycat.maxsigisoaper)
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
if 'zspec' in mycat.labels:
mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
mybpz.add('zqual', mycat.zqual)
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | if read:
break |
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq')
else:
print('N_PEAKS = %d!?' % npeaks)
sys.exit(1)
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
if read:
<|fim_middle|>
else:
read = 1
if read == 1:
ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
labels.append(labelnicks.get(label, label))
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs)
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
mybpz.add('stel', mycat.stel)
elif 'stellarity' in mycat.labels:
mybpz.add('stel', mycat.stellarity)
if 'maxsigisoaper' in mycat.labels:
mybpz.add('sig', mycat.maxsigisoaper)
if 'sig' in mycat.labels:
mybpz.assign('sig', mycat.maxsigisoaper)
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
if 'zspec' in mycat.labels:
mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
mybpz.add('zqual', mycat.zqual)
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | break |
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq')
else:
print('N_PEAKS = %d!?' % npeaks)
sys.exit(1)
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
if read:
break
else:
<|fim_middle|>
if read == 1:
ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
labels.append(labelnicks.get(label, label))
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs)
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
mybpz.add('stel', mycat.stel)
elif 'stellarity' in mycat.labels:
mybpz.add('stel', mycat.stellarity)
if 'maxsigisoaper' in mycat.labels:
mybpz.add('sig', mycat.maxsigisoaper)
if 'sig' in mycat.labels:
mybpz.assign('sig', mycat.maxsigisoaper)
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
if 'zspec' in mycat.labels:
mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
mybpz.add('zqual', mycat.zqual)
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | read = 1 |
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq')
else:
print('N_PEAKS = %d!?' % npeaks)
sys.exit(1)
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
if read:
break
else:
read = 1
if read == 1:
<|fim_middle|>
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs)
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
mybpz.add('stel', mycat.stel)
elif 'stellarity' in mycat.labels:
mybpz.add('stel', mycat.stellarity)
if 'maxsigisoaper' in mycat.labels:
mybpz.add('sig', mycat.maxsigisoaper)
if 'sig' in mycat.labels:
mybpz.assign('sig', mycat.maxsigisoaper)
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
if 'zspec' in mycat.labels:
mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
mybpz.add('zqual', mycat.zqual)
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
labels.append(labelnicks.get(label, label)) |
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq')
else:
print('N_PEAKS = %d!?' % npeaks)
sys.exit(1)
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
if read:
break
else:
read = 1
if read == 1:
ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
<|fim_middle|>
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs)
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
mybpz.add('stel', mycat.stel)
elif 'stellarity' in mycat.labels:
mybpz.add('stel', mycat.stellarity)
if 'maxsigisoaper' in mycat.labels:
mybpz.add('sig', mycat.maxsigisoaper)
if 'sig' in mycat.labels:
mybpz.assign('sig', mycat.maxsigisoaper)
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
if 'zspec' in mycat.labels:
mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
mybpz.add('zqual', mycat.zqual)
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | labels.append(labelnicks.get(label, label)) |
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq')
else:
print('N_PEAKS = %d!?' % npeaks)
sys.exit(1)
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
if read:
break
else:
read = 1
if read == 1:
ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
labels.append(labelnicks.get(label, label))
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
<|fim_middle|>
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
mybpz.add('stel', mycat.stel)
elif 'stellarity' in mycat.labels:
mybpz.add('stel', mycat.stellarity)
if 'maxsigisoaper' in mycat.labels:
mybpz.add('sig', mycat.maxsigisoaper)
if 'sig' in mycat.labels:
mybpz.assign('sig', mycat.maxsigisoaper)
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
if 'zspec' in mycat.labels:
mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
mybpz.add('zqual', mycat.zqual)
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs) |
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq')
else:
print('N_PEAKS = %d!?' % npeaks)
sys.exit(1)
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
if read:
break
else:
read = 1
if read == 1:
ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
labels.append(labelnicks.get(label, label))
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs)
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
<|fim_middle|>
elif 'stellarity' in mycat.labels:
mybpz.add('stel', mycat.stellarity)
if 'maxsigisoaper' in mycat.labels:
mybpz.add('sig', mycat.maxsigisoaper)
if 'sig' in mycat.labels:
mybpz.assign('sig', mycat.maxsigisoaper)
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
if 'zspec' in mycat.labels:
mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
mybpz.add('zqual', mycat.zqual)
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | mybpz.add('stel', mycat.stel) |
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq')
else:
print('N_PEAKS = %d!?' % npeaks)
sys.exit(1)
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
if read:
break
else:
read = 1
if read == 1:
ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
labels.append(labelnicks.get(label, label))
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs)
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
mybpz.add('stel', mycat.stel)
elif 'stellarity' in mycat.labels:
<|fim_middle|>
if 'maxsigisoaper' in mycat.labels:
mybpz.add('sig', mycat.maxsigisoaper)
if 'sig' in mycat.labels:
mybpz.assign('sig', mycat.maxsigisoaper)
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
if 'zspec' in mycat.labels:
mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
mybpz.add('zqual', mycat.zqual)
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | mybpz.add('stel', mycat.stellarity) |
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq')
else:
print('N_PEAKS = %d!?' % npeaks)
sys.exit(1)
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
if read:
break
else:
read = 1
if read == 1:
ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
labels.append(labelnicks.get(label, label))
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs)
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
mybpz.add('stel', mycat.stel)
elif 'stellarity' in mycat.labels:
mybpz.add('stel', mycat.stellarity)
if 'maxsigisoaper' in mycat.labels:
<|fim_middle|>
if 'sig' in mycat.labels:
mybpz.assign('sig', mycat.maxsigisoaper)
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
if 'zspec' in mycat.labels:
mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
mybpz.add('zqual', mycat.zqual)
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | mybpz.add('sig', mycat.maxsigisoaper) |
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq')
else:
print('N_PEAKS = %d!?' % npeaks)
sys.exit(1)
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
if read:
break
else:
read = 1
if read == 1:
ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
labels.append(labelnicks.get(label, label))
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs)
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
mybpz.add('stel', mycat.stel)
elif 'stellarity' in mycat.labels:
mybpz.add('stel', mycat.stellarity)
if 'maxsigisoaper' in mycat.labels:
mybpz.add('sig', mycat.maxsigisoaper)
if 'sig' in mycat.labels:
<|fim_middle|>
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
if 'zspec' in mycat.labels:
mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
mybpz.add('zqual', mycat.zqual)
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | mybpz.assign('sig', mycat.maxsigisoaper) |
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq')
else:
print('N_PEAKS = %d!?' % npeaks)
sys.exit(1)
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
if read:
break
else:
read = 1
if read == 1:
ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
labels.append(labelnicks.get(label, label))
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs)
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
mybpz.add('stel', mycat.stel)
elif 'stellarity' in mycat.labels:
mybpz.add('stel', mycat.stellarity)
if 'maxsigisoaper' in mycat.labels:
mybpz.add('sig', mycat.maxsigisoaper)
if 'sig' in mycat.labels:
mybpz.assign('sig', mycat.maxsigisoaper)
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
<|fim_middle|>
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | if 'zspec' in mycat.labels:
mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
mybpz.add('zqual', mycat.zqual) |
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq')
else:
print('N_PEAKS = %d!?' % npeaks)
sys.exit(1)
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
if read:
break
else:
read = 1
if read == 1:
ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
labels.append(labelnicks.get(label, label))
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs)
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
mybpz.add('stel', mycat.stel)
elif 'stellarity' in mycat.labels:
mybpz.add('stel', mycat.stellarity)
if 'maxsigisoaper' in mycat.labels:
mybpz.add('sig', mycat.maxsigisoaper)
if 'sig' in mycat.labels:
mybpz.assign('sig', mycat.maxsigisoaper)
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
if 'zspec' in mycat.labels:
<|fim_middle|>
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
mybpz.add('zqual', mycat.zqual) |
<|file_name|>bpzfinalize.py<|end_file_name|><｜fim▁begin｜>from __future__ import print_function
from __future__ import division
# python bpzchisq2run.py ACS-Subaru
# PRODUCES ACS-Subaru_bpz.cat
# ADDS A FEW THINGS TO THE BPZ CATALOG
# INCLUDING chisq2 AND LABEL HEADERS
# ~/p/bpzchisq2run.py NOW INCLUDED!
# ~/Tonetti/colorpro/bpzfinalize7a.py
# ~/UDF/Elmegreen/phot8/bpzfinalize7.py
# ~/UDF/bpzfinalize7a.py, 7, 5, 4, 23_djh, 23, 3
# NOW TAKING BPZ OUTPUT w/ 3 REDSHIFT PEAKS
# ALSO USING NEW i-band CATALOG istel.cat -- w/ CORRECT IDs
# python bpzfinalize.py bvizjh_cut_sexseg2_allobjs_newres_offset3_djh_Burst_1M
from builtins import range
from past.utils import old_div
from coetools import *
sum = add.reduce # Just to make sure
##################
# add nf, jhgood, stellarity, x, y
inbpz = capfile(sys.argv[1], 'bpz')
inroot = inbpz[:-4]
infile = loadfile(inbpz)
for line in infile:
if line[:7] == '##INPUT':
incat = line[8:]
break
for line in infile:
if line[:9] == '##N_PEAKS':
npeaks = string.atoi(line[10])
break
#inchisq2 = inroot + '.chisq2'
#outbpz = inroot + '_b.bpz'
outbpz = inroot + '_bpz.cat'
if npeaks == 1:
labels = string.split(
'id zb zbmin zbmax tb odds zml tml chisq')
elif npeaks == 3:
labels = string.split(
'id zb zbmin zbmax tb odds zb2 zb2min zb2max tb2 odds2 zb3 zb3min zb3max tb3 odds3 zml tml chisq')
else:
print('N_PEAKS = %d!?' % npeaks)
sys.exit(1)
labelnicks = {'Z_S': 'zspec', 'M_0': 'M0'}
read = 0
ilabel = 0
for iline in range(len(infile)):
line = infile[iline]
if line[:2] == '##':
if read:
break
else:
read = 1
if read == 1:
ilabel += 1
label = string.split(line)[-1]
if ilabel >= 10:
labels.append(labelnicks.get(label, label))
mybpz = loadvarswithclass(inbpz, labels=labels)
mycat = loadvarswithclass(incat)
#icat = loadvarswithclass('/home/coe/UDF/istel.cat')
#icat = icat.takeids(mycat.id)
#bpzchisq2 = loadvarswithclass(inchisq2)
#################################
# CHISQ2, nfdet, nfobs
if os.path.exists(inroot + '.flux_comparison'):
data = loaddata(inroot + '.flux_comparison+')
#nf = 6
nf = old_div((len(data) - 5), 3)
# id M0 zb tb*3
id = data[0]
ft = data[5:5 + nf] # FLUX (from spectrum for that TYPE)
fo = data[5 + nf:5 + 2 * nf] # FLUX (OBSERVED)
efo = data[5 + 2 * nf:5 + 3 * nf] # FLUX_ERROR (OBSERVED)
# chisq 2
eft = old_div(ft, 15.)
eft = max(eft) # for each galaxy, take max eft among filters
ef = sqrt(efo**2 + eft**2) # (6, 18981) + (18981) done correctly
dfosq = (old_div((ft - fo), ef))**2
dfosqsum = sum(dfosq)
detected = greater(fo, 0)
nfdet = sum(detected)
observed = less(efo, 1)
nfobs = sum(observed)
# DEGREES OF FREEDOM
dof = clip2(nfobs - 3., 1, None) # 3 params (z, t, a)
chisq2clip = old_div(dfosqsum, dof)
sedfrac = divsafe(max(fo - efo), max(ft), -1) # SEDzero
chisq2 = chisq2clip[:]
chisq2 = where(less(sedfrac, 1e-10), 900., chisq2)
chisq2 = where(equal(nfobs, 1), 990., chisq2)
chisq2 = where(equal(nfobs, 0), 999., chisq2)
#################################
#print 'BPZ tb N_PEAKS BUG FIX'
#mybpz.tb = mybpz.tb + 0.667
#mybpz.tb2 = where(greater(mybpz.tb2, 0), mybpz.tb2 + 0.667, -1.)
#mybpz.tb3 = where(greater(mybpz.tb3, 0), mybpz.tb3 + 0.667, -1.)
mybpz.add('chisq2', chisq2)
mybpz.add('nfdet', nfdet)
mybpz.add('nfobs', nfobs)
#mybpz.add('jhgood', jhgood)
if 'stel' in mycat.labels:
mybpz.add('stel', mycat.stel)
elif 'stellarity' in mycat.labels:
mybpz.add('stel', mycat.stellarity)
if 'maxsigisoaper' in mycat.labels:
mybpz.add('sig', mycat.maxsigisoaper)
if 'sig' in mycat.labels:
mybpz.assign('sig', mycat.maxsigisoaper)
#mybpz.add('x', mycat.x)
#mybpz.add('y', mycat.y)
if 'zspec' not in mybpz.labels:
if 'zspec' in mycat.labels:
mybpz.add('zspec', mycat.zspec)
print(mycat.zspec)
if 'zqual' in mycat.labels:
<|fim_middle|>
print(mybpz.labels)
mybpz.save(outbpz, maxy=None)
##################
# det
# 0 < mag < 99
# dmag > 0
# fo > 0
# efo -> 1.6e-8, e.g.
# undet
# mag = 99
# dmag = -m_1sigma
# fo = 0
# efo = 0 -> 5e13, e.g.
# unobs
# mag = -99
# dmag = 0
# fo = 0
# efo = inf (1e108)
## # original chisq usually matches this:
## dfosq = ((ft - fo) / efo) ** 2
## dfosqsum = sum(dfosq)
## observed = less(efo, 1)
## nfobs = sum(observed)
## chisq = dfosqsum / (nfobs - 1.)
<｜fim▁end｜> | mybpz.add('zqual', mycat.zqual) |
<|file_name|>annotate_trees.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
import sys
import os
from treestore import Treestore
try: taxonomy = sys.argv[1]
except: taxonomy = None
<｜fim▁hole｜>t = Treestore()
treebase_uri = 'http://purl.org/phylo/treebase/phylows/tree/%s'
tree_files = [x for x in os.listdir('trees') if x.endswith('.nex')]
base_uri = 'http://www.phylocommons.org/trees/%s'
tree_list = set(t.list_trees())
for tree_uri in tree_list:
if not 'TB2_' in tree_uri: continue
tree_id = t.id_from_uri(tree_uri)
tb_uri = treebase_uri % (tree_id.replace('_', ':'))
print tree_id, tb_uri
t.annotate(tree_uri, annotations='?tree bibo:cites <%s> .' % tb_uri)<｜fim▁end｜> | |
<|file_name|>annotate_trees.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
import sys
import os
from treestore import Treestore
try: taxonomy = sys.argv[1]
except: taxonomy = None
t = Treestore()
treebase_uri = 'http://purl.org/phylo/treebase/phylows/tree/%s'
tree_files = [x for x in os.listdir('trees') if x.endswith('.nex')]
base_uri = 'http://www.phylocommons.org/trees/%s'
tree_list = set(t.list_trees())
for tree_uri in tree_list:
if not 'TB2_' in tree_uri: <|fim_middle|>
tree_id = t.id_from_uri(tree_uri)
tb_uri = treebase_uri % (tree_id.replace('_', ':'))
print tree_id, tb_uri
t.annotate(tree_uri, annotations='?tree bibo:cites <%s> .' % tb_uri)
<｜fim▁end｜> | continue |
<|file_name|>__main__.py<|end_file_name|><｜fim▁begin｜>import ast
import heisenberg.library.heisenberg_dynamics_context
import heisenberg.library.orbit_plot
import heisenberg.option_parser
import heisenberg.plot
import heisenberg.util
import matplotlib
import numpy as np
import sys
# https://github.com/matplotlib/matplotlib/issues/5907 says this should fix "Exceeded cell block limit" problems
matplotlib.rcParams['agg.path.chunksize'] = 10000
dynamics_context = heisenberg.library.heisenberg_dynamics_context.Numeric()
op = heisenberg.option_parser.OptionParser(module=heisenberg.plot)
# Add the subprogram-specific options here.
op.add_option(
'--initial-preimage',
dest='initial_preimage',
type='string',
help='Specifies the preimage of the initial conditions with respect to the embedding map specified by the --embedding-dimension and --embedding-solution-sheet-index option values. Should have the form [x_1,...,x_n], where n is the embedding dimension and x_i is a floating point literal for each i.'
)
op.add_option(
'--initial',
dest='initial',
type='string',
help='Specifies the initial conditions [x,y,z,p_x,p_y,p_z], where each of x,y,z,p_x,p_y,p_z are floating point literals.'
)
op.add_option(
'--optimization-iterations',
dest='optimization_iterations',
default=1000,
type='int',
help='Specifies the number of iterations to run the optimization for (if applicable). Default is 1000.'
)
op.add_option(
'--optimize-initial',
dest='optimize_initial',
action='store_true',
default=False,
help='Indicates that the specified initial condition (via whichever of the --initial... options) should be used as the starting point for an optimization to attempt to close the orbit. Default value is False.'
)
op.add_option(
'--output-dir',
dest='output_dir',
default='.',
help='Specifies the directory to write plot images and data files to. Default is current directory.'
)
op.add_option(
'--disable-plot-initial',
dest='disable_plot_initial',
action='store_true',
default=False,
help='Disables plotting the initial curve; only has effect if --optimize-initial is specified.'
)
options,args = op.parse_argv_and_validate()
if options is None:
sys.exit(-1)
num_initial_conditions_specified = sum([
options.initial_preimage is not None,
options.initial is not None,
])
if num_initial_conditions_specified != 1:
print('Some initial condition option must be specified; --initial-preimage, --initial. However, {0} of those were specified.'.format(num_initial_conditions_specified))
op.print_help()
sys.exit(-1)
# Validate subprogram-specific options here.
# Attempt to parse initial conditions. Upon success, the attribute options.qp_0 should exist.
if options.initial_preimage is not None:
try:
options.initial_preimage = np.array(ast.literal_eval(options.initial_preimage))
expected_shape = (options.embedding_dimension,)
if options.initial_preimage.shape != expected_shape:
raise ValueError('--initial-preimage value had the wrong number of components (got {0} but expected {1}).'.format(options.initial_preimage.shape, expected_shape))
options.qp_0 = dynamics_context.embedding(N=options.embedding_dimension, sheet_index=options.embedding_solution_sheet_index)(options.initial_preimage)
except Exception as e:
print('error parsing --initial-preimage value; error was {0}'.format(e))
op.print_help()
sys.exit(-1)
elif options.initial is not None:
try:
options.initial = heisenberg.util.csv_as_ndarray(heisenberg.util.pop_brackets_off_of(options.initial), float)
expected_shape = (6,)
if options.initial.shape != expected_shape:
raise ValueError('--initial value had the wrong number of components (got {0} but expected {1}).'.format(options.initial.shape, expected_shape))
options.qp_0 = options.initial.reshape(2,3)
except ValueError as e:
print('error parsing --initial value: {0}'.format(str(e)))<｜fim▁hole｜> assert False, 'this should never happen because of the check with num_initial_conditions_specified'
rng = np.random.RandomState(options.seed)
heisenberg.plot.plot(dynamics_context, options, rng=rng)<｜fim▁end｜> | op.print_help()
sys.exit(-1)
else: |
<|file_name|>__main__.py<|end_file_name|><｜fim▁begin｜>import ast
import heisenberg.library.heisenberg_dynamics_context
import heisenberg.library.orbit_plot
import heisenberg.option_parser
import heisenberg.plot
import heisenberg.util
import matplotlib
import numpy as np
import sys
# https://github.com/matplotlib/matplotlib/issues/5907 says this should fix "Exceeded cell block limit" problems
matplotlib.rcParams['agg.path.chunksize'] = 10000
dynamics_context = heisenberg.library.heisenberg_dynamics_context.Numeric()
op = heisenberg.option_parser.OptionParser(module=heisenberg.plot)
# Add the subprogram-specific options here.
op.add_option(
'--initial-preimage',
dest='initial_preimage',
type='string',
help='Specifies the preimage of the initial conditions with respect to the embedding map specified by the --embedding-dimension and --embedding-solution-sheet-index option values. Should have the form [x_1,...,x_n], where n is the embedding dimension and x_i is a floating point literal for each i.'
)
op.add_option(
'--initial',
dest='initial',
type='string',
help='Specifies the initial conditions [x,y,z,p_x,p_y,p_z], where each of x,y,z,p_x,p_y,p_z are floating point literals.'
)
op.add_option(
'--optimization-iterations',
dest='optimization_iterations',
default=1000,
type='int',
help='Specifies the number of iterations to run the optimization for (if applicable). Default is 1000.'
)
op.add_option(
'--optimize-initial',
dest='optimize_initial',
action='store_true',
default=False,
help='Indicates that the specified initial condition (via whichever of the --initial... options) should be used as the starting point for an optimization to attempt to close the orbit. Default value is False.'
)
op.add_option(
'--output-dir',
dest='output_dir',
default='.',
help='Specifies the directory to write plot images and data files to. Default is current directory.'
)
op.add_option(
'--disable-plot-initial',
dest='disable_plot_initial',
action='store_true',
default=False,
help='Disables plotting the initial curve; only has effect if --optimize-initial is specified.'
)
options,args = op.parse_argv_and_validate()
if options is None:
<|fim_middle|>
num_initial_conditions_specified = sum([
options.initial_preimage is not None,
options.initial is not None,
])
if num_initial_conditions_specified != 1:
print('Some initial condition option must be specified; --initial-preimage, --initial. However, {0} of those were specified.'.format(num_initial_conditions_specified))
op.print_help()
sys.exit(-1)
# Validate subprogram-specific options here.
# Attempt to parse initial conditions. Upon success, the attribute options.qp_0 should exist.
if options.initial_preimage is not None:
try:
options.initial_preimage = np.array(ast.literal_eval(options.initial_preimage))
expected_shape = (options.embedding_dimension,)
if options.initial_preimage.shape != expected_shape:
raise ValueError('--initial-preimage value had the wrong number of components (got {0} but expected {1}).'.format(options.initial_preimage.shape, expected_shape))
options.qp_0 = dynamics_context.embedding(N=options.embedding_dimension, sheet_index=options.embedding_solution_sheet_index)(options.initial_preimage)
except Exception as e:
print('error parsing --initial-preimage value; error was {0}'.format(e))
op.print_help()
sys.exit(-1)
elif options.initial is not None:
try:
options.initial = heisenberg.util.csv_as_ndarray(heisenberg.util.pop_brackets_off_of(options.initial), float)
expected_shape = (6,)
if options.initial.shape != expected_shape:
raise ValueError('--initial value had the wrong number of components (got {0} but expected {1}).'.format(options.initial.shape, expected_shape))
options.qp_0 = options.initial.reshape(2,3)
except ValueError as e:
print('error parsing --initial value: {0}'.format(str(e)))
op.print_help()
sys.exit(-1)
else:
assert False, 'this should never happen because of the check with num_initial_conditions_specified'
rng = np.random.RandomState(options.seed)
heisenberg.plot.plot(dynamics_context, options, rng=rng)
<｜fim▁end｜> | sys.exit(-1) |
<|file_name|>__main__.py<|end_file_name|><｜fim▁begin｜>import ast
import heisenberg.library.heisenberg_dynamics_context
import heisenberg.library.orbit_plot
import heisenberg.option_parser
import heisenberg.plot
import heisenberg.util
import matplotlib
import numpy as np
import sys
# https://github.com/matplotlib/matplotlib/issues/5907 says this should fix "Exceeded cell block limit" problems
matplotlib.rcParams['agg.path.chunksize'] = 10000
dynamics_context = heisenberg.library.heisenberg_dynamics_context.Numeric()
op = heisenberg.option_parser.OptionParser(module=heisenberg.plot)
# Add the subprogram-specific options here.
op.add_option(
'--initial-preimage',
dest='initial_preimage',
type='string',
help='Specifies the preimage of the initial conditions with respect to the embedding map specified by the --embedding-dimension and --embedding-solution-sheet-index option values. Should have the form [x_1,...,x_n], where n is the embedding dimension and x_i is a floating point literal for each i.'
)
op.add_option(
'--initial',
dest='initial',
type='string',
help='Specifies the initial conditions [x,y,z,p_x,p_y,p_z], where each of x,y,z,p_x,p_y,p_z are floating point literals.'
)
op.add_option(
'--optimization-iterations',
dest='optimization_iterations',
default=1000,
type='int',
help='Specifies the number of iterations to run the optimization for (if applicable). Default is 1000.'
)
op.add_option(
'--optimize-initial',
dest='optimize_initial',
action='store_true',
default=False,
help='Indicates that the specified initial condition (via whichever of the --initial... options) should be used as the starting point for an optimization to attempt to close the orbit. Default value is False.'
)
op.add_option(
'--output-dir',
dest='output_dir',
default='.',
help='Specifies the directory to write plot images and data files to. Default is current directory.'
)
op.add_option(
'--disable-plot-initial',
dest='disable_plot_initial',
action='store_true',
default=False,
help='Disables plotting the initial curve; only has effect if --optimize-initial is specified.'
)
options,args = op.parse_argv_and_validate()
if options is None:
sys.exit(-1)
num_initial_conditions_specified = sum([
options.initial_preimage is not None,
options.initial is not None,
])
if num_initial_conditions_specified != 1:
<|fim_middle|>
# Validate subprogram-specific options here.
# Attempt to parse initial conditions. Upon success, the attribute options.qp_0 should exist.
if options.initial_preimage is not None:
try:
options.initial_preimage = np.array(ast.literal_eval(options.initial_preimage))
expected_shape = (options.embedding_dimension,)
if options.initial_preimage.shape != expected_shape:
raise ValueError('--initial-preimage value had the wrong number of components (got {0} but expected {1}).'.format(options.initial_preimage.shape, expected_shape))
options.qp_0 = dynamics_context.embedding(N=options.embedding_dimension, sheet_index=options.embedding_solution_sheet_index)(options.initial_preimage)
except Exception as e:
print('error parsing --initial-preimage value; error was {0}'.format(e))
op.print_help()
sys.exit(-1)
elif options.initial is not None:
try:
options.initial = heisenberg.util.csv_as_ndarray(heisenberg.util.pop_brackets_off_of(options.initial), float)
expected_shape = (6,)
if options.initial.shape != expected_shape:
raise ValueError('--initial value had the wrong number of components (got {0} but expected {1}).'.format(options.initial.shape, expected_shape))
options.qp_0 = options.initial.reshape(2,3)
except ValueError as e:
print('error parsing --initial value: {0}'.format(str(e)))
op.print_help()
sys.exit(-1)
else:
assert False, 'this should never happen because of the check with num_initial_conditions_specified'
rng = np.random.RandomState(options.seed)
heisenberg.plot.plot(dynamics_context, options, rng=rng)
<｜fim▁end｜> | print('Some initial condition option must be specified; --initial-preimage, --initial. However, {0} of those were specified.'.format(num_initial_conditions_specified))
op.print_help()
sys.exit(-1) |
<|file_name|>__main__.py<|end_file_name|><｜fim▁begin｜>import ast
import heisenberg.library.heisenberg_dynamics_context
import heisenberg.library.orbit_plot
import heisenberg.option_parser
import heisenberg.plot
import heisenberg.util
import matplotlib
import numpy as np
import sys
# https://github.com/matplotlib/matplotlib/issues/5907 says this should fix "Exceeded cell block limit" problems
matplotlib.rcParams['agg.path.chunksize'] = 10000
dynamics_context = heisenberg.library.heisenberg_dynamics_context.Numeric()
op = heisenberg.option_parser.OptionParser(module=heisenberg.plot)
# Add the subprogram-specific options here.
op.add_option(
'--initial-preimage',
dest='initial_preimage',
type='string',
help='Specifies the preimage of the initial conditions with respect to the embedding map specified by the --embedding-dimension and --embedding-solution-sheet-index option values. Should have the form [x_1,...,x_n], where n is the embedding dimension and x_i is a floating point literal for each i.'
)
op.add_option(
'--initial',
dest='initial',
type='string',
help='Specifies the initial conditions [x,y,z,p_x,p_y,p_z], where each of x,y,z,p_x,p_y,p_z are floating point literals.'
)
op.add_option(
'--optimization-iterations',
dest='optimization_iterations',
default=1000,
type='int',
help='Specifies the number of iterations to run the optimization for (if applicable). Default is 1000.'
)
op.add_option(
'--optimize-initial',
dest='optimize_initial',
action='store_true',
default=False,
help='Indicates that the specified initial condition (via whichever of the --initial... options) should be used as the starting point for an optimization to attempt to close the orbit. Default value is False.'
)
op.add_option(
'--output-dir',
dest='output_dir',
default='.',
help='Specifies the directory to write plot images and data files to. Default is current directory.'
)
op.add_option(
'--disable-plot-initial',
dest='disable_plot_initial',
action='store_true',
default=False,
help='Disables plotting the initial curve; only has effect if --optimize-initial is specified.'
)
options,args = op.parse_argv_and_validate()
if options is None:
sys.exit(-1)
num_initial_conditions_specified = sum([
options.initial_preimage is not None,
options.initial is not None,
])
if num_initial_conditions_specified != 1:
print('Some initial condition option must be specified; --initial-preimage, --initial. However, {0} of those were specified.'.format(num_initial_conditions_specified))
op.print_help()
sys.exit(-1)
# Validate subprogram-specific options here.
# Attempt to parse initial conditions. Upon success, the attribute options.qp_0 should exist.
if options.initial_preimage is not None:
<|fim_middle|>
elif options.initial is not None:
try:
options.initial = heisenberg.util.csv_as_ndarray(heisenberg.util.pop_brackets_off_of(options.initial), float)
expected_shape = (6,)
if options.initial.shape != expected_shape:
raise ValueError('--initial value had the wrong number of components (got {0} but expected {1}).'.format(options.initial.shape, expected_shape))
options.qp_0 = options.initial.reshape(2,3)
except ValueError as e:
print('error parsing --initial value: {0}'.format(str(e)))
op.print_help()
sys.exit(-1)
else:
assert False, 'this should never happen because of the check with num_initial_conditions_specified'
rng = np.random.RandomState(options.seed)
heisenberg.plot.plot(dynamics_context, options, rng=rng)
<｜fim▁end｜> | try:
options.initial_preimage = np.array(ast.literal_eval(options.initial_preimage))
expected_shape = (options.embedding_dimension,)
if options.initial_preimage.shape != expected_shape:
raise ValueError('--initial-preimage value had the wrong number of components (got {0} but expected {1}).'.format(options.initial_preimage.shape, expected_shape))
options.qp_0 = dynamics_context.embedding(N=options.embedding_dimension, sheet_index=options.embedding_solution_sheet_index)(options.initial_preimage)
except Exception as e:
print('error parsing --initial-preimage value; error was {0}'.format(e))
op.print_help()
sys.exit(-1) |
<|file_name|>__main__.py<|end_file_name|><｜fim▁begin｜>import ast
import heisenberg.library.heisenberg_dynamics_context
import heisenberg.library.orbit_plot
import heisenberg.option_parser
import heisenberg.plot
import heisenberg.util
import matplotlib
import numpy as np
import sys
# https://github.com/matplotlib/matplotlib/issues/5907 says this should fix "Exceeded cell block limit" problems
matplotlib.rcParams['agg.path.chunksize'] = 10000
dynamics_context = heisenberg.library.heisenberg_dynamics_context.Numeric()
op = heisenberg.option_parser.OptionParser(module=heisenberg.plot)
# Add the subprogram-specific options here.
op.add_option(
'--initial-preimage',
dest='initial_preimage',
type='string',
help='Specifies the preimage of the initial conditions with respect to the embedding map specified by the --embedding-dimension and --embedding-solution-sheet-index option values. Should have the form [x_1,...,x_n], where n is the embedding dimension and x_i is a floating point literal for each i.'
)
op.add_option(
'--initial',
dest='initial',
type='string',
help='Specifies the initial conditions [x,y,z,p_x,p_y,p_z], where each of x,y,z,p_x,p_y,p_z are floating point literals.'
)
op.add_option(
'--optimization-iterations',
dest='optimization_iterations',
default=1000,
type='int',
help='Specifies the number of iterations to run the optimization for (if applicable). Default is 1000.'
)
op.add_option(
'--optimize-initial',
dest='optimize_initial',
action='store_true',
default=False,
help='Indicates that the specified initial condition (via whichever of the --initial... options) should be used as the starting point for an optimization to attempt to close the orbit. Default value is False.'
)
op.add_option(
'--output-dir',
dest='output_dir',
default='.',
help='Specifies the directory to write plot images and data files to. Default is current directory.'
)
op.add_option(
'--disable-plot-initial',
dest='disable_plot_initial',
action='store_true',
default=False,
help='Disables plotting the initial curve; only has effect if --optimize-initial is specified.'
)
options,args = op.parse_argv_and_validate()
if options is None:
sys.exit(-1)
num_initial_conditions_specified = sum([
options.initial_preimage is not None,
options.initial is not None,
])
if num_initial_conditions_specified != 1:
print('Some initial condition option must be specified; --initial-preimage, --initial. However, {0} of those were specified.'.format(num_initial_conditions_specified))
op.print_help()
sys.exit(-1)
# Validate subprogram-specific options here.
# Attempt to parse initial conditions. Upon success, the attribute options.qp_0 should exist.
if options.initial_preimage is not None:
try:
options.initial_preimage = np.array(ast.literal_eval(options.initial_preimage))
expected_shape = (options.embedding_dimension,)
if options.initial_preimage.shape != expected_shape:
<|fim_middle|>
options.qp_0 = dynamics_context.embedding(N=options.embedding_dimension, sheet_index=options.embedding_solution_sheet_index)(options.initial_preimage)
except Exception as e:
print('error parsing --initial-preimage value; error was {0}'.format(e))
op.print_help()
sys.exit(-1)
elif options.initial is not None:
try:
options.initial = heisenberg.util.csv_as_ndarray(heisenberg.util.pop_brackets_off_of(options.initial), float)
expected_shape = (6,)
if options.initial.shape != expected_shape:
raise ValueError('--initial value had the wrong number of components (got {0} but expected {1}).'.format(options.initial.shape, expected_shape))
options.qp_0 = options.initial.reshape(2,3)
except ValueError as e:
print('error parsing --initial value: {0}'.format(str(e)))
op.print_help()
sys.exit(-1)
else:
assert False, 'this should never happen because of the check with num_initial_conditions_specified'
rng = np.random.RandomState(options.seed)
heisenberg.plot.plot(dynamics_context, options, rng=rng)
<｜fim▁end｜> | raise ValueError('--initial-preimage value had the wrong number of components (got {0} but expected {1}).'.format(options.initial_preimage.shape, expected_shape)) |
<|file_name|>__main__.py<|end_file_name|><｜fim▁begin｜>import ast
import heisenberg.library.heisenberg_dynamics_context
import heisenberg.library.orbit_plot
import heisenberg.option_parser
import heisenberg.plot
import heisenberg.util
import matplotlib
import numpy as np
import sys
# https://github.com/matplotlib/matplotlib/issues/5907 says this should fix "Exceeded cell block limit" problems
matplotlib.rcParams['agg.path.chunksize'] = 10000
dynamics_context = heisenberg.library.heisenberg_dynamics_context.Numeric()
op = heisenberg.option_parser.OptionParser(module=heisenberg.plot)
# Add the subprogram-specific options here.
op.add_option(
'--initial-preimage',
dest='initial_preimage',
type='string',
help='Specifies the preimage of the initial conditions with respect to the embedding map specified by the --embedding-dimension and --embedding-solution-sheet-index option values. Should have the form [x_1,...,x_n], where n is the embedding dimension and x_i is a floating point literal for each i.'
)
op.add_option(
'--initial',
dest='initial',
type='string',
help='Specifies the initial conditions [x,y,z,p_x,p_y,p_z], where each of x,y,z,p_x,p_y,p_z are floating point literals.'
)
op.add_option(
'--optimization-iterations',
dest='optimization_iterations',
default=1000,
type='int',
help='Specifies the number of iterations to run the optimization for (if applicable). Default is 1000.'
)
op.add_option(
'--optimize-initial',
dest='optimize_initial',
action='store_true',
default=False,
help='Indicates that the specified initial condition (via whichever of the --initial... options) should be used as the starting point for an optimization to attempt to close the orbit. Default value is False.'
)
op.add_option(
'--output-dir',
dest='output_dir',
default='.',
help='Specifies the directory to write plot images and data files to. Default is current directory.'
)
op.add_option(
'--disable-plot-initial',
dest='disable_plot_initial',
action='store_true',
default=False,
help='Disables plotting the initial curve; only has effect if --optimize-initial is specified.'
)
options,args = op.parse_argv_and_validate()
if options is None:
sys.exit(-1)
num_initial_conditions_specified = sum([
options.initial_preimage is not None,
options.initial is not None,
])
if num_initial_conditions_specified != 1:
print('Some initial condition option must be specified; --initial-preimage, --initial. However, {0} of those were specified.'.format(num_initial_conditions_specified))
op.print_help()
sys.exit(-1)
# Validate subprogram-specific options here.
# Attempt to parse initial conditions. Upon success, the attribute options.qp_0 should exist.
if options.initial_preimage is not None:
try:
options.initial_preimage = np.array(ast.literal_eval(options.initial_preimage))
expected_shape = (options.embedding_dimension,)
if options.initial_preimage.shape != expected_shape:
raise ValueError('--initial-preimage value had the wrong number of components (got {0} but expected {1}).'.format(options.initial_preimage.shape, expected_shape))
options.qp_0 = dynamics_context.embedding(N=options.embedding_dimension, sheet_index=options.embedding_solution_sheet_index)(options.initial_preimage)
except Exception as e:
print('error parsing --initial-preimage value; error was {0}'.format(e))
op.print_help()
sys.exit(-1)
elif options.initial is not None:
<|fim_middle|>
else:
assert False, 'this should never happen because of the check with num_initial_conditions_specified'
rng = np.random.RandomState(options.seed)
heisenberg.plot.plot(dynamics_context, options, rng=rng)
<｜fim▁end｜> | try:
options.initial = heisenberg.util.csv_as_ndarray(heisenberg.util.pop_brackets_off_of(options.initial), float)
expected_shape = (6,)
if options.initial.shape != expected_shape:
raise ValueError('--initial value had the wrong number of components (got {0} but expected {1}).'.format(options.initial.shape, expected_shape))
options.qp_0 = options.initial.reshape(2,3)
except ValueError as e:
print('error parsing --initial value: {0}'.format(str(e)))
op.print_help()
sys.exit(-1) |
<|file_name|>__main__.py<|end_file_name|><｜fim▁begin｜>import ast
import heisenberg.library.heisenberg_dynamics_context
import heisenberg.library.orbit_plot
import heisenberg.option_parser
import heisenberg.plot
import heisenberg.util
import matplotlib
import numpy as np
import sys
# https://github.com/matplotlib/matplotlib/issues/5907 says this should fix "Exceeded cell block limit" problems
matplotlib.rcParams['agg.path.chunksize'] = 10000
dynamics_context = heisenberg.library.heisenberg_dynamics_context.Numeric()
op = heisenberg.option_parser.OptionParser(module=heisenberg.plot)
# Add the subprogram-specific options here.
op.add_option(
'--initial-preimage',
dest='initial_preimage',
type='string',
help='Specifies the preimage of the initial conditions with respect to the embedding map specified by the --embedding-dimension and --embedding-solution-sheet-index option values. Should have the form [x_1,...,x_n], where n is the embedding dimension and x_i is a floating point literal for each i.'
)
op.add_option(
'--initial',
dest='initial',
type='string',
help='Specifies the initial conditions [x,y,z,p_x,p_y,p_z], where each of x,y,z,p_x,p_y,p_z are floating point literals.'
)
op.add_option(
'--optimization-iterations',
dest='optimization_iterations',
default=1000,
type='int',
help='Specifies the number of iterations to run the optimization for (if applicable). Default is 1000.'
)
op.add_option(
'--optimize-initial',
dest='optimize_initial',
action='store_true',
default=False,
help='Indicates that the specified initial condition (via whichever of the --initial... options) should be used as the starting point for an optimization to attempt to close the orbit. Default value is False.'
)
op.add_option(
'--output-dir',
dest='output_dir',
default='.',
help='Specifies the directory to write plot images and data files to. Default is current directory.'
)
op.add_option(
'--disable-plot-initial',
dest='disable_plot_initial',
action='store_true',
default=False,
help='Disables plotting the initial curve; only has effect if --optimize-initial is specified.'
)
options,args = op.parse_argv_and_validate()
if options is None:
sys.exit(-1)
num_initial_conditions_specified = sum([
options.initial_preimage is not None,
options.initial is not None,
])
if num_initial_conditions_specified != 1:
print('Some initial condition option must be specified; --initial-preimage, --initial. However, {0} of those were specified.'.format(num_initial_conditions_specified))
op.print_help()
sys.exit(-1)
# Validate subprogram-specific options here.
# Attempt to parse initial conditions. Upon success, the attribute options.qp_0 should exist.
if options.initial_preimage is not None:
try:
options.initial_preimage = np.array(ast.literal_eval(options.initial_preimage))
expected_shape = (options.embedding_dimension,)
if options.initial_preimage.shape != expected_shape:
raise ValueError('--initial-preimage value had the wrong number of components (got {0} but expected {1}).'.format(options.initial_preimage.shape, expected_shape))
options.qp_0 = dynamics_context.embedding(N=options.embedding_dimension, sheet_index=options.embedding_solution_sheet_index)(options.initial_preimage)
except Exception as e:
print('error parsing --initial-preimage value; error was {0}'.format(e))
op.print_help()
sys.exit(-1)
elif options.initial is not None:
try:
options.initial = heisenberg.util.csv_as_ndarray(heisenberg.util.pop_brackets_off_of(options.initial), float)
expected_shape = (6,)
if options.initial.shape != expected_shape:
<|fim_middle|>
options.qp_0 = options.initial.reshape(2,3)
except ValueError as e:
print('error parsing --initial value: {0}'.format(str(e)))
op.print_help()
sys.exit(-1)
else:
assert False, 'this should never happen because of the check with num_initial_conditions_specified'
rng = np.random.RandomState(options.seed)
heisenberg.plot.plot(dynamics_context, options, rng=rng)
<｜fim▁end｜> | raise ValueError('--initial value had the wrong number of components (got {0} but expected {1}).'.format(options.initial.shape, expected_shape)) |
<|file_name|>__main__.py<|end_file_name|><｜fim▁begin｜>import ast
import heisenberg.library.heisenberg_dynamics_context
import heisenberg.library.orbit_plot
import heisenberg.option_parser
import heisenberg.plot
import heisenberg.util
import matplotlib
import numpy as np
import sys
# https://github.com/matplotlib/matplotlib/issues/5907 says this should fix "Exceeded cell block limit" problems
matplotlib.rcParams['agg.path.chunksize'] = 10000
dynamics_context = heisenberg.library.heisenberg_dynamics_context.Numeric()
op = heisenberg.option_parser.OptionParser(module=heisenberg.plot)
# Add the subprogram-specific options here.
op.add_option(
'--initial-preimage',
dest='initial_preimage',
type='string',
help='Specifies the preimage of the initial conditions with respect to the embedding map specified by the --embedding-dimension and --embedding-solution-sheet-index option values. Should have the form [x_1,...,x_n], where n is the embedding dimension and x_i is a floating point literal for each i.'
)
op.add_option(
'--initial',
dest='initial',
type='string',
help='Specifies the initial conditions [x,y,z,p_x,p_y,p_z], where each of x,y,z,p_x,p_y,p_z are floating point literals.'
)
op.add_option(
'--optimization-iterations',
dest='optimization_iterations',
default=1000,
type='int',
help='Specifies the number of iterations to run the optimization for (if applicable). Default is 1000.'
)
op.add_option(
'--optimize-initial',
dest='optimize_initial',
action='store_true',
default=False,
help='Indicates that the specified initial condition (via whichever of the --initial... options) should be used as the starting point for an optimization to attempt to close the orbit. Default value is False.'
)
op.add_option(
'--output-dir',
dest='output_dir',
default='.',
help='Specifies the directory to write plot images and data files to. Default is current directory.'
)
op.add_option(
'--disable-plot-initial',
dest='disable_plot_initial',
action='store_true',
default=False,
help='Disables plotting the initial curve; only has effect if --optimize-initial is specified.'
)
options,args = op.parse_argv_and_validate()
if options is None:
sys.exit(-1)
num_initial_conditions_specified = sum([
options.initial_preimage is not None,
options.initial is not None,
])
if num_initial_conditions_specified != 1:
print('Some initial condition option must be specified; --initial-preimage, --initial. However, {0} of those were specified.'.format(num_initial_conditions_specified))
op.print_help()
sys.exit(-1)
# Validate subprogram-specific options here.
# Attempt to parse initial conditions. Upon success, the attribute options.qp_0 should exist.
if options.initial_preimage is not None:
try:
options.initial_preimage = np.array(ast.literal_eval(options.initial_preimage))
expected_shape = (options.embedding_dimension,)
if options.initial_preimage.shape != expected_shape:
raise ValueError('--initial-preimage value had the wrong number of components (got {0} but expected {1}).'.format(options.initial_preimage.shape, expected_shape))
options.qp_0 = dynamics_context.embedding(N=options.embedding_dimension, sheet_index=options.embedding_solution_sheet_index)(options.initial_preimage)
except Exception as e:
print('error parsing --initial-preimage value; error was {0}'.format(e))
op.print_help()
sys.exit(-1)
elif options.initial is not None:
try:
options.initial = heisenberg.util.csv_as_ndarray(heisenberg.util.pop_brackets_off_of(options.initial), float)
expected_shape = (6,)
if options.initial.shape != expected_shape:
raise ValueError('--initial value had the wrong number of components (got {0} but expected {1}).'.format(options.initial.shape, expected_shape))
options.qp_0 = options.initial.reshape(2,3)
except ValueError as e:
print('error parsing --initial value: {0}'.format(str(e)))
op.print_help()
sys.exit(-1)
else:
<|fim_middle|>
rng = np.random.RandomState(options.seed)
heisenberg.plot.plot(dynamics_context, options, rng=rng)
<｜fim▁end｜> | assert False, 'this should never happen because of the check with num_initial_conditions_specified' |
<|file_name|>shared_armor_composite_helmet.py<|end_file_name|><｜fim▁begin｜>#### NOTICE: THIS FILE IS AUTOGENERATED
#### MODIFICATIONS MAY BE LOST IF DONE IMPROPERLY
#### PLEASE SEE THE ONLINE DOCUMENTATION FOR EXAMPLES
from swgpy.object import *
def create(kernel):
result = Static()
<｜fim▁hole｜> result.template = "object/static/item/shared_armor_composite_helmet.iff"
result.attribute_template_id = -1
result.stfName("obj_n","unknown_object")
#### BEGIN MODIFICATIONS ####
#### END MODIFICATIONS ####
return result<｜fim▁end｜> | |
<|file_name|>shared_armor_composite_helmet.py<|end_file_name|><｜fim▁begin｜>#### NOTICE: THIS FILE IS AUTOGENERATED
#### MODIFICATIONS MAY BE LOST IF DONE IMPROPERLY
#### PLEASE SEE THE ONLINE DOCUMENTATION FOR EXAMPLES
from swgpy.object import *
def create(kernel):
<|fim_middle|>
<｜fim▁end｜> | result = Static()
result.template = "object/static/item/shared_armor_composite_helmet.iff"
result.attribute_template_id = -1
result.stfName("obj_n","unknown_object")
#### BEGIN MODIFICATIONS ####
#### END MODIFICATIONS ####
return result |
<|file_name|>shared_armor_composite_helmet.py<|end_file_name|><｜fim▁begin｜>#### NOTICE: THIS FILE IS AUTOGENERATED
#### MODIFICATIONS MAY BE LOST IF DONE IMPROPERLY
#### PLEASE SEE THE ONLINE DOCUMENTATION FOR EXAMPLES
from swgpy.object import *
def <|fim_middle|>(kernel):
result = Static()
result.template = "object/static/item/shared_armor_composite_helmet.iff"
result.attribute_template_id = -1
result.stfName("obj_n","unknown_object")
#### BEGIN MODIFICATIONS ####
#### END MODIFICATIONS ####
return result<｜fim▁end｜> | create |
<|file_name|>create.py<|end_file_name|><｜fim▁begin｜><｜fim▁hole｜># You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Command for spanner databases create."""
from googlecloudsdk.api_lib.spanner import database_operations
from googlecloudsdk.api_lib.spanner import databases
from googlecloudsdk.calliope import base
from googlecloudsdk.command_lib.spanner import flags
class Create(base.CreateCommand):
"""Cloud Spanner databases create command."""
@staticmethod
def Args(parser):
"""Args is called by calliope to gather arguments for this command.
Please add arguments in alphabetical order except for no- or a clear-
pair for that argument which can follow the argument itself.
Args:
parser: An argparse parser that you can use to add arguments that go
on the command line after this command. Positional arguments are
allowed.
"""
flags.Instance(positional=False).AddToParser(parser)
flags.Database().AddToParser(parser)
flags.Ddl(help_text='Semi-colon separated DDL (data definition language) '
'statements to run inside the '
'newly created database. If there is an error in any statement, '
'the database is not created. Full DDL specification is at '
'https://cloud.google.com/spanner/docs/data-definition-language'
).AddToParser(parser)
base.ASYNC_FLAG.AddToParser(parser)
def Run(self, args):
"""This is what gets called when the user runs this command.
Args:
args: an argparse namespace. All the arguments that were provided to this
command invocation.
Returns:
Some value that we want to have printed later.
"""
op = databases.Create(
args.instance, args.database, flags.FixDdl(args.ddl or []))
if args.async:
return op
return database_operations.Await(op, 'Creating database')<｜fim▁end｜> | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. |
<|file_name|>create.py<|end_file_name|><｜fim▁begin｜># Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Command for spanner databases create."""
from googlecloudsdk.api_lib.spanner import database_operations
from googlecloudsdk.api_lib.spanner import databases
from googlecloudsdk.calliope import base
from googlecloudsdk.command_lib.spanner import flags
class Create(base.CreateCommand):
<|fim_middle|>
<｜fim▁end｜> | """Cloud Spanner databases create command."""
@staticmethod
def Args(parser):
"""Args is called by calliope to gather arguments for this command.
Please add arguments in alphabetical order except for no- or a clear-
pair for that argument which can follow the argument itself.
Args:
parser: An argparse parser that you can use to add arguments that go
on the command line after this command. Positional arguments are
allowed.
"""
flags.Instance(positional=False).AddToParser(parser)
flags.Database().AddToParser(parser)
flags.Ddl(help_text='Semi-colon separated DDL (data definition language) '
'statements to run inside the '
'newly created database. If there is an error in any statement, '
'the database is not created. Full DDL specification is at '
'https://cloud.google.com/spanner/docs/data-definition-language'
).AddToParser(parser)
base.ASYNC_FLAG.AddToParser(parser)
def Run(self, args):
"""This is what gets called when the user runs this command.
Args:
args: an argparse namespace. All the arguments that were provided to this
command invocation.
Returns:
Some value that we want to have printed later.
"""
op = databases.Create(
args.instance, args.database, flags.FixDdl(args.ddl or []))
if args.async:
return op
return database_operations.Await(op, 'Creating database') |
<|file_name|>create.py<|end_file_name|><｜fim▁begin｜># Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Command for spanner databases create."""
from googlecloudsdk.api_lib.spanner import database_operations
from googlecloudsdk.api_lib.spanner import databases
from googlecloudsdk.calliope import base
from googlecloudsdk.command_lib.spanner import flags
class Create(base.CreateCommand):
"""Cloud Spanner databases create command."""
@staticmethod
def Args(parser):
<|fim_middle|>
def Run(self, args):
"""This is what gets called when the user runs this command.
Args:
args: an argparse namespace. All the arguments that were provided to this
command invocation.
Returns:
Some value that we want to have printed later.
"""
op = databases.Create(
args.instance, args.database, flags.FixDdl(args.ddl or []))
if args.async:
return op
return database_operations.Await(op, 'Creating database')
<｜fim▁end｜> | """Args is called by calliope to gather arguments for this command.
Please add arguments in alphabetical order except for no- or a clear-
pair for that argument which can follow the argument itself.
Args:
parser: An argparse parser that you can use to add arguments that go
on the command line after this command. Positional arguments are
allowed.
"""
flags.Instance(positional=False).AddToParser(parser)
flags.Database().AddToParser(parser)
flags.Ddl(help_text='Semi-colon separated DDL (data definition language) '
'statements to run inside the '
'newly created database. If there is an error in any statement, '
'the database is not created. Full DDL specification is at '
'https://cloud.google.com/spanner/docs/data-definition-language'
).AddToParser(parser)
base.ASYNC_FLAG.AddToParser(parser) |
<|file_name|>create.py<|end_file_name|><｜fim▁begin｜># Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Command for spanner databases create."""
from googlecloudsdk.api_lib.spanner import database_operations
from googlecloudsdk.api_lib.spanner import databases
from googlecloudsdk.calliope import base
from googlecloudsdk.command_lib.spanner import flags
class Create(base.CreateCommand):
"""Cloud Spanner databases create command."""
@staticmethod
def Args(parser):
"""Args is called by calliope to gather arguments for this command.
Please add arguments in alphabetical order except for no- or a clear-
pair for that argument which can follow the argument itself.
Args:
parser: An argparse parser that you can use to add arguments that go
on the command line after this command. Positional arguments are
allowed.
"""
flags.Instance(positional=False).AddToParser(parser)
flags.Database().AddToParser(parser)
flags.Ddl(help_text='Semi-colon separated DDL (data definition language) '
'statements to run inside the '
'newly created database. If there is an error in any statement, '
'the database is not created. Full DDL specification is at '
'https://cloud.google.com/spanner/docs/data-definition-language'
).AddToParser(parser)
base.ASYNC_FLAG.AddToParser(parser)
def Run(self, args):
<|fim_middle|>
<｜fim▁end｜> | """This is what gets called when the user runs this command.
Args:
args: an argparse namespace. All the arguments that were provided to this
command invocation.
Returns:
Some value that we want to have printed later.
"""
op = databases.Create(
args.instance, args.database, flags.FixDdl(args.ddl or []))
if args.async:
return op
return database_operations.Await(op, 'Creating database') |
<|file_name|>create.py<|end_file_name|><｜fim▁begin｜># Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Command for spanner databases create."""
from googlecloudsdk.api_lib.spanner import database_operations
from googlecloudsdk.api_lib.spanner import databases
from googlecloudsdk.calliope import base
from googlecloudsdk.command_lib.spanner import flags
class Create(base.CreateCommand):
"""Cloud Spanner databases create command."""
@staticmethod
def Args(parser):
"""Args is called by calliope to gather arguments for this command.
Please add arguments in alphabetical order except for no- or a clear-
pair for that argument which can follow the argument itself.
Args:
parser: An argparse parser that you can use to add arguments that go
on the command line after this command. Positional arguments are
allowed.
"""
flags.Instance(positional=False).AddToParser(parser)
flags.Database().AddToParser(parser)
flags.Ddl(help_text='Semi-colon separated DDL (data definition language) '
'statements to run inside the '
'newly created database. If there is an error in any statement, '
'the database is not created. Full DDL specification is at '
'https://cloud.google.com/spanner/docs/data-definition-language'
).AddToParser(parser)
base.ASYNC_FLAG.AddToParser(parser)
def Run(self, args):
"""This is what gets called when the user runs this command.
Args:
args: an argparse namespace. All the arguments that were provided to this
command invocation.
Returns:
Some value that we want to have printed later.
"""
op = databases.Create(
args.instance, args.database, flags.FixDdl(args.ddl or []))
if args.async:
<|fim_middle|>
return database_operations.Await(op, 'Creating database')
<｜fim▁end｜> | return op |
<|file_name|>create.py<|end_file_name|><｜fim▁begin｜># Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Command for spanner databases create."""
from googlecloudsdk.api_lib.spanner import database_operations
from googlecloudsdk.api_lib.spanner import databases
from googlecloudsdk.calliope import base
from googlecloudsdk.command_lib.spanner import flags
class Create(base.CreateCommand):
"""Cloud Spanner databases create command."""
@staticmethod
def <|fim_middle|>(parser):
"""Args is called by calliope to gather arguments for this command.
Please add arguments in alphabetical order except for no- or a clear-
pair for that argument which can follow the argument itself.
Args:
parser: An argparse parser that you can use to add arguments that go
on the command line after this command. Positional arguments are
allowed.
"""
flags.Instance(positional=False).AddToParser(parser)
flags.Database().AddToParser(parser)
flags.Ddl(help_text='Semi-colon separated DDL (data definition language) '
'statements to run inside the '
'newly created database. If there is an error in any statement, '
'the database is not created. Full DDL specification is at '
'https://cloud.google.com/spanner/docs/data-definition-language'
).AddToParser(parser)
base.ASYNC_FLAG.AddToParser(parser)
def Run(self, args):
"""This is what gets called when the user runs this command.
Args:
args: an argparse namespace. All the arguments that were provided to this
command invocation.
Returns:
Some value that we want to have printed later.
"""
op = databases.Create(
args.instance, args.database, flags.FixDdl(args.ddl or []))
if args.async:
return op
return database_operations.Await(op, 'Creating database')
<｜fim▁end｜> | Args |
<|file_name|>create.py<|end_file_name|><｜fim▁begin｜># Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Command for spanner databases create."""
from googlecloudsdk.api_lib.spanner import database_operations
from googlecloudsdk.api_lib.spanner import databases
from googlecloudsdk.calliope import base
from googlecloudsdk.command_lib.spanner import flags
class Create(base.CreateCommand):
"""Cloud Spanner databases create command."""
@staticmethod
def Args(parser):
"""Args is called by calliope to gather arguments for this command.
Please add arguments in alphabetical order except for no- or a clear-
pair for that argument which can follow the argument itself.
Args:
parser: An argparse parser that you can use to add arguments that go
on the command line after this command. Positional arguments are
allowed.
"""
flags.Instance(positional=False).AddToParser(parser)
flags.Database().AddToParser(parser)
flags.Ddl(help_text='Semi-colon separated DDL (data definition language) '
'statements to run inside the '
'newly created database. If there is an error in any statement, '
'the database is not created. Full DDL specification is at '
'https://cloud.google.com/spanner/docs/data-definition-language'
).AddToParser(parser)
base.ASYNC_FLAG.AddToParser(parser)
def <|fim_middle|>(self, args):
"""This is what gets called when the user runs this command.
Args:
args: an argparse namespace. All the arguments that were provided to this
command invocation.
Returns:
Some value that we want to have printed later.
"""
op = databases.Create(
args.instance, args.database, flags.FixDdl(args.ddl or []))
if args.async:
return op
return database_operations.Await(op, 'Creating database')
<｜fim▁end｜> | Run |
<|file_name|>_minexponent.py<|end_file_name|><｜fim▁begin｜>import _plotly_utils.basevalidators
class MinexponentValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="minexponent", parent_name="choropleth.colorbar", **kwargs
):
super(MinexponentValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,<｜fim▁hole｜> **kwargs
)<｜fim▁end｜> | edit_type=kwargs.pop("edit_type", "colorbars"),
min=kwargs.pop("min", 0), |
<|file_name|>_minexponent.py<|end_file_name|><｜fim▁begin｜>import _plotly_utils.basevalidators
class MinexponentValidator(_plotly_utils.basevalidators.NumberValidator):
<|fim_middle|>
<｜fim▁end｜> | def __init__(
self, plotly_name="minexponent", parent_name="choropleth.colorbar", **kwargs
):
super(MinexponentValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
min=kwargs.pop("min", 0),
**kwargs
) |
<|file_name|>_minexponent.py<|end_file_name|><｜fim▁begin｜>import _plotly_utils.basevalidators
class MinexponentValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="minexponent", parent_name="choropleth.colorbar", **kwargs
):
<|fim_middle|>
<｜fim▁end｜> | super(MinexponentValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
min=kwargs.pop("min", 0),
**kwargs
) |
<|file_name|>_minexponent.py<|end_file_name|><｜fim▁begin｜>import _plotly_utils.basevalidators
class MinexponentValidator(_plotly_utils.basevalidators.NumberValidator):
def <|fim_middle|>(
self, plotly_name="minexponent", parent_name="choropleth.colorbar", **kwargs
):
super(MinexponentValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
min=kwargs.pop("min", 0),
**kwargs
)
<｜fim▁end｜> | __init__ |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):<｜fim▁hole｜> assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"<｜fim▁end｜> | input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer): |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
<|fim_middle|>
<｜fim▁end｜> | @pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input" |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
<|fim_middle|>
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | from lasagne.layers.input import InputLayer
return InputLayer((3, 2)) |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
<|fim_middle|>
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | assert layer.input_var.ndim == 2 |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
<|fim_middle|>
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | assert layer.get_output_shape() == (3, 2) |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
<|fim_middle|>
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | assert layer.get_output() is layer.input_var |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
<|fim_middle|>
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
<|fim_middle|>
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input) |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
<|fim_middle|>
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer] |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
<|fim_middle|>
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | assert layer.input_var.name == "input" |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
<|fim_middle|>
<｜fim▁end｜> | from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input" |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def <|fim_middle|>(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | layer |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def <|fim_middle|>(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | test_input_var |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def <|fim_middle|>(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | test_get_output_shape |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def <|fim_middle|>(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | test_get_output_without_arguments |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def <|fim_middle|>(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | test_get_output_input_is_variable |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def <|fim_middle|>(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | test_get_output_input_is_array |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def <|fim_middle|>(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | test_get_output_input_is_a_mapping |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def <|fim_middle|>(self, layer):
assert layer.input_var.name == "input"
def test_named_layer_input_var_name(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | test_input_var_name |
<|file_name|>test_input.py<|end_file_name|><｜fim▁begin｜>import numpy
import pytest
import theano
class TestInputLayer:
@pytest.fixture
def layer(self):
from lasagne.layers.input import InputLayer
return InputLayer((3, 2))
def test_input_var(self, layer):
assert layer.input_var.ndim == 2
def test_get_output_shape(self, layer):
assert layer.get_output_shape() == (3, 2)
def test_get_output_without_arguments(self, layer):
assert layer.get_output() is layer.input_var
def test_get_output_input_is_variable(self, layer):
variable = theano.Variable("myvariable")
assert layer.get_output(variable) is variable
def test_get_output_input_is_array(self, layer):
input = [[1,2,3]]
output = layer.get_output(input)
assert numpy.all(output.eval() == input)
def test_get_output_input_is_a_mapping(self, layer):
input = {layer: theano.tensor.matrix()}
assert layer.get_output(input) is input[layer]
def test_input_var_name(self, layer):
assert layer.input_var.name == "input"
def <|fim_middle|>(self):
from lasagne.layers.input import InputLayer
layer = InputLayer((3, 2), name="foo")
assert layer.input_var.name == "foo.input"
<｜fim▁end｜> | test_named_layer_input_var_name |
<|file_name|>t001_basic.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
from runtest import TestBase
class TestCase(TestBase):
def __init__(self):
TestBase.__init__(self, 'abc', """
# DURATION TID FUNCTION
62.202 us [28141] | __cxa_atexit();
[28141] | main() {
[28141] | a() {
[28141] | b() {<｜fim▁hole｜> [28141] | c() {
0.753 us [28141] | getpid();
1.430 us [28141] | } /* c */
1.915 us [28141] | } /* b */
2.405 us [28141] | } /* a */
3.005 us [28141] | } /* main */
""")<｜fim▁end｜> | |
<|file_name|>t001_basic.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
from runtest import TestBase
class TestCase(TestBase):
<|fim_middle|>
<｜fim▁end｜> | def __init__(self):
TestBase.__init__(self, 'abc', """
# DURATION TID FUNCTION
62.202 us [28141] | __cxa_atexit();
[28141] | main() {
[28141] | a() {
[28141] | b() {
[28141] | c() {
0.753 us [28141] | getpid();
1.430 us [28141] | } /* c */
1.915 us [28141] | } /* b */
2.405 us [28141] | } /* a */
3.005 us [28141] | } /* main */
""") |
<|file_name|>t001_basic.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
from runtest import TestBase
class TestCase(TestBase):
def __init__(self):
<|fim_middle|>
<｜fim▁end｜> | TestBase.__init__(self, 'abc', """
# DURATION TID FUNCTION
62.202 us [28141] | __cxa_atexit();
[28141] | main() {
[28141] | a() {
[28141] | b() {
[28141] | c() {
0.753 us [28141] | getpid();
1.430 us [28141] | } /* c */
1.915 us [28141] | } /* b */
2.405 us [28141] | } /* a */
3.005 us [28141] | } /* main */
""") |
<|file_name|>t001_basic.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python
from runtest import TestBase
class TestCase(TestBase):
def <|fim_middle|>(self):
TestBase.__init__(self, 'abc', """
# DURATION TID FUNCTION
62.202 us [28141] | __cxa_atexit();
[28141] | main() {
[28141] | a() {
[28141] | b() {
[28141] | c() {
0.753 us [28141] | getpid();
1.430 us [28141] | } /* c */
1.915 us [28141] | } /* b */
2.405 us [28141] | } /* a */
3.005 us [28141] | } /* main */
""")
<｜fim▁end｜> | __init__ |
<|file_name|>stage01_rnasequencing_analysis_postgresql_models.py<|end_file_name|><｜fim▁begin｜>from SBaaS_base.postgresql_orm_base import *
class data_stage01_rnasequencing_analysis(Base):
__tablename__ = 'data_stage01_rnasequencing_analysis'
id = Column(Integer, Sequence('data_stage01_rnasequencing_analysis_id_seq'), primary_key=True)
analysis_id = Column(String(500))
experiment_id = Column(String(50))
sample_name_abbreviation = Column(String(500)) # equivalent to sample_name_abbreviation
sample_name = Column(String(500)) # equivalent to sample_name_abbreviation
time_point = Column(String(10)) # converted to intermediate in lineage analysis
analysis_type = Column(String(100)); # time-course (i.e., multiple time points), paired (i.e., control compared to multiple replicates), group (i.e., single grouping of samples).
used_ = Column(Boolean);
comment_ = Column(Text);
__table_args__ = (
UniqueConstraint('experiment_id','sample_name_abbreviation','sample_name','time_point','analysis_type','analysis_id'),
)
def __init__(self,
row_dict_I,
):
self.analysis_id=row_dict_I['analysis_id'];
self.experiment_id=row_dict_I['experiment_id'];
self.sample_name_abbreviation=row_dict_I['sample_name_abbreviation'];
self.sample_name=row_dict_I['sample_name'];
self.time_point=row_dict_I['time_point'];
self.analysis_type=row_dict_I['analysis_type'];
self.used_=row_dict_I['used_'];
self.comment_=row_dict_I['comment_'];
def __set__row__(self,analysis_id_I,
experiment_id_I,
sample_name_abbreviation_I,
sample_name_I,
time_point_I,
analysis_type_I,
used__I,
comment__I):
self.analysis_id=analysis_id_I
self.experiment_id=experiment_id_I
self.sample_name_abbreviation=sample_name_abbreviation_I
self.sample_name=sample_name_I
self.time_point=time_point_I
self.analysis_type=analysis_type_I
self.used_=used__I
self.comment_=comment__I
def __repr__dict__(self):
return {'id':self.id,
'analysis_id':self.analysis_id,
'experiment_id':self.experiment_id,
'sample_name_abbreviation':self.sample_name_abbreviation,<｜fim▁hole｜> 'time_point':self.time_point,
'analysis_type':self.analysis_type,
'used_':self.used_,
'comment_':self.comment_}
def __repr__json__(self):
return json.dumps(self.__repr__dict__())<｜fim▁end｜> | 'sample_name':self.sample_name, |
<|file_name|>stage01_rnasequencing_analysis_postgresql_models.py<|end_file_name|><｜fim▁begin｜>from SBaaS_base.postgresql_orm_base import *
class data_stage01_rnasequencing_analysis(Base):
<|fim_middle|>
<｜fim▁end｜> | __tablename__ = 'data_stage01_rnasequencing_analysis'
id = Column(Integer, Sequence('data_stage01_rnasequencing_analysis_id_seq'), primary_key=True)
analysis_id = Column(String(500))
experiment_id = Column(String(50))
sample_name_abbreviation = Column(String(500)) # equivalent to sample_name_abbreviation
sample_name = Column(String(500)) # equivalent to sample_name_abbreviation
time_point = Column(String(10)) # converted to intermediate in lineage analysis
analysis_type = Column(String(100)); # time-course (i.e., multiple time points), paired (i.e., control compared to multiple replicates), group (i.e., single grouping of samples).
used_ = Column(Boolean);
comment_ = Column(Text);
__table_args__ = (
UniqueConstraint('experiment_id','sample_name_abbreviation','sample_name','time_point','analysis_type','analysis_id'),
)
def __init__(self,
row_dict_I,
):
self.analysis_id=row_dict_I['analysis_id'];
self.experiment_id=row_dict_I['experiment_id'];
self.sample_name_abbreviation=row_dict_I['sample_name_abbreviation'];
self.sample_name=row_dict_I['sample_name'];
self.time_point=row_dict_I['time_point'];
self.analysis_type=row_dict_I['analysis_type'];
self.used_=row_dict_I['used_'];
self.comment_=row_dict_I['comment_'];
def __set__row__(self,analysis_id_I,
experiment_id_I,
sample_name_abbreviation_I,
sample_name_I,
time_point_I,
analysis_type_I,
used__I,
comment__I):
self.analysis_id=analysis_id_I
self.experiment_id=experiment_id_I
self.sample_name_abbreviation=sample_name_abbreviation_I
self.sample_name=sample_name_I
self.time_point=time_point_I
self.analysis_type=analysis_type_I
self.used_=used__I
self.comment_=comment__I
def __repr__dict__(self):
return {'id':self.id,
'analysis_id':self.analysis_id,
'experiment_id':self.experiment_id,
'sample_name_abbreviation':self.sample_name_abbreviation,
'sample_name':self.sample_name,
'time_point':self.time_point,
'analysis_type':self.analysis_type,
'used_':self.used_,
'comment_':self.comment_}
def __repr__json__(self):
return json.dumps(self.__repr__dict__()) |
<|file_name|>stage01_rnasequencing_analysis_postgresql_models.py<|end_file_name|><｜fim▁begin｜>from SBaaS_base.postgresql_orm_base import *
class data_stage01_rnasequencing_analysis(Base):
__tablename__ = 'data_stage01_rnasequencing_analysis'
id = Column(Integer, Sequence('data_stage01_rnasequencing_analysis_id_seq'), primary_key=True)
analysis_id = Column(String(500))
experiment_id = Column(String(50))
sample_name_abbreviation = Column(String(500)) # equivalent to sample_name_abbreviation
sample_name = Column(String(500)) # equivalent to sample_name_abbreviation
time_point = Column(String(10)) # converted to intermediate in lineage analysis
analysis_type = Column(String(100)); # time-course (i.e., multiple time points), paired (i.e., control compared to multiple replicates), group (i.e., single grouping of samples).
used_ = Column(Boolean);
comment_ = Column(Text);
__table_args__ = (
UniqueConstraint('experiment_id','sample_name_abbreviation','sample_name','time_point','analysis_type','analysis_id'),
)
def __init__(self,
row_dict_I,
):
<|fim_middle|>
def __set__row__(self,analysis_id_I,
experiment_id_I,
sample_name_abbreviation_I,
sample_name_I,
time_point_I,
analysis_type_I,
used__I,
comment__I):
self.analysis_id=analysis_id_I
self.experiment_id=experiment_id_I
self.sample_name_abbreviation=sample_name_abbreviation_I
self.sample_name=sample_name_I
self.time_point=time_point_I
self.analysis_type=analysis_type_I
self.used_=used__I
self.comment_=comment__I
def __repr__dict__(self):
return {'id':self.id,
'analysis_id':self.analysis_id,
'experiment_id':self.experiment_id,
'sample_name_abbreviation':self.sample_name_abbreviation,
'sample_name':self.sample_name,
'time_point':self.time_point,
'analysis_type':self.analysis_type,
'used_':self.used_,
'comment_':self.comment_}
def __repr__json__(self):
return json.dumps(self.__repr__dict__())<｜fim▁end｜> | self.analysis_id=row_dict_I['analysis_id'];
self.experiment_id=row_dict_I['experiment_id'];
self.sample_name_abbreviation=row_dict_I['sample_name_abbreviation'];
self.sample_name=row_dict_I['sample_name'];
self.time_point=row_dict_I['time_point'];
self.analysis_type=row_dict_I['analysis_type'];
self.used_=row_dict_I['used_'];
self.comment_=row_dict_I['comment_']; |
<|file_name|>stage01_rnasequencing_analysis_postgresql_models.py<|end_file_name|><｜fim▁begin｜>from SBaaS_base.postgresql_orm_base import *
class data_stage01_rnasequencing_analysis(Base):
__tablename__ = 'data_stage01_rnasequencing_analysis'
id = Column(Integer, Sequence('data_stage01_rnasequencing_analysis_id_seq'), primary_key=True)
analysis_id = Column(String(500))
experiment_id = Column(String(50))
sample_name_abbreviation = Column(String(500)) # equivalent to sample_name_abbreviation
sample_name = Column(String(500)) # equivalent to sample_name_abbreviation
time_point = Column(String(10)) # converted to intermediate in lineage analysis
analysis_type = Column(String(100)); # time-course (i.e., multiple time points), paired (i.e., control compared to multiple replicates), group (i.e., single grouping of samples).
used_ = Column(Boolean);
comment_ = Column(Text);
__table_args__ = (
UniqueConstraint('experiment_id','sample_name_abbreviation','sample_name','time_point','analysis_type','analysis_id'),
)
def __init__(self,
row_dict_I,
):
self.analysis_id=row_dict_I['analysis_id'];
self.experiment_id=row_dict_I['experiment_id'];
self.sample_name_abbreviation=row_dict_I['sample_name_abbreviation'];
self.sample_name=row_dict_I['sample_name'];
self.time_point=row_dict_I['time_point'];
self.analysis_type=row_dict_I['analysis_type'];
self.used_=row_dict_I['used_'];
self.comment_=row_dict_I['comment_'];
def __set__row__(self,analysis_id_I,
experiment_id_I,
sample_name_abbreviation_I,
sample_name_I,
time_point_I,
analysis_type_I,
used__I,
comment__I):
<|fim_middle|>
def __repr__dict__(self):
return {'id':self.id,
'analysis_id':self.analysis_id,
'experiment_id':self.experiment_id,
'sample_name_abbreviation':self.sample_name_abbreviation,
'sample_name':self.sample_name,
'time_point':self.time_point,
'analysis_type':self.analysis_type,
'used_':self.used_,
'comment_':self.comment_}
def __repr__json__(self):
return json.dumps(self.__repr__dict__())<｜fim▁end｜> | self.analysis_id=analysis_id_I
self.experiment_id=experiment_id_I
self.sample_name_abbreviation=sample_name_abbreviation_I
self.sample_name=sample_name_I
self.time_point=time_point_I
self.analysis_type=analysis_type_I
self.used_=used__I
self.comment_=comment__I |
<|file_name|>stage01_rnasequencing_analysis_postgresql_models.py<|end_file_name|><｜fim▁begin｜>from SBaaS_base.postgresql_orm_base import *
class data_stage01_rnasequencing_analysis(Base):
__tablename__ = 'data_stage01_rnasequencing_analysis'
id = Column(Integer, Sequence('data_stage01_rnasequencing_analysis_id_seq'), primary_key=True)
analysis_id = Column(String(500))
experiment_id = Column(String(50))
sample_name_abbreviation = Column(String(500)) # equivalent to sample_name_abbreviation
sample_name = Column(String(500)) # equivalent to sample_name_abbreviation
time_point = Column(String(10)) # converted to intermediate in lineage analysis
analysis_type = Column(String(100)); # time-course (i.e., multiple time points), paired (i.e., control compared to multiple replicates), group (i.e., single grouping of samples).
used_ = Column(Boolean);
comment_ = Column(Text);
__table_args__ = (
UniqueConstraint('experiment_id','sample_name_abbreviation','sample_name','time_point','analysis_type','analysis_id'),
)
def __init__(self,
row_dict_I,
):
self.analysis_id=row_dict_I['analysis_id'];
self.experiment_id=row_dict_I['experiment_id'];
self.sample_name_abbreviation=row_dict_I['sample_name_abbreviation'];
self.sample_name=row_dict_I['sample_name'];
self.time_point=row_dict_I['time_point'];
self.analysis_type=row_dict_I['analysis_type'];
self.used_=row_dict_I['used_'];
self.comment_=row_dict_I['comment_'];
def __set__row__(self,analysis_id_I,
experiment_id_I,
sample_name_abbreviation_I,
sample_name_I,
time_point_I,
analysis_type_I,
used__I,
comment__I):
self.analysis_id=analysis_id_I
self.experiment_id=experiment_id_I
self.sample_name_abbreviation=sample_name_abbreviation_I
self.sample_name=sample_name_I
self.time_point=time_point_I
self.analysis_type=analysis_type_I
self.used_=used__I
self.comment_=comment__I
def __repr__dict__(self):
<|fim_middle|>
def __repr__json__(self):
return json.dumps(self.__repr__dict__())<｜fim▁end｜> | return {'id':self.id,
'analysis_id':self.analysis_id,
'experiment_id':self.experiment_id,
'sample_name_abbreviation':self.sample_name_abbreviation,
'sample_name':self.sample_name,
'time_point':self.time_point,
'analysis_type':self.analysis_type,
'used_':self.used_,
'comment_':self.comment_} |
<|file_name|>stage01_rnasequencing_analysis_postgresql_models.py<|end_file_name|><｜fim▁begin｜>from SBaaS_base.postgresql_orm_base import *
class data_stage01_rnasequencing_analysis(Base):
__tablename__ = 'data_stage01_rnasequencing_analysis'
id = Column(Integer, Sequence('data_stage01_rnasequencing_analysis_id_seq'), primary_key=True)
analysis_id = Column(String(500))
experiment_id = Column(String(50))
sample_name_abbreviation = Column(String(500)) # equivalent to sample_name_abbreviation
sample_name = Column(String(500)) # equivalent to sample_name_abbreviation
time_point = Column(String(10)) # converted to intermediate in lineage analysis
analysis_type = Column(String(100)); # time-course (i.e., multiple time points), paired (i.e., control compared to multiple replicates), group (i.e., single grouping of samples).
used_ = Column(Boolean);
comment_ = Column(Text);
__table_args__ = (
UniqueConstraint('experiment_id','sample_name_abbreviation','sample_name','time_point','analysis_type','analysis_id'),
)
def __init__(self,
row_dict_I,
):
self.analysis_id=row_dict_I['analysis_id'];
self.experiment_id=row_dict_I['experiment_id'];
self.sample_name_abbreviation=row_dict_I['sample_name_abbreviation'];
self.sample_name=row_dict_I['sample_name'];
self.time_point=row_dict_I['time_point'];
self.analysis_type=row_dict_I['analysis_type'];
self.used_=row_dict_I['used_'];
self.comment_=row_dict_I['comment_'];
def __set__row__(self,analysis_id_I,
experiment_id_I,
sample_name_abbreviation_I,
sample_name_I,
time_point_I,
analysis_type_I,
used__I,
comment__I):
self.analysis_id=analysis_id_I
self.experiment_id=experiment_id_I
self.sample_name_abbreviation=sample_name_abbreviation_I
self.sample_name=sample_name_I
self.time_point=time_point_I
self.analysis_type=analysis_type_I
self.used_=used__I
self.comment_=comment__I
def __repr__dict__(self):
return {'id':self.id,
'analysis_id':self.analysis_id,
'experiment_id':self.experiment_id,
'sample_name_abbreviation':self.sample_name_abbreviation,
'sample_name':self.sample_name,
'time_point':self.time_point,
'analysis_type':self.analysis_type,
'used_':self.used_,
'comment_':self.comment_}
def __repr__json__(self):
<|fim_middle|>
<｜fim▁end｜> | return json.dumps(self.__repr__dict__()) |
<|file_name|>stage01_rnasequencing_analysis_postgresql_models.py<|end_file_name|><｜fim▁begin｜>from SBaaS_base.postgresql_orm_base import *
class data_stage01_rnasequencing_analysis(Base):
__tablename__ = 'data_stage01_rnasequencing_analysis'
id = Column(Integer, Sequence('data_stage01_rnasequencing_analysis_id_seq'), primary_key=True)
analysis_id = Column(String(500))
experiment_id = Column(String(50))
sample_name_abbreviation = Column(String(500)) # equivalent to sample_name_abbreviation
sample_name = Column(String(500)) # equivalent to sample_name_abbreviation
time_point = Column(String(10)) # converted to intermediate in lineage analysis
analysis_type = Column(String(100)); # time-course (i.e., multiple time points), paired (i.e., control compared to multiple replicates), group (i.e., single grouping of samples).
used_ = Column(Boolean);
comment_ = Column(Text);
__table_args__ = (
UniqueConstraint('experiment_id','sample_name_abbreviation','sample_name','time_point','analysis_type','analysis_id'),
)
def <|fim_middle|>(self,
row_dict_I,
):
self.analysis_id=row_dict_I['analysis_id'];
self.experiment_id=row_dict_I['experiment_id'];
self.sample_name_abbreviation=row_dict_I['sample_name_abbreviation'];
self.sample_name=row_dict_I['sample_name'];
self.time_point=row_dict_I['time_point'];
self.analysis_type=row_dict_I['analysis_type'];
self.used_=row_dict_I['used_'];
self.comment_=row_dict_I['comment_'];
def __set__row__(self,analysis_id_I,
experiment_id_I,
sample_name_abbreviation_I,
sample_name_I,
time_point_I,
analysis_type_I,
used__I,
comment__I):
self.analysis_id=analysis_id_I
self.experiment_id=experiment_id_I
self.sample_name_abbreviation=sample_name_abbreviation_I
self.sample_name=sample_name_I
self.time_point=time_point_I
self.analysis_type=analysis_type_I
self.used_=used__I
self.comment_=comment__I
def __repr__dict__(self):
return {'id':self.id,
'analysis_id':self.analysis_id,
'experiment_id':self.experiment_id,
'sample_name_abbreviation':self.sample_name_abbreviation,
'sample_name':self.sample_name,
'time_point':self.time_point,
'analysis_type':self.analysis_type,
'used_':self.used_,
'comment_':self.comment_}
def __repr__json__(self):
return json.dumps(self.__repr__dict__())<｜fim▁end｜> | __init__ |
<|file_name|>stage01_rnasequencing_analysis_postgresql_models.py<|end_file_name|><｜fim▁begin｜>from SBaaS_base.postgresql_orm_base import *
class data_stage01_rnasequencing_analysis(Base):
__tablename__ = 'data_stage01_rnasequencing_analysis'
id = Column(Integer, Sequence('data_stage01_rnasequencing_analysis_id_seq'), primary_key=True)
analysis_id = Column(String(500))
experiment_id = Column(String(50))
sample_name_abbreviation = Column(String(500)) # equivalent to sample_name_abbreviation
sample_name = Column(String(500)) # equivalent to sample_name_abbreviation
time_point = Column(String(10)) # converted to intermediate in lineage analysis
analysis_type = Column(String(100)); # time-course (i.e., multiple time points), paired (i.e., control compared to multiple replicates), group (i.e., single grouping of samples).
used_ = Column(Boolean);
comment_ = Column(Text);
__table_args__ = (
UniqueConstraint('experiment_id','sample_name_abbreviation','sample_name','time_point','analysis_type','analysis_id'),
)
def __init__(self,
row_dict_I,
):
self.analysis_id=row_dict_I['analysis_id'];
self.experiment_id=row_dict_I['experiment_id'];
self.sample_name_abbreviation=row_dict_I['sample_name_abbreviation'];
self.sample_name=row_dict_I['sample_name'];
self.time_point=row_dict_I['time_point'];
self.analysis_type=row_dict_I['analysis_type'];
self.used_=row_dict_I['used_'];
self.comment_=row_dict_I['comment_'];
def <|fim_middle|>(self,analysis_id_I,
experiment_id_I,
sample_name_abbreviation_I,
sample_name_I,
time_point_I,
analysis_type_I,
used__I,
comment__I):
self.analysis_id=analysis_id_I
self.experiment_id=experiment_id_I
self.sample_name_abbreviation=sample_name_abbreviation_I
self.sample_name=sample_name_I
self.time_point=time_point_I
self.analysis_type=analysis_type_I
self.used_=used__I
self.comment_=comment__I
def __repr__dict__(self):
return {'id':self.id,
'analysis_id':self.analysis_id,
'experiment_id':self.experiment_id,
'sample_name_abbreviation':self.sample_name_abbreviation,
'sample_name':self.sample_name,
'time_point':self.time_point,
'analysis_type':self.analysis_type,
'used_':self.used_,
'comment_':self.comment_}
def __repr__json__(self):
return json.dumps(self.__repr__dict__())<｜fim▁end｜> | __set__row__ |
<|file_name|>stage01_rnasequencing_analysis_postgresql_models.py<|end_file_name|><｜fim▁begin｜>from SBaaS_base.postgresql_orm_base import *
class data_stage01_rnasequencing_analysis(Base):
__tablename__ = 'data_stage01_rnasequencing_analysis'
id = Column(Integer, Sequence('data_stage01_rnasequencing_analysis_id_seq'), primary_key=True)
analysis_id = Column(String(500))
experiment_id = Column(String(50))
sample_name_abbreviation = Column(String(500)) # equivalent to sample_name_abbreviation
sample_name = Column(String(500)) # equivalent to sample_name_abbreviation
time_point = Column(String(10)) # converted to intermediate in lineage analysis
analysis_type = Column(String(100)); # time-course (i.e., multiple time points), paired (i.e., control compared to multiple replicates), group (i.e., single grouping of samples).
used_ = Column(Boolean);
comment_ = Column(Text);
__table_args__ = (
UniqueConstraint('experiment_id','sample_name_abbreviation','sample_name','time_point','analysis_type','analysis_id'),
)
def __init__(self,
row_dict_I,
):
self.analysis_id=row_dict_I['analysis_id'];
self.experiment_id=row_dict_I['experiment_id'];
self.sample_name_abbreviation=row_dict_I['sample_name_abbreviation'];
self.sample_name=row_dict_I['sample_name'];
self.time_point=row_dict_I['time_point'];
self.analysis_type=row_dict_I['analysis_type'];
self.used_=row_dict_I['used_'];
self.comment_=row_dict_I['comment_'];
def __set__row__(self,analysis_id_I,
experiment_id_I,
sample_name_abbreviation_I,
sample_name_I,
time_point_I,
analysis_type_I,
used__I,
comment__I):
self.analysis_id=analysis_id_I
self.experiment_id=experiment_id_I
self.sample_name_abbreviation=sample_name_abbreviation_I
self.sample_name=sample_name_I
self.time_point=time_point_I
self.analysis_type=analysis_type_I
self.used_=used__I
self.comment_=comment__I
def <|fim_middle|>(self):
return {'id':self.id,
'analysis_id':self.analysis_id,
'experiment_id':self.experiment_id,
'sample_name_abbreviation':self.sample_name_abbreviation,
'sample_name':self.sample_name,
'time_point':self.time_point,
'analysis_type':self.analysis_type,
'used_':self.used_,
'comment_':self.comment_}
def __repr__json__(self):
return json.dumps(self.__repr__dict__())<｜fim▁end｜> | __repr__dict__ |
<|file_name|>stage01_rnasequencing_analysis_postgresql_models.py<|end_file_name|><｜fim▁begin｜>from SBaaS_base.postgresql_orm_base import *
class data_stage01_rnasequencing_analysis(Base):
__tablename__ = 'data_stage01_rnasequencing_analysis'
id = Column(Integer, Sequence('data_stage01_rnasequencing_analysis_id_seq'), primary_key=True)
analysis_id = Column(String(500))
experiment_id = Column(String(50))
sample_name_abbreviation = Column(String(500)) # equivalent to sample_name_abbreviation
sample_name = Column(String(500)) # equivalent to sample_name_abbreviation
time_point = Column(String(10)) # converted to intermediate in lineage analysis
analysis_type = Column(String(100)); # time-course (i.e., multiple time points), paired (i.e., control compared to multiple replicates), group (i.e., single grouping of samples).
used_ = Column(Boolean);
comment_ = Column(Text);
__table_args__ = (
UniqueConstraint('experiment_id','sample_name_abbreviation','sample_name','time_point','analysis_type','analysis_id'),
)
def __init__(self,
row_dict_I,
):
self.analysis_id=row_dict_I['analysis_id'];
self.experiment_id=row_dict_I['experiment_id'];
self.sample_name_abbreviation=row_dict_I['sample_name_abbreviation'];
self.sample_name=row_dict_I['sample_name'];
self.time_point=row_dict_I['time_point'];
self.analysis_type=row_dict_I['analysis_type'];
self.used_=row_dict_I['used_'];
self.comment_=row_dict_I['comment_'];
def __set__row__(self,analysis_id_I,
experiment_id_I,
sample_name_abbreviation_I,
sample_name_I,
time_point_I,
analysis_type_I,
used__I,
comment__I):
self.analysis_id=analysis_id_I
self.experiment_id=experiment_id_I
self.sample_name_abbreviation=sample_name_abbreviation_I
self.sample_name=sample_name_I
self.time_point=time_point_I
self.analysis_type=analysis_type_I
self.used_=used__I
self.comment_=comment__I
def __repr__dict__(self):
return {'id':self.id,
'analysis_id':self.analysis_id,
'experiment_id':self.experiment_id,
'sample_name_abbreviation':self.sample_name_abbreviation,
'sample_name':self.sample_name,
'time_point':self.time_point,
'analysis_type':self.analysis_type,
'used_':self.used_,
'comment_':self.comment_}
def <|fim_middle|>(self):
return json.dumps(self.__repr__dict__())<｜fim▁end｜> | __repr__json__ |
<|file_name|>plots.py<|end_file_name|><｜fim▁begin｜><｜fim▁hole｜>from positions import POS_10_5
from scipy import signal
def plot_timeseries(frames, time=None, offset=None, color='k', linestyle='-'):
frames = np.asarray(frames)
if offset == None:
offset = np.max(np.std(frames, axis=0)) * 3
if time == None:
time = np.arange(frames.shape[0])
plt.plot(time, frames - np.mean(frames, axis=0) +
np.arange(frames.shape[1]) * offset, color=color, ls=linestyle)
def plot_scalpgrid(scalps, sensors, locs=POS_10_5, width=None,
clim=None, cmap=None, titles=None):
'''
Plots a grid with scalpplots. Scalps contains the different scalps in the
rows, sensors contains the names for the columns of scalps, locs is a dict
that maps the sensor-names to locations.
Width determines the width of the grid that contains the plots. Cmap selects
a colormap, for example plt.cm.RdBu_r is very useful for AUC-ROC plots.
Clim is a list containing the minimim and maximum value mapped to a color.
Titles is an optional list with titles for each subplot.
Returns a list with subplots for further manipulation.
'''
scalps = np.asarray(scalps)
assert scalps.ndim == 2
nscalps = scalps.shape[0]
subplots = []
if not width:
width = int(min(8, np.ceil(np.sqrt(nscalps))))
height = int(np.ceil(nscalps/float(width)))
if not clim:
clim = [np.min(scalps), np.max(scalps)]
plt.clf()
for i in range(nscalps):
subplots.append(plt.subplot(height, width, i + 1))
plot_scalp(scalps[i], sensors, locs, clim=clim, cmap=cmap)
if titles:
plt.title(titles[i])
# plot colorbar next to last scalp
bb = plt.gca().get_position()
plt.colorbar(cax=plt.axes([bb.xmax + bb.width/10, bb.ymin, bb.width/10,
bb.height]), ticks=np.linspace(clim[0], clim[1], 5).round(2))
return subplots<｜fim▁end｜> | import matplotlib.pyplot as plt
import numpy as np
import scalpplot
from scalpplot import plot_scalp |
<|file_name|>plots.py<|end_file_name|><｜fim▁begin｜>import matplotlib.pyplot as plt
import numpy as np
import scalpplot
from scalpplot import plot_scalp
from positions import POS_10_5
from scipy import signal
def plot_timeseries(frames, time=None, offset=None, color='k', linestyle='-'):
<|fim_middle|>
def plot_scalpgrid(scalps, sensors, locs=POS_10_5, width=None,
clim=None, cmap=None, titles=None):
'''
Plots a grid with scalpplots. Scalps contains the different scalps in the
rows, sensors contains the names for the columns of scalps, locs is a dict
that maps the sensor-names to locations.
Width determines the width of the grid that contains the plots. Cmap selects
a colormap, for example plt.cm.RdBu_r is very useful for AUC-ROC plots.
Clim is a list containing the minimim and maximum value mapped to a color.
Titles is an optional list with titles for each subplot.
Returns a list with subplots for further manipulation.
'''
scalps = np.asarray(scalps)
assert scalps.ndim == 2
nscalps = scalps.shape[0]
subplots = []
if not width:
width = int(min(8, np.ceil(np.sqrt(nscalps))))
height = int(np.ceil(nscalps/float(width)))
if not clim:
clim = [np.min(scalps), np.max(scalps)]
plt.clf()
for i in range(nscalps):
subplots.append(plt.subplot(height, width, i + 1))
plot_scalp(scalps[i], sensors, locs, clim=clim, cmap=cmap)
if titles:
plt.title(titles[i])
# plot colorbar next to last scalp
bb = plt.gca().get_position()
plt.colorbar(cax=plt.axes([bb.xmax + bb.width/10, bb.ymin, bb.width/10,
bb.height]), ticks=np.linspace(clim[0], clim[1], 5).round(2))
return subplots
<｜fim▁end｜> | frames = np.asarray(frames)
if offset == None:
offset = np.max(np.std(frames, axis=0)) * 3
if time == None:
time = np.arange(frames.shape[0])
plt.plot(time, frames - np.mean(frames, axis=0) +
np.arange(frames.shape[1]) * offset, color=color, ls=linestyle) |
<|file_name|>plots.py<|end_file_name|><｜fim▁begin｜>import matplotlib.pyplot as plt
import numpy as np
import scalpplot
from scalpplot import plot_scalp
from positions import POS_10_5
from scipy import signal
def plot_timeseries(frames, time=None, offset=None, color='k', linestyle='-'):
frames = np.asarray(frames)
if offset == None:
offset = np.max(np.std(frames, axis=0)) * 3
if time == None:
time = np.arange(frames.shape[0])
plt.plot(time, frames - np.mean(frames, axis=0) +
np.arange(frames.shape[1]) * offset, color=color, ls=linestyle)
def plot_scalpgrid(scalps, sensors, locs=POS_10_5, width=None,
clim=None, cmap=None, titles=None):
<|fim_middle|>
<｜fim▁end｜> | '''
Plots a grid with scalpplots. Scalps contains the different scalps in the
rows, sensors contains the names for the columns of scalps, locs is a dict
that maps the sensor-names to locations.
Width determines the width of the grid that contains the plots. Cmap selects
a colormap, for example plt.cm.RdBu_r is very useful for AUC-ROC plots.
Clim is a list containing the minimim and maximum value mapped to a color.
Titles is an optional list with titles for each subplot.
Returns a list with subplots for further manipulation.
'''
scalps = np.asarray(scalps)
assert scalps.ndim == 2
nscalps = scalps.shape[0]
subplots = []
if not width:
width = int(min(8, np.ceil(np.sqrt(nscalps))))
height = int(np.ceil(nscalps/float(width)))
if not clim:
clim = [np.min(scalps), np.max(scalps)]
plt.clf()
for i in range(nscalps):
subplots.append(plt.subplot(height, width, i + 1))
plot_scalp(scalps[i], sensors, locs, clim=clim, cmap=cmap)
if titles:
plt.title(titles[i])
# plot colorbar next to last scalp
bb = plt.gca().get_position()
plt.colorbar(cax=plt.axes([bb.xmax + bb.width/10, bb.ymin, bb.width/10,
bb.height]), ticks=np.linspace(clim[0], clim[1], 5).round(2))
return subplots |
<|file_name|>plots.py<|end_file_name|><｜fim▁begin｜>import matplotlib.pyplot as plt
import numpy as np
import scalpplot
from scalpplot import plot_scalp
from positions import POS_10_5
from scipy import signal
def plot_timeseries(frames, time=None, offset=None, color='k', linestyle='-'):
frames = np.asarray(frames)
if offset == None:
<|fim_middle|>
if time == None:
time = np.arange(frames.shape[0])
plt.plot(time, frames - np.mean(frames, axis=0) +
np.arange(frames.shape[1]) * offset, color=color, ls=linestyle)
def plot_scalpgrid(scalps, sensors, locs=POS_10_5, width=None,
clim=None, cmap=None, titles=None):
'''
Plots a grid with scalpplots. Scalps contains the different scalps in the
rows, sensors contains the names for the columns of scalps, locs is a dict
that maps the sensor-names to locations.
Width determines the width of the grid that contains the plots. Cmap selects
a colormap, for example plt.cm.RdBu_r is very useful for AUC-ROC plots.
Clim is a list containing the minimim and maximum value mapped to a color.
Titles is an optional list with titles for each subplot.
Returns a list with subplots for further manipulation.
'''
scalps = np.asarray(scalps)
assert scalps.ndim == 2
nscalps = scalps.shape[0]
subplots = []
if not width:
width = int(min(8, np.ceil(np.sqrt(nscalps))))
height = int(np.ceil(nscalps/float(width)))
if not clim:
clim = [np.min(scalps), np.max(scalps)]
plt.clf()
for i in range(nscalps):
subplots.append(plt.subplot(height, width, i + 1))
plot_scalp(scalps[i], sensors, locs, clim=clim, cmap=cmap)
if titles:
plt.title(titles[i])
# plot colorbar next to last scalp
bb = plt.gca().get_position()
plt.colorbar(cax=plt.axes([bb.xmax + bb.width/10, bb.ymin, bb.width/10,
bb.height]), ticks=np.linspace(clim[0], clim[1], 5).round(2))
return subplots
<｜fim▁end｜> | offset = np.max(np.std(frames, axis=0)) * 3 |
<|file_name|>plots.py<|end_file_name|><｜fim▁begin｜>import matplotlib.pyplot as plt
import numpy as np
import scalpplot
from scalpplot import plot_scalp
from positions import POS_10_5
from scipy import signal
def plot_timeseries(frames, time=None, offset=None, color='k', linestyle='-'):
frames = np.asarray(frames)
if offset == None:
offset = np.max(np.std(frames, axis=0)) * 3
if time == None:
<|fim_middle|>
plt.plot(time, frames - np.mean(frames, axis=0) +
np.arange(frames.shape[1]) * offset, color=color, ls=linestyle)
def plot_scalpgrid(scalps, sensors, locs=POS_10_5, width=None,
clim=None, cmap=None, titles=None):
'''
Plots a grid with scalpplots. Scalps contains the different scalps in the
rows, sensors contains the names for the columns of scalps, locs is a dict
that maps the sensor-names to locations.
Width determines the width of the grid that contains the plots. Cmap selects
a colormap, for example plt.cm.RdBu_r is very useful for AUC-ROC plots.
Clim is a list containing the minimim and maximum value mapped to a color.
Titles is an optional list with titles for each subplot.
Returns a list with subplots for further manipulation.
'''
scalps = np.asarray(scalps)
assert scalps.ndim == 2
nscalps = scalps.shape[0]
subplots = []
if not width:
width = int(min(8, np.ceil(np.sqrt(nscalps))))
height = int(np.ceil(nscalps/float(width)))
if not clim:
clim = [np.min(scalps), np.max(scalps)]
plt.clf()
for i in range(nscalps):
subplots.append(plt.subplot(height, width, i + 1))
plot_scalp(scalps[i], sensors, locs, clim=clim, cmap=cmap)
if titles:
plt.title(titles[i])
# plot colorbar next to last scalp
bb = plt.gca().get_position()
plt.colorbar(cax=plt.axes([bb.xmax + bb.width/10, bb.ymin, bb.width/10,
bb.height]), ticks=np.linspace(clim[0], clim[1], 5).round(2))
return subplots
<｜fim▁end｜> | time = np.arange(frames.shape[0]) |
<|file_name|>plots.py<|end_file_name|><｜fim▁begin｜>import matplotlib.pyplot as plt
import numpy as np
import scalpplot
from scalpplot import plot_scalp
from positions import POS_10_5
from scipy import signal
def plot_timeseries(frames, time=None, offset=None, color='k', linestyle='-'):
frames = np.asarray(frames)
if offset == None:
offset = np.max(np.std(frames, axis=0)) * 3
if time == None:
time = np.arange(frames.shape[0])
plt.plot(time, frames - np.mean(frames, axis=0) +
np.arange(frames.shape[1]) * offset, color=color, ls=linestyle)
def plot_scalpgrid(scalps, sensors, locs=POS_10_5, width=None,
clim=None, cmap=None, titles=None):
'''
Plots a grid with scalpplots. Scalps contains the different scalps in the
rows, sensors contains the names for the columns of scalps, locs is a dict
that maps the sensor-names to locations.
Width determines the width of the grid that contains the plots. Cmap selects
a colormap, for example plt.cm.RdBu_r is very useful for AUC-ROC plots.
Clim is a list containing the minimim and maximum value mapped to a color.
Titles is an optional list with titles for each subplot.
Returns a list with subplots for further manipulation.
'''
scalps = np.asarray(scalps)
assert scalps.ndim == 2
nscalps = scalps.shape[0]
subplots = []
if not width:
<|fim_middle|>
height = int(np.ceil(nscalps/float(width)))
if not clim:
clim = [np.min(scalps), np.max(scalps)]
plt.clf()
for i in range(nscalps):
subplots.append(plt.subplot(height, width, i + 1))
plot_scalp(scalps[i], sensors, locs, clim=clim, cmap=cmap)
if titles:
plt.title(titles[i])
# plot colorbar next to last scalp
bb = plt.gca().get_position()
plt.colorbar(cax=plt.axes([bb.xmax + bb.width/10, bb.ymin, bb.width/10,
bb.height]), ticks=np.linspace(clim[0], clim[1], 5).round(2))
return subplots
<｜fim▁end｜> | width = int(min(8, np.ceil(np.sqrt(nscalps)))) |
<|file_name|>plots.py<|end_file_name|><｜fim▁begin｜>import matplotlib.pyplot as plt
import numpy as np
import scalpplot
from scalpplot import plot_scalp
from positions import POS_10_5
from scipy import signal
def plot_timeseries(frames, time=None, offset=None, color='k', linestyle='-'):
frames = np.asarray(frames)
if offset == None:
offset = np.max(np.std(frames, axis=0)) * 3
if time == None:
time = np.arange(frames.shape[0])
plt.plot(time, frames - np.mean(frames, axis=0) +
np.arange(frames.shape[1]) * offset, color=color, ls=linestyle)
def plot_scalpgrid(scalps, sensors, locs=POS_10_5, width=None,
clim=None, cmap=None, titles=None):
'''
Plots a grid with scalpplots. Scalps contains the different scalps in the
rows, sensors contains the names for the columns of scalps, locs is a dict
that maps the sensor-names to locations.
Width determines the width of the grid that contains the plots. Cmap selects
a colormap, for example plt.cm.RdBu_r is very useful for AUC-ROC plots.
Clim is a list containing the minimim and maximum value mapped to a color.
Titles is an optional list with titles for each subplot.
Returns a list with subplots for further manipulation.
'''
scalps = np.asarray(scalps)
assert scalps.ndim == 2
nscalps = scalps.shape[0]
subplots = []
if not width:
width = int(min(8, np.ceil(np.sqrt(nscalps))))
height = int(np.ceil(nscalps/float(width)))
if not clim:
<|fim_middle|>
plt.clf()
for i in range(nscalps):
subplots.append(plt.subplot(height, width, i + 1))
plot_scalp(scalps[i], sensors, locs, clim=clim, cmap=cmap)
if titles:
plt.title(titles[i])
# plot colorbar next to last scalp
bb = plt.gca().get_position()
plt.colorbar(cax=plt.axes([bb.xmax + bb.width/10, bb.ymin, bb.width/10,
bb.height]), ticks=np.linspace(clim[0], clim[1], 5).round(2))
return subplots
<｜fim▁end｜> | clim = [np.min(scalps), np.max(scalps)] |
<|file_name|>plots.py<|end_file_name|><｜fim▁begin｜>import matplotlib.pyplot as plt
import numpy as np
import scalpplot
from scalpplot import plot_scalp
from positions import POS_10_5
from scipy import signal
def plot_timeseries(frames, time=None, offset=None, color='k', linestyle='-'):
frames = np.asarray(frames)
if offset == None:
offset = np.max(np.std(frames, axis=0)) * 3
if time == None:
time = np.arange(frames.shape[0])
plt.plot(time, frames - np.mean(frames, axis=0) +
np.arange(frames.shape[1]) * offset, color=color, ls=linestyle)
def plot_scalpgrid(scalps, sensors, locs=POS_10_5, width=None,
clim=None, cmap=None, titles=None):
'''
Plots a grid with scalpplots. Scalps contains the different scalps in the
rows, sensors contains the names for the columns of scalps, locs is a dict
that maps the sensor-names to locations.
Width determines the width of the grid that contains the plots. Cmap selects
a colormap, for example plt.cm.RdBu_r is very useful for AUC-ROC plots.
Clim is a list containing the minimim and maximum value mapped to a color.
Titles is an optional list with titles for each subplot.
Returns a list with subplots for further manipulation.
'''
scalps = np.asarray(scalps)
assert scalps.ndim == 2
nscalps = scalps.shape[0]
subplots = []
if not width:
width = int(min(8, np.ceil(np.sqrt(nscalps))))
height = int(np.ceil(nscalps/float(width)))
if not clim:
clim = [np.min(scalps), np.max(scalps)]
plt.clf()
for i in range(nscalps):
subplots.append(plt.subplot(height, width, i + 1))
plot_scalp(scalps[i], sensors, locs, clim=clim, cmap=cmap)
if titles:
<|fim_middle|>
# plot colorbar next to last scalp
bb = plt.gca().get_position()
plt.colorbar(cax=plt.axes([bb.xmax + bb.width/10, bb.ymin, bb.width/10,
bb.height]), ticks=np.linspace(clim[0], clim[1], 5).round(2))
return subplots
<｜fim▁end｜> | plt.title(titles[i]) |
<|file_name|>plots.py<|end_file_name|><｜fim▁begin｜>import matplotlib.pyplot as plt
import numpy as np
import scalpplot
from scalpplot import plot_scalp
from positions import POS_10_5
from scipy import signal
def <|fim_middle|>(frames, time=None, offset=None, color='k', linestyle='-'):
frames = np.asarray(frames)
if offset == None:
offset = np.max(np.std(frames, axis=0)) * 3
if time == None:
time = np.arange(frames.shape[0])
plt.plot(time, frames - np.mean(frames, axis=0) +
np.arange(frames.shape[1]) * offset, color=color, ls=linestyle)
def plot_scalpgrid(scalps, sensors, locs=POS_10_5, width=None,
clim=None, cmap=None, titles=None):
'''
Plots a grid with scalpplots. Scalps contains the different scalps in the
rows, sensors contains the names for the columns of scalps, locs is a dict
that maps the sensor-names to locations.
Width determines the width of the grid that contains the plots. Cmap selects
a colormap, for example plt.cm.RdBu_r is very useful for AUC-ROC plots.
Clim is a list containing the minimim and maximum value mapped to a color.
Titles is an optional list with titles for each subplot.
Returns a list with subplots for further manipulation.
'''
scalps = np.asarray(scalps)
assert scalps.ndim == 2
nscalps = scalps.shape[0]
subplots = []
if not width:
width = int(min(8, np.ceil(np.sqrt(nscalps))))
height = int(np.ceil(nscalps/float(width)))
if not clim:
clim = [np.min(scalps), np.max(scalps)]
plt.clf()
for i in range(nscalps):
subplots.append(plt.subplot(height, width, i + 1))
plot_scalp(scalps[i], sensors, locs, clim=clim, cmap=cmap)
if titles:
plt.title(titles[i])
# plot colorbar next to last scalp
bb = plt.gca().get_position()
plt.colorbar(cax=plt.axes([bb.xmax + bb.width/10, bb.ymin, bb.width/10,
bb.height]), ticks=np.linspace(clim[0], clim[1], 5).round(2))
return subplots
<｜fim▁end｜> | plot_timeseries |
<|file_name|>plots.py<|end_file_name|><｜fim▁begin｜>import matplotlib.pyplot as plt
import numpy as np
import scalpplot
from scalpplot import plot_scalp
from positions import POS_10_5
from scipy import signal
def plot_timeseries(frames, time=None, offset=None, color='k', linestyle='-'):
frames = np.asarray(frames)
if offset == None:
offset = np.max(np.std(frames, axis=0)) * 3
if time == None:
time = np.arange(frames.shape[0])
plt.plot(time, frames - np.mean(frames, axis=0) +
np.arange(frames.shape[1]) * offset, color=color, ls=linestyle)
def <|fim_middle|>(scalps, sensors, locs=POS_10_5, width=None,
clim=None, cmap=None, titles=None):
'''
Plots a grid with scalpplots. Scalps contains the different scalps in the
rows, sensors contains the names for the columns of scalps, locs is a dict
that maps the sensor-names to locations.
Width determines the width of the grid that contains the plots. Cmap selects
a colormap, for example plt.cm.RdBu_r is very useful for AUC-ROC plots.
Clim is a list containing the minimim and maximum value mapped to a color.
Titles is an optional list with titles for each subplot.
Returns a list with subplots for further manipulation.
'''
scalps = np.asarray(scalps)
assert scalps.ndim == 2
nscalps = scalps.shape[0]
subplots = []
if not width:
width = int(min(8, np.ceil(np.sqrt(nscalps))))
height = int(np.ceil(nscalps/float(width)))
if not clim:
clim = [np.min(scalps), np.max(scalps)]
plt.clf()
for i in range(nscalps):
subplots.append(plt.subplot(height, width, i + 1))
plot_scalp(scalps[i], sensors, locs, clim=clim, cmap=cmap)
if titles:
plt.title(titles[i])
# plot colorbar next to last scalp
bb = plt.gca().get_position()
plt.colorbar(cax=plt.axes([bb.xmax + bb.width/10, bb.ymin, bb.width/10,
bb.height]), ticks=np.linspace(clim[0], clim[1], 5).round(2))
return subplots
<｜fim▁end｜> | plot_scalpgrid |
<|file_name|>apps.py<|end_file_name|><｜fim▁begin｜># Copyright (c) 2016 nVentiveUX
#<｜fim▁hole｜># "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to
# the following conditions:
#
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""Application configuration"""
from django.apps import AppConfig
class ShowcaseConfig(AppConfig):
name = 'mystartupmanager.showcase'<｜fim▁end｜> | # Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the |
<|file_name|>apps.py<|end_file_name|><｜fim▁begin｜># Copyright (c) 2016 nVentiveUX
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to
# the following conditions:
#
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""Application configuration"""
from django.apps import AppConfig
class ShowcaseConfig(AppConfig):
<|fim_middle|>
<｜fim▁end｜> | name = 'mystartupmanager.showcase' |
<|file_name|>report_financial.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# This module copyright (C) 2015 Therp BV (<http://therp.nl>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import copy
from openerp import models
from openerp.addons.account.report.account_financial_report import\
report_account_common
class report_account_common_horizontal(report_account_common):
def __init__(self, cr, uid, name, context=None):<｜fim▁hole｜> super(report_account_common_horizontal, self).__init__(
cr, uid, name, context=context)
self.localcontext.update({
'get_left_lines': self.get_left_lines,
'get_right_lines': self.get_right_lines,
})
def get_lines(self, data, side=None):
data = copy.deepcopy(data)
if data['form']['used_context'] is None:
data['form']['used_context'] = {}
data['form']['used_context'].update(
account_financial_report_horizontal_side=side)
return super(report_account_common_horizontal, self).get_lines(
data)
def get_left_lines(self, data):
return self.get_lines(data, side='left')
def get_right_lines(self, data):
return self.get_lines(data, side='right')
class ReportFinancial(models.AbstractModel):
_inherit = 'report.account.report_financial'
_wrapped_report_class = report_account_common_horizontal<｜fim▁end｜> | |
<|file_name|>report_financial.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# This module copyright (C) 2015 Therp BV (<http://therp.nl>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import copy
from openerp import models
from openerp.addons.account.report.account_financial_report import\
report_account_common
class report_account_common_horizontal(report_account_common):
<|fim_middle|>
class ReportFinancial(models.AbstractModel):
_inherit = 'report.account.report_financial'
_wrapped_report_class = report_account_common_horizontal
<｜fim▁end｜> | def __init__(self, cr, uid, name, context=None):
super(report_account_common_horizontal, self).__init__(
cr, uid, name, context=context)
self.localcontext.update({
'get_left_lines': self.get_left_lines,
'get_right_lines': self.get_right_lines,
})
def get_lines(self, data, side=None):
data = copy.deepcopy(data)
if data['form']['used_context'] is None:
data['form']['used_context'] = {}
data['form']['used_context'].update(
account_financial_report_horizontal_side=side)
return super(report_account_common_horizontal, self).get_lines(
data)
def get_left_lines(self, data):
return self.get_lines(data, side='left')
def get_right_lines(self, data):
return self.get_lines(data, side='right') |
<|file_name|>report_financial.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# This module copyright (C) 2015 Therp BV (<http://therp.nl>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import copy
from openerp import models
from openerp.addons.account.report.account_financial_report import\
report_account_common
class report_account_common_horizontal(report_account_common):
def __init__(self, cr, uid, name, context=None):
<|fim_middle|>
def get_lines(self, data, side=None):
data = copy.deepcopy(data)
if data['form']['used_context'] is None:
data['form']['used_context'] = {}
data['form']['used_context'].update(
account_financial_report_horizontal_side=side)
return super(report_account_common_horizontal, self).get_lines(
data)
def get_left_lines(self, data):
return self.get_lines(data, side='left')
def get_right_lines(self, data):
return self.get_lines(data, side='right')
class ReportFinancial(models.AbstractModel):
_inherit = 'report.account.report_financial'
_wrapped_report_class = report_account_common_horizontal
<｜fim▁end｜> | super(report_account_common_horizontal, self).__init__(
cr, uid, name, context=context)
self.localcontext.update({
'get_left_lines': self.get_left_lines,
'get_right_lines': self.get_right_lines,
}) |
<|file_name|>report_financial.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# This module copyright (C) 2015 Therp BV (<http://therp.nl>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import copy
from openerp import models
from openerp.addons.account.report.account_financial_report import\
report_account_common
class report_account_common_horizontal(report_account_common):
def __init__(self, cr, uid, name, context=None):
super(report_account_common_horizontal, self).__init__(
cr, uid, name, context=context)
self.localcontext.update({
'get_left_lines': self.get_left_lines,
'get_right_lines': self.get_right_lines,
})
def get_lines(self, data, side=None):
<|fim_middle|>
def get_left_lines(self, data):
return self.get_lines(data, side='left')
def get_right_lines(self, data):
return self.get_lines(data, side='right')
class ReportFinancial(models.AbstractModel):
_inherit = 'report.account.report_financial'
_wrapped_report_class = report_account_common_horizontal
<｜fim▁end｜> | data = copy.deepcopy(data)
if data['form']['used_context'] is None:
data['form']['used_context'] = {}
data['form']['used_context'].update(
account_financial_report_horizontal_side=side)
return super(report_account_common_horizontal, self).get_lines(
data) |
<|file_name|>report_financial.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# This module copyright (C) 2015 Therp BV (<http://therp.nl>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import copy
from openerp import models
from openerp.addons.account.report.account_financial_report import\
report_account_common
class report_account_common_horizontal(report_account_common):
def __init__(self, cr, uid, name, context=None):
super(report_account_common_horizontal, self).__init__(
cr, uid, name, context=context)
self.localcontext.update({
'get_left_lines': self.get_left_lines,
'get_right_lines': self.get_right_lines,
})
def get_lines(self, data, side=None):
data = copy.deepcopy(data)
if data['form']['used_context'] is None:
data['form']['used_context'] = {}
data['form']['used_context'].update(
account_financial_report_horizontal_side=side)
return super(report_account_common_horizontal, self).get_lines(
data)
def get_left_lines(self, data):
<|fim_middle|>
def get_right_lines(self, data):
return self.get_lines(data, side='right')
class ReportFinancial(models.AbstractModel):
_inherit = 'report.account.report_financial'
_wrapped_report_class = report_account_common_horizontal
<｜fim▁end｜> | return self.get_lines(data, side='left') |
<|file_name|>report_financial.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# This module copyright (C) 2015 Therp BV (<http://therp.nl>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import copy
from openerp import models
from openerp.addons.account.report.account_financial_report import\
report_account_common
class report_account_common_horizontal(report_account_common):
def __init__(self, cr, uid, name, context=None):
super(report_account_common_horizontal, self).__init__(
cr, uid, name, context=context)
self.localcontext.update({
'get_left_lines': self.get_left_lines,
'get_right_lines': self.get_right_lines,
})
def get_lines(self, data, side=None):
data = copy.deepcopy(data)
if data['form']['used_context'] is None:
data['form']['used_context'] = {}
data['form']['used_context'].update(
account_financial_report_horizontal_side=side)
return super(report_account_common_horizontal, self).get_lines(
data)
def get_left_lines(self, data):
return self.get_lines(data, side='left')
def get_right_lines(self, data):
<|fim_middle|>
class ReportFinancial(models.AbstractModel):
_inherit = 'report.account.report_financial'
_wrapped_report_class = report_account_common_horizontal
<｜fim▁end｜> | return self.get_lines(data, side='right') |
<|file_name|>report_financial.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# This module copyright (C) 2015 Therp BV (<http://therp.nl>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import copy
from openerp import models
from openerp.addons.account.report.account_financial_report import\
report_account_common
class report_account_common_horizontal(report_account_common):
def __init__(self, cr, uid, name, context=None):
super(report_account_common_horizontal, self).__init__(
cr, uid, name, context=context)
self.localcontext.update({
'get_left_lines': self.get_left_lines,
'get_right_lines': self.get_right_lines,
})
def get_lines(self, data, side=None):
data = copy.deepcopy(data)
if data['form']['used_context'] is None:
data['form']['used_context'] = {}
data['form']['used_context'].update(
account_financial_report_horizontal_side=side)
return super(report_account_common_horizontal, self).get_lines(
data)
def get_left_lines(self, data):
return self.get_lines(data, side='left')
def get_right_lines(self, data):
return self.get_lines(data, side='right')
class ReportFinancial(models.AbstractModel):
<|fim_middle|>
<｜fim▁end｜> | _inherit = 'report.account.report_financial'
_wrapped_report_class = report_account_common_horizontal |
<|file_name|>report_financial.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# This module copyright (C) 2015 Therp BV (<http://therp.nl>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import copy
from openerp import models
from openerp.addons.account.report.account_financial_report import\
report_account_common
class report_account_common_horizontal(report_account_common):
def __init__(self, cr, uid, name, context=None):
super(report_account_common_horizontal, self).__init__(
cr, uid, name, context=context)
self.localcontext.update({
'get_left_lines': self.get_left_lines,
'get_right_lines': self.get_right_lines,
})
def get_lines(self, data, side=None):
data = copy.deepcopy(data)
if data['form']['used_context'] is None:
<|fim_middle|>
data['form']['used_context'].update(
account_financial_report_horizontal_side=side)
return super(report_account_common_horizontal, self).get_lines(
data)
def get_left_lines(self, data):
return self.get_lines(data, side='left')
def get_right_lines(self, data):
return self.get_lines(data, side='right')
class ReportFinancial(models.AbstractModel):
_inherit = 'report.account.report_financial'
_wrapped_report_class = report_account_common_horizontal
<｜fim▁end｜> | data['form']['used_context'] = {} |
<|file_name|>report_financial.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# This module copyright (C) 2015 Therp BV (<http://therp.nl>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import copy
from openerp import models
from openerp.addons.account.report.account_financial_report import\
report_account_common
class report_account_common_horizontal(report_account_common):
def <|fim_middle|>(self, cr, uid, name, context=None):
super(report_account_common_horizontal, self).__init__(
cr, uid, name, context=context)
self.localcontext.update({
'get_left_lines': self.get_left_lines,
'get_right_lines': self.get_right_lines,
})
def get_lines(self, data, side=None):
data = copy.deepcopy(data)
if data['form']['used_context'] is None:
data['form']['used_context'] = {}
data['form']['used_context'].update(
account_financial_report_horizontal_side=side)
return super(report_account_common_horizontal, self).get_lines(
data)
def get_left_lines(self, data):
return self.get_lines(data, side='left')
def get_right_lines(self, data):
return self.get_lines(data, side='right')
class ReportFinancial(models.AbstractModel):
_inherit = 'report.account.report_financial'
_wrapped_report_class = report_account_common_horizontal
<｜fim▁end｜> | __init__ |
<|file_name|>report_financial.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# This module copyright (C) 2015 Therp BV (<http://therp.nl>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import copy
from openerp import models
from openerp.addons.account.report.account_financial_report import\
report_account_common
class report_account_common_horizontal(report_account_common):
def __init__(self, cr, uid, name, context=None):
super(report_account_common_horizontal, self).__init__(
cr, uid, name, context=context)
self.localcontext.update({
'get_left_lines': self.get_left_lines,
'get_right_lines': self.get_right_lines,
})
def <|fim_middle|>(self, data, side=None):
data = copy.deepcopy(data)
if data['form']['used_context'] is None:
data['form']['used_context'] = {}
data['form']['used_context'].update(
account_financial_report_horizontal_side=side)
return super(report_account_common_horizontal, self).get_lines(
data)
def get_left_lines(self, data):
return self.get_lines(data, side='left')
def get_right_lines(self, data):
return self.get_lines(data, side='right')
class ReportFinancial(models.AbstractModel):
_inherit = 'report.account.report_financial'
_wrapped_report_class = report_account_common_horizontal
<｜fim▁end｜> | get_lines |
<|file_name|>report_financial.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# This module copyright (C) 2015 Therp BV (<http://therp.nl>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import copy
from openerp import models
from openerp.addons.account.report.account_financial_report import\
report_account_common
class report_account_common_horizontal(report_account_common):
def __init__(self, cr, uid, name, context=None):
super(report_account_common_horizontal, self).__init__(
cr, uid, name, context=context)
self.localcontext.update({
'get_left_lines': self.get_left_lines,
'get_right_lines': self.get_right_lines,
})
def get_lines(self, data, side=None):
data = copy.deepcopy(data)
if data['form']['used_context'] is None:
data['form']['used_context'] = {}
data['form']['used_context'].update(
account_financial_report_horizontal_side=side)
return super(report_account_common_horizontal, self).get_lines(
data)
def <|fim_middle|>(self, data):
return self.get_lines(data, side='left')
def get_right_lines(self, data):
return self.get_lines(data, side='right')
class ReportFinancial(models.AbstractModel):
_inherit = 'report.account.report_financial'
_wrapped_report_class = report_account_common_horizontal
<｜fim▁end｜> | get_left_lines |
<|file_name|>report_financial.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# This module copyright (C) 2015 Therp BV (<http://therp.nl>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import copy
from openerp import models
from openerp.addons.account.report.account_financial_report import\
report_account_common
class report_account_common_horizontal(report_account_common):
def __init__(self, cr, uid, name, context=None):
super(report_account_common_horizontal, self).__init__(
cr, uid, name, context=context)
self.localcontext.update({
'get_left_lines': self.get_left_lines,
'get_right_lines': self.get_right_lines,
})
def get_lines(self, data, side=None):
data = copy.deepcopy(data)
if data['form']['used_context'] is None:
data['form']['used_context'] = {}
data['form']['used_context'].update(
account_financial_report_horizontal_side=side)
return super(report_account_common_horizontal, self).get_lines(
data)
def get_left_lines(self, data):
return self.get_lines(data, side='left')
def <|fim_middle|>(self, data):
return self.get_lines(data, side='right')
class ReportFinancial(models.AbstractModel):
_inherit = 'report.account.report_financial'
_wrapped_report_class = report_account_common_horizontal
<｜fim▁end｜> | get_right_lines |
<|file_name|>__init__.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.<｜fim▁hole｜>
from ._test_base import TestBase
__all__ = ['TestBase']<｜fim▁end｜> | # Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# -------------------------------------------------------------------------- |
<|file_name|>bba880ef5bbd_add_is_loud_and_pronouns_columns_to_.py<|end_file_name|><｜fim▁begin｜>"""Add is_loud and pronouns columns to PanelApplicant
Revision ID: bba880ef5bbd
Revises: 8f8419ebcf27
Create Date: 2019-07-20 02:57:17.794469
"""
# revision identifiers, used by Alembic.
revision = 'bba880ef5bbd'
down_revision = '8f8419ebcf27'
branch_labels = None
depends_on = None
from alembic import op
import sqlalchemy as sa
try:
is_sqlite = op.get_context().dialect.name == 'sqlite'
except Exception:
is_sqlite = False
if is_sqlite:
op.get_context().connection.execute('PRAGMA foreign_keys=ON;')
utcnow_server_default = "(datetime('now', 'utc'))"
else:
utcnow_server_default = "timezone('utc', current_timestamp)"
def sqlite_column_reflect_listener(inspector, table, column_info):
"""Adds parenthesis around SQLite datetime defaults for utcnow."""<｜fim▁hole｜> if column_info['default'] == "datetime('now', 'utc')":
column_info['default'] = utcnow_server_default
sqlite_reflect_kwargs = {
'listeners': [('column_reflect', sqlite_column_reflect_listener)]
}
# ===========================================================================
# HOWTO: Handle alter statements in SQLite
#
# def upgrade():
# if is_sqlite:
# with op.batch_alter_table('table_name', reflect_kwargs=sqlite_reflect_kwargs) as batch_op:
# batch_op.alter_column('column_name', type_=sa.Unicode(), server_default='', nullable=False)
# else:
# op.alter_column('table_name', 'column_name', type_=sa.Unicode(), server_default='', nullable=False)
#
# ===========================================================================
def upgrade():
op.add_column('panel_applicant', sa.Column('other_pronouns', sa.Unicode(), server_default='', nullable=False))
op.add_column('panel_applicant', sa.Column('pronouns', sa.Unicode(), server_default='', nullable=False))
op.add_column('panel_application', sa.Column('is_loud', sa.Boolean(), server_default='False', nullable=False))
def downgrade():
op.drop_column('panel_application', 'is_loud')
op.drop_column('panel_applicant', 'pronouns')
op.drop_column('panel_applicant', 'other_pronouns')<｜fim▁end｜> | |
<|file_name|>bba880ef5bbd_add_is_loud_and_pronouns_columns_to_.py<|end_file_name|><｜fim▁begin｜>"""Add is_loud and pronouns columns to PanelApplicant
Revision ID: bba880ef5bbd
Revises: 8f8419ebcf27
Create Date: 2019-07-20 02:57:17.794469
"""
# revision identifiers, used by Alembic.
revision = 'bba880ef5bbd'
down_revision = '8f8419ebcf27'
branch_labels = None
depends_on = None
from alembic import op
import sqlalchemy as sa
try:
is_sqlite = op.get_context().dialect.name == 'sqlite'
except Exception:
is_sqlite = False
if is_sqlite:
op.get_context().connection.execute('PRAGMA foreign_keys=ON;')
utcnow_server_default = "(datetime('now', 'utc'))"
else:
utcnow_server_default = "timezone('utc', current_timestamp)"
def sqlite_column_reflect_listener(inspector, table, column_info):
<|fim_middle|>
sqlite_reflect_kwargs = {
'listeners': [('column_reflect', sqlite_column_reflect_listener)]
}
# ===========================================================================
# HOWTO: Handle alter statements in SQLite
#
# def upgrade():
# if is_sqlite:
# with op.batch_alter_table('table_name', reflect_kwargs=sqlite_reflect_kwargs) as batch_op:
# batch_op.alter_column('column_name', type_=sa.Unicode(), server_default='', nullable=False)
# else:
# op.alter_column('table_name', 'column_name', type_=sa.Unicode(), server_default='', nullable=False)
#
# ===========================================================================
def upgrade():
op.add_column('panel_applicant', sa.Column('other_pronouns', sa.Unicode(), server_default='', nullable=False))
op.add_column('panel_applicant', sa.Column('pronouns', sa.Unicode(), server_default='', nullable=False))
op.add_column('panel_application', sa.Column('is_loud', sa.Boolean(), server_default='False', nullable=False))
def downgrade():
op.drop_column('panel_application', 'is_loud')
op.drop_column('panel_applicant', 'pronouns')
op.drop_column('panel_applicant', 'other_pronouns')
<｜fim▁end｜> | """Adds parenthesis around SQLite datetime defaults for utcnow."""
if column_info['default'] == "datetime('now', 'utc')":
column_info['default'] = utcnow_server_default |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.