def test_remove_brackets():
x = ' [] [] asdf [] '
nose.tools.assert_equal(reg.remove_brackets(x), 'asdf')
def ldscore(args, log):
'''
Wrapper function for estimating l1, l1^2, l2 and l4 (+ optionally standard errors) from
reference panel genotypes.
Annot format is
chr snp bp cm <annotations>
'''
if args.bfile:
snp_file, snp_obj = args.bfile+'.bim', ps.PlinkBIMFile
ind_file, ind_obj = args.bfile+'.fam', ps.PlinkFAMFile
array_file, array_obj = args.bfile+'.bed', ld.PlinkBEDFile
# read bim/snp
array_snps = snp_obj(snp_file)
m = len(array_snps.IDList)
log.log('Read list of {m} SNPs from {f}'.format(m=m, f=snp_file))
if args.annot is not None: # read --annot
try:
annot = ps.AnnotFile(args.annot)
n_annot, ma = len(annot.df.columns) - 4, len(annot.df)
log.log("Read {A} annotations for {M} SNPs from {f}".format(f=args.annot,
A=n_annot, M=ma))
annot_matrix = np.array(annot.df.iloc[:,4:])
annot_colnames = annot.df.columns[4:]
keep_snps = None
if np.any(annot.df.SNP.values != array_snps.df.SNP.values):
raise ValueError('The .annot file must contain the same SNPs in the same'+\
' order as the .bim file.')
except Exception:
log.log('Error parsing .annot file')
raise
elif args.extract is not None: # --extract
keep_snps = __filter__(args.extract, 'SNPs', 'include', array_snps)
annot_matrix, annot_colnames, n_annot = None, None, 1
elif args.cts_bin is not None and args.cts_breaks is not None: # --cts-bin
cts_fnames = sumstats._splitp(args.cts_bin) # read filenames
args.cts_breaks = args.cts_breaks.replace('N','-') # replace N with negative sign
try: # split on x
breaks = [[float(x) for x in y.split(',')] for y in args.cts_breaks.split('x')]
except ValueError as e:
raise ValueError('--cts-breaks must be a comma-separated list of numbers: '
+str(e.args))
if len(breaks) != len(cts_fnames):
raise ValueError('Need to specify one set of breaks for each file in --cts-bin.')
if args.cts_names:
cts_colnames = [str(x) for x in args.cts_names.split(',')]
if len(cts_colnames) != len(cts_fnames):
msg = 'Must specify either no --cts-names or one value for each file in --cts-bin.'
raise ValueError(msg)
else:
cts_colnames = ['ANNOT'+str(i) for i in xrange(len(cts_fnames))]
log.log('Reading numbers with which to bin SNPs from {F}'.format(F=args.cts_bin))
cts_levs = []
full_labs = []
for i,fh in enumerate(cts_fnames):
vec = ps.read_cts(cts_fnames[i], array_snps.df.SNP.values)
max_cts = np.max(vec)
min_cts = np.min(vec)
cut_breaks = list(breaks[i])
name_breaks = list(cut_breaks)
if np.all(cut_breaks >= max_cts) or np.all(cut_breaks <= min_cts):
raise ValueError('All breaks lie outside the range of the cts variable.')
if np.all(cut_breaks <= max_cts):
name_breaks.append(max_cts)
cut_breaks.append(max_cts+1)
if np.all(cut_breaks >= min_cts):
name_breaks.append(min_cts)
cut_breaks.append(min_cts-1)
name_breaks.sort()
cut_breaks.sort()
n_breaks = len(cut_breaks)
# so that col names are consistent across chromosomes with different max vals
name_breaks[0] = 'min'
name_breaks[-1] = 'max'
name_breaks = [str(x) for x in name_breaks]
labs = [name_breaks[i]+'_'+name_breaks[i+1] for i in xrange(n_breaks-1)]
cut_vec = pd.Series(pd.cut(vec, bins=cut_breaks, labels=labs))
cts_levs.append(cut_vec)
full_labs.append(labs)
annot_matrix = pd.concat(cts_levs, axis=1)
annot_matrix.columns = cts_colnames
# crosstab -- for now we keep empty columns
annot_matrix = pd.crosstab(annot_matrix.index,
[annot_matrix[i] for i in annot_matrix.columns], dropna=False,
colnames=annot_matrix.columns)
# add missing columns
if len(cts_colnames) > 1:
for x in product(*full_labs):
if x not in annot_matrix.columns:
annot_matrix[x] = 0
else:
for x in full_labs[0]:
if x not in annot_matrix.columns:
annot_matrix[x] = 0
annot_matrix = annot_matrix[sorted(annot_matrix.columns, key=annot_sort_key)]
if len(cts_colnames) > 1:
# flatten multi-index
annot_colnames = ['_'.join([cts_colnames[i]+'_'+b for i,b in enumerate(c)])
for c in annot_matrix.columns]
else:
annot_colnames = [cts_colnames[0]+'_'+b for b in annot_matrix.columns]
annot_matrix = np.matrix(annot_matrix)
keep_snps = None
n_annot = len(annot_colnames)
if np.any(np.sum(annot_matrix, axis=1) == 0):
# This exception should never be raised. For debugging only.
raise ValueError('Some SNPs have no annotation in --cts-bin. This is a bug!')
else:
annot_matrix, annot_colnames, keep_snps = None, None, None,
n_annot = 1
# read fam
array_indivs = ind_obj(ind_file)
n = len(array_indivs.IDList)
log.log('Read list of {n} individuals from {f}'.format(n=n, f=ind_file))
# read keep_indivs
if args.keep:
keep_indivs = __filter__(args.keep, 'individuals', 'include', array_indivs)
else:
keep_indivs = None
# read genotype array
log.log('Reading genotypes from {fname}'.format(fname=array_file))
geno_array = array_obj(array_file, n, array_snps, keep_snps=keep_snps,
keep_indivs=keep_indivs, mafMin=args.maf)
# filter annot_matrix down to only SNPs passing MAF cutoffs
if annot_matrix is not None:
annot_keep = geno_array.kept_snps
annot_matrix = annot_matrix[annot_keep,:]
# determine block widths
x = np.array((args.ld_wind_snps, args.ld_wind_kb, args.ld_wind_cm), dtype=bool)
if np.sum(x) != 1:
raise ValueError('Must specify exactly one --ld-wind option')
if args.ld_wind_snps:
max_dist = args.ld_wind_snps
coords = np.array(xrange(geno_array.m))
elif args.ld_wind_kb:
max_dist = args.ld_wind_kb*1000
coords = np.array(array_snps.df['BP'])[geno_array.kept_snps]
elif args.ld_wind_cm:
max_dist = args.ld_wind_cm
coords = np.array(array_snps.df['CM'])[geno_array.kept_snps]
block_left = ld.getBlockLefts(coords, max_dist)
if block_left[len(block_left)-1] == 0 and not args.yes_really:
error_msg = 'Do you really want to compute whole-chomosome LD Score? If so, set the '
error_msg += '--yes-really flag (warning: it will use a lot of time / memory)'
raise ValueError(error_msg)
scale_suffix = ''
if args.pq_exp is not None:
log.log('Computing LD with pq ^ {S}.'.format(S=args.pq_exp))
msg = 'Note that LD Scores with pq raised to a nonzero power are'
msg += 'not directly comparable to normal LD Scores.'
log.log(msg)
scale_suffix = '_S{S}'.format(S=args.pq_exp)
pq = np.matrix(geno_array.maf*(1-geno_array.maf)).reshape((geno_array.m, 1))
pq = np.power(pq, args.pq_exp)
if annot_matrix is not None:
annot_matrix = np.multiply(annot_matrix, pq)
else:
annot_matrix = pq
log.log("Estimating LD Score.")
lN = geno_array.ldScoreVarBlocks(block_left, args.chunk_size, annot=annot_matrix)
col_prefix = "L2"; file_suffix = "l2"
if n_annot == 1:
ldscore_colnames = [col_prefix+scale_suffix]
else:
ldscore_colnames = [y+col_prefix+scale_suffix for y in annot_colnames]
# print .ldscore. Output columns: CHR, BP, RS, [LD Scores]
out_fname = args.out + '.' + file_suffix + '.ldscore'
new_colnames = geno_array.colnames + ldscore_colnames
df = pd.DataFrame.from_records(np.c_[geno_array.df, lN])
df.columns = new_colnames
if args.print_snps:
if args.print_snps.endswith('gz'):
print_snps = pd.read_csv(args.print_snps, header=None, compression='gzip')
elif args.print_snps.endswith('bz2'):
print_snps = pd.read_csv(args.print_snps, header=None, compression='bz2')
else:
print_snps = pd.read_csv(args.print_snps, header=None)
if len(print_snps.columns) > 1:
raise ValueError('--print-snps must refer to a file with a one column of SNP IDs.')
log.log('Reading list of {N} SNPs for which to print LD Scores from {F}'.format(\
F=args.print_snps, N=len(print_snps)))
print_snps.columns=['SNP']
df = df.ix[df.SNP.isin(print_snps.SNP),:]
if len(df) == 0:
raise ValueError('After merging with --print-snps, no SNPs remain.')
else:
msg = 'After merging with --print-snps, LD Scores for {N} SNPs will be printed.'
log.log(msg.format(N=len(df)))
l2_suffix = '.gz'
log.log("Writing LD Scores for {N} SNPs to {f}.gz".format(f=out_fname, N=len(df)))
df.drop(['CM','MAF'], axis=1).to_csv(out_fname, sep="\t", header=True, index=False,
float_format='%.3f')
call(['gzip', '-f', out_fname])
if annot_matrix is not None:
M = np.atleast_1d(np.squeeze(np.asarray(np.sum(annot_matrix, axis=0))))
ii = geno_array.maf > 0.05
M_5_50 = np.atleast_1d(np.squeeze(np.asarray(np.sum(annot_matrix[ii,:], axis=0))))
else:
M = [geno_array.m]
M_5_50 = [np.sum(geno_array.maf > 0.05)]
# print .M
fout_M = open(args.out + '.'+ file_suffix +'.M','wb')
print >>fout_M, '\t'.join(map(str,M))
fout_M.close()
# print .M_5_50
fout_M_5_50 = open(args.out + '.'+ file_suffix +'.M_5_50','wb')
print >>fout_M_5_50, '\t'.join(map(str,M_5_50))
fout_M_5_50.close()
# print annot matrix
if (args.cts_bin is not None) and not args.no_print_annot:
out_fname_annot = args.out + '.annot'
new_colnames = geno_array.colnames + ldscore_colnames
annot_df = pd.DataFrame(np.c_[geno_array.df, annot_matrix])
annot_df.columns = new_colnames
del annot_df['MAF']
log.log("Writing annot matrix produced by --cts-bin to {F}".format(F=out_fname+'.gz'))
annot_df.to_csv(out_fname_annot, sep="\t", header=True, index=False)
call(['gzip', '-f', out_fname_annot])
# print LD Score summary
pd.set_option('display.max_rows', 200)
log.log('\nSummary of LD Scores in {F}'.format(F=out_fname+l2_suffix))
t = df.ix[:,4:].describe()
log.log( t.ix[1:,:] )
np.seterr(divide='ignore', invalid='ignore') # print NaN instead of weird errors
# print correlation matrix including all LD Scores and sample MAF
log.log('')
log.log('MAF/LD Score Correlation Matrix')
log.log( df.ix[:,4:].corr() )
# print condition number
if n_annot > 1: # condition number of a column vector w/ nonzero var is trivially one
log.log('\nLD Score Matrix Condition Number')
cond_num = np.linalg.cond(df.ix[:,5:])
log.log( reg.remove_brackets(str(np.matrix(cond_num))) )
if cond_num > 10000:
log.log('WARNING: ill-conditioned LD Score Matrix!')
# summarize annot matrix if there is one
if annot_matrix is not None:
# covariance matrix
x = pd.DataFrame(annot_matrix, columns=annot_colnames)
log.log('\nAnnotation Correlation Matrix')
log.log( x.corr() )
# column sums
log.log('\nAnnotation Matrix Column Sums')
log.log(_remove_dtype(x.sum(axis=0)))
# row sums
log.log('\nSummary of Annotation Matrix Row Sums')
row_sums = x.sum(axis=1).describe()
log.log(_remove_dtype(row_sums))
np.seterr(divide='raise', invalid='raise')
def ldscore(args, log):
'''
Wrapper function for estimating l1, l1^2, l2 and l4 (+ optionally standard errors) from
reference panel genotypes.
Annot format is
chr snp bp cm <annotations>
'''
if args.bfile:
snp_file, snp_obj = args.bfile + '.bim', ps.PlinkBIMFile
ind_file, ind_obj = args.bfile + '.fam', ps.PlinkFAMFile
array_file, array_obj = args.bfile + '.bed', ld.PlinkBEDFile
# read bim/snp
array_snps = snp_obj(snp_file)
m = len(array_snps.IDList)
log.log('Read list of {m} SNPs from {f}'.format(m=m, f=snp_file))
if args.annot is not None: # read --annot
try:
if args.thin_annot: # annot file has only annotations
annot = ps.ThinAnnotFile(args.annot)
n_annot, ma = len(annot.df.columns), len(annot.df)
log.log("Read {A} annotations for {M} SNPs from {f}".format(
f=args.annot, A=n_annot, M=ma))
annot_matrix = annot.df.values
annot_colnames = annot.df.columns
keep_snps = None
else:
annot = ps.AnnotFile(args.annot)
n_annot, ma = len(annot.df.columns) - 4, len(annot.df)
log.log("Read {A} annotations for {M} SNPs from {f}".format(
f=args.annot, A=n_annot, M=ma))
annot_matrix = np.array(annot.df.iloc[:, 4:])
annot_colnames = annot.df.columns[4:]
keep_snps = None
if np.any(annot.df.SNP.values != array_snps.df.SNP.values):
raise ValueError('The .annot file must contain the same SNPs in the same'+\
' order as the .bim file.')
except Exception:
log.log('Error parsing .annot file')
raise
elif args.extract is not None: # --extract
keep_snps = __filter__(args.extract, 'SNPs', 'include', array_snps)
annot_matrix, annot_colnames, n_annot = None, None, 1
elif args.cts_bin is not None and args.cts_breaks is not None: # --cts-bin
cts_fnames = sumstats._splitp(args.cts_bin) # read filenames
args.cts_breaks = args.cts_breaks.replace(
'N', '-') # replace N with negative sign
try: # split on x
breaks = [[float(x) for x in y.split(',')]
for y in args.cts_breaks.split('x')]
except ValueError as e:
raise ValueError(
'--cts-breaks must be a comma-separated list of numbers: ' +
str(e.args))
if len(breaks) != len(cts_fnames):
raise ValueError(
'Need to specify one set of breaks for each file in --cts-bin.'
)
if args.cts_names:
cts_colnames = [str(x) for x in args.cts_names.split(',')]
if len(cts_colnames) != len(cts_fnames):
msg = 'Must specify either no --cts-names or one value for each file in --cts-bin.'
raise ValueError(msg)
else:
cts_colnames = ['ANNOT' + str(i) for i in xrange(len(cts_fnames))]
log.log('Reading numbers with which to bin SNPs from {F}'.format(
F=args.cts_bin))
cts_levs = []
full_labs = []
for i, fh in enumerate(cts_fnames):
vec = ps.read_cts(cts_fnames[i], array_snps.df.SNP.values)
max_cts = np.max(vec)
min_cts = np.min(vec)
cut_breaks = list(breaks[i])
name_breaks = list(cut_breaks)
if np.all(cut_breaks >= max_cts) or np.all(cut_breaks <= min_cts):
raise ValueError(
'All breaks lie outside the range of the cts variable.')
if np.all(cut_breaks <= max_cts):
name_breaks.append(max_cts)
cut_breaks.append(max_cts + 1)
if np.all(cut_breaks >= min_cts):
name_breaks.append(min_cts)
cut_breaks.append(min_cts - 1)
name_breaks.sort()
cut_breaks.sort()
n_breaks = len(cut_breaks)
# so that col names are consistent across chromosomes with different max vals
name_breaks[0] = 'min'
name_breaks[-1] = 'max'
name_breaks = [str(x) for x in name_breaks]
labs = [
name_breaks[i] + '_' + name_breaks[i + 1]
for i in xrange(n_breaks - 1)
]
cut_vec = pd.Series(pd.cut(vec, bins=cut_breaks, labels=labs))
cts_levs.append(cut_vec)
full_labs.append(labs)
annot_matrix = pd.concat(cts_levs, axis=1)
annot_matrix.columns = cts_colnames
# crosstab -- for now we keep empty columns
annot_matrix = pd.crosstab(
annot_matrix.index,
[annot_matrix[i] for i in annot_matrix.columns],
dropna=False,
colnames=annot_matrix.columns)
# add missing columns
if len(cts_colnames) > 1:
for x in product(*full_labs):
if x not in annot_matrix.columns:
annot_matrix[x] = 0
else:
for x in full_labs[0]:
if x not in annot_matrix.columns:
annot_matrix[x] = 0
annot_matrix = annot_matrix[sorted(annot_matrix.columns,
key=annot_sort_key)]
if len(cts_colnames) > 1:
# flatten multi-index
annot_colnames = [
'_'.join([cts_colnames[i] + '_' + b for i, b in enumerate(c)])
for c in annot_matrix.columns
]
else:
annot_colnames = [
cts_colnames[0] + '_' + b for b in annot_matrix.columns
]
annot_matrix = np.matrix(annot_matrix)
keep_snps = None
n_annot = len(annot_colnames)
if np.any(np.sum(annot_matrix, axis=1) == 0):
# This exception should never be raised. For debugging only.
raise ValueError(
'Some SNPs have no annotation in --cts-bin. This is a bug!')
else:
annot_matrix, annot_colnames, keep_snps = None, None, None,
n_annot = 1
# read fam
array_indivs = ind_obj(ind_file)
n = len(array_indivs.IDList)
log.log('Read list of {n} individuals from {f}'.format(n=n, f=ind_file))
# read keep_indivs
if args.keep:
keep_indivs = __filter__(args.keep, 'individuals', 'include',
array_indivs)
else:
keep_indivs = None
# read genotype array
log.log('Reading genotypes from {fname}'.format(fname=array_file))
geno_array = array_obj(array_file,
n,
array_snps,
keep_snps=keep_snps,
keep_indivs=keep_indivs,
mafMin=args.maf)
# filter annot_matrix down to only SNPs passing MAF cutoffs
if annot_matrix is not None:
annot_keep = geno_array.kept_snps
annot_matrix = annot_matrix[annot_keep, :]
# determine block widths
x = np.array((args.ld_wind_snps, args.ld_wind_kb, args.ld_wind_cm),
dtype=bool)
if np.sum(x) != 1:
raise ValueError('Must specify exactly one --ld-wind option')
if args.ld_wind_snps:
max_dist = args.ld_wind_snps
coords = np.array(xrange(geno_array.m))
elif args.ld_wind_kb:
max_dist = args.ld_wind_kb * 1000
coords = np.array(array_snps.df['BP'])[geno_array.kept_snps]
elif args.ld_wind_cm:
max_dist = args.ld_wind_cm
coords = np.array(array_snps.df['CM'])[geno_array.kept_snps]
block_left = ld.getBlockLefts(coords, max_dist)
if block_left[len(block_left) - 1] == 0 and not args.yes_really:
error_msg = 'Do you really want to compute whole-chomosome LD Score? If so, set the '
error_msg += '--yes-really flag (warning: it will use a lot of time / memory)'
raise ValueError(error_msg)
scale_suffix = ''
if args.pq_exp is not None:
log.log('Computing LD with pq ^ {S}.'.format(S=args.pq_exp))
msg = 'Note that LD Scores with pq raised to a nonzero power are'
msg += 'not directly comparable to normal LD Scores.'
log.log(msg)
scale_suffix = '_S{S}'.format(S=args.pq_exp)
pq = np.matrix(geno_array.maf * (1 - geno_array.maf)).reshape(
(geno_array.m, 1))
pq = np.power(pq, args.pq_exp)
if annot_matrix is not None:
annot_matrix = np.multiply(annot_matrix, pq)
else:
annot_matrix = pq
log.log("Estimating LD Score.")
lN = geno_array.ldScoreVarBlocks(block_left,
args.chunk_size,
annot=annot_matrix)
col_prefix = "L2"
file_suffix = "l2"
if n_annot == 1:
ldscore_colnames = [col_prefix + scale_suffix]
else:
ldscore_colnames = [
y + col_prefix + scale_suffix for y in annot_colnames
]
# print .ldscore. Output columns: CHR, BP, RS, [LD Scores]
out_fname = args.out + '.' + file_suffix + '.ldscore'
new_colnames = geno_array.colnames + ldscore_colnames
df = pd.DataFrame.from_records(np.c_[geno_array.df, lN])
df.columns = new_colnames
if args.print_snps:
if args.print_snps.endswith('gz'):
print_snps = pd.read_csv(args.print_snps,
header=None,
compression='gzip')
elif args.print_snps.endswith('bz2'):
print_snps = pd.read_csv(args.print_snps,
header=None,
compression='bz2')
else:
print_snps = pd.read_csv(args.print_snps, header=None)
if len(print_snps.columns) > 1:
raise ValueError(
'--print-snps must refer to a file with a one column of SNP IDs.'
)
log.log('Reading list of {N} SNPs for which to print LD Scores from {F}'.format(\
F=args.print_snps, N=len(print_snps)))
print_snps.columns = ['SNP']
df = df.ix[df.SNP.isin(print_snps.SNP), :]
if len(df) == 0:
raise ValueError(
'After merging with --print-snps, no SNPs remain.')
else:
msg = 'After merging with --print-snps, LD Scores for {N} SNPs will be printed.'
log.log(msg.format(N=len(df)))
l2_suffix = '.gz'
log.log("Writing LD Scores for {N} SNPs to {f}.gz".format(f=out_fname,
N=len(df)))
df.drop(['CM', 'MAF'], axis=1).to_csv(out_fname,
sep="\t",
header=True,
index=False,
float_format='%.3f')
call(['gzip', '-f', out_fname])
if annot_matrix is not None:
M = np.atleast_1d(np.squeeze(np.asarray(np.sum(annot_matrix, axis=0))))
ii = geno_array.maf > 0.05
M_5_50 = np.atleast_1d(
np.squeeze(np.asarray(np.sum(annot_matrix[ii, :], axis=0))))
else:
M = [geno_array.m]
M_5_50 = [np.sum(geno_array.maf > 0.05)]
# print .M
fout_M = open(args.out + '.' + file_suffix + '.M', 'wb')
print >> fout_M, '\t'.join(map(str, M))
fout_M.close()
# print .M_5_50
fout_M_5_50 = open(args.out + '.' + file_suffix + '.M_5_50', 'wb')
print >> fout_M_5_50, '\t'.join(map(str, M_5_50))
fout_M_5_50.close()
# print annot matrix
if (args.cts_bin is not None) and not args.no_print_annot:
out_fname_annot = args.out + '.annot'
new_colnames = geno_array.colnames + ldscore_colnames
annot_df = pd.DataFrame(np.c_[geno_array.df, annot_matrix])
annot_df.columns = new_colnames
del annot_df['MAF']
log.log("Writing annot matrix produced by --cts-bin to {F}".format(
F=out_fname + '.gz'))
annot_df.to_csv(out_fname_annot, sep="\t", header=True, index=False)
call(['gzip', '-f', out_fname_annot])
# print LD Score summary
pd.set_option('display.max_rows', 200)
log.log('\nSummary of LD Scores in {F}'.format(F=out_fname + l2_suffix))
t = df.ix[:, 4:].describe()
log.log(t.ix[1:, :])
np.seterr(divide='ignore',
invalid='ignore') # print NaN instead of weird errors
# print correlation matrix including all LD Scores and sample MAF
log.log('')
log.log('MAF/LD Score Correlation Matrix')
log.log(df.ix[:, 4:].corr())
# print condition number
if n_annot > 1: # condition number of a column vector w/ nonzero var is trivially one
log.log('\nLD Score Matrix Condition Number')
cond_num = np.linalg.cond(df.ix[:, 5:])
log.log(reg.remove_brackets(str(np.matrix(cond_num))))
if cond_num > 10000:
log.log('WARNING: ill-conditioned LD Score Matrix!')
# summarize annot matrix if there is one
if annot_matrix is not None:
# covariance matrix
x = pd.DataFrame(annot_matrix, columns=annot_colnames)
log.log('\nAnnotation Correlation Matrix')
log.log(x.corr())
# column sums
log.log('\nAnnotation Matrix Column Sums')
log.log(_remove_dtype(x.sum(axis=0)))
# row sums
log.log('\nSummary of Annotation Matrix Row Sums')
row_sums = x.sum(axis=1).describe()
log.log(_remove_dtype(row_sums))
np.seterr(divide='raise', invalid='raise')
def test_remove_brackets():
x = ' [] [] asdf [] '
nose.tools.assert_equal(reg.remove_brackets(x), 'asdf')