def main(src, ovr = 0):
from glob import glob as gg
for fi in gg(pt.join(src, '*.pgz')):
fo = pt.splitext(fi)[0] + '.rds'
if pt.exists(fo) and not ovr:
print 'exists:', fo
else:
__enc2rds__(fi, fo)
def main(datenpfad):
for textdatei in gg(datenpfad):
print(textdatei)
text = read_textfile(textdatei)
lowercase = make_lowercase(text)
cleantext = remove_punctuation(lowercase)
print(cleantext)
print("Fertig.")
def main(textfolder):
allresults = {}
for textfile in gg(textfolder):
filename = os.path.basename(textfile).split(".")[0]
text = read_textfile(textfile)
text = clean_text(text)
textstats = get_textstats(text)
allresults[filename] = textstats
print(allresults)
def __sample_wm__(wrk):
"""
given a list of center vertices {cvs}, and hemispheres {hms},
pick regions from WM surface across subject in {src}
"""
## fetch working specifications
src = wrk['src'] # source directory with subjects
dst = wrk['dst'] # target filenames
hms = wrk['hms'] # hemispheres
cvs = wrk['cvs'] # center vertices
nbs = wrk['nbs'] # neighbor table for each vertex
sz = wrk['sz'] # region size
## lists of output path, vertex indices, and connetion matrices
lfo, lvi, lcn = [], [], []
for hm, nb, cv in izip(hms, nbs, cvs):
vi, cn = __neighbor__(nb, cv, sz)
lvi.append(vi)
lcn.append(cn)
lfo.append(pt.join(dst, '{}{:05X}'.format(hm, cv)))
## lists of surfaces to be sampled, and subjects
lsf, lsb = [[] for i in xrange(len(lvi))], []
## iterate all subjects
print 'xt: sample ', len(lvi), 'WM areas from ', src, ':'
for fn in gg(pt.join(src, '*')):
if not fn.endswith('npz'):
continue
lsb.append(pt.basename(fn).split('.')[0])
print lsb[-1]
sys.stdout.flush()
wm = np.load(fn)
## sample surfaces for subject {sb}
## si: surface index, hm: hemisphere, vi: vertex indices
for si, hm, vi in izip(xrange(len(lvi)), hms, lvi):
lsf[si].append(wm[hm][vi])
if not len(lsb) < 5:
break
## write the samples to file in numpy format.
print 'xt: write WM samples to ', dst, ':'
sys.stdout.flush()
sbj = np.array(lsb)
for sf, vi, cn, fo in izip(lsf, lvi, lcn, lfo):
np.savez_compressed(fo + '.npz', sbj=sbj, vtx=np.vstack(sf), cmx=cn)
vi = ['{:05X}'.format(i) for i in vi]
__save_rds__(sf, lsb, vi, cn, fo + '.rds')
print fo + ": created"
sys.stdout.flush()
print 'xt: success'
sys.stdout.flush()
def get_pk(src, idx = 0):
""" get data from pickle """
if pt.isdir(src):
fn = gg(pt.join(src, "*"))[idx]
else:
fn = src
with open(fn, 'rb') as fp:
obj = cPickle.load(fp)
print fn + ": fetched"
return obj
def apec(src, dst, sn, hm, ovr=0):
"""
given a directory of subjects, extract anatomical regions
src: subject source
dst: where to put extracted regions
sn: region serial number, 2~35
hm: hemesphere, lh or rh
"""
## output path
fo = pt.join(dst, '{}{:02d}.npz'.format(hm, sn))
if pt.exists(fo) and not ovr:
print 'exists:', fo
return
## fetch anatomical peceration table
at = np.load('apec.npz')
## vertex indices
vi = (at[hm]==sn).nonzero()[0]
## anatomy region id and name
id, nm = at['tb'][sn]['id'], at['tb'][sn]['nm']
## surface, and subject index
vt, sb = [], []
## iterate all subjects
print 'xt: extract', hm, at['tb'][sn]['nm'], 'from ', src, ':'
for fn in gg(pt.join(src, '*.npz')):
sb.append(pt.basename(fn).split('.')[0])
print sb[-1]
sys.stdout.flush()
wm = np.load(fn)
## extract surfaces for subject {sb}
## hm: hemisphere, vi: vertex indices
vt.append(wm[hm][vi])
wm.close()
## write the samples to file in numpy format.
print 'xt: write surface to ', dst
sys.stdout.flush()
vt=np.vstack(vt)
sb=np.array(sb)
np.savez_compressed(
fo, sb=sb, vt=vt, vi=vi, hm=hm, sn=sn, id=id, nm=nm)
print 'xt: success'
sys.stdout.flush()
def main(textfolder):
for textfile in gg(textfolder):
text = read_textfile(textfile)
text = clean_text(text)
textstats = get_textstats(text)
def itr_fn(src = "", fmt = 'n', flt = None, drop = True):
"""
filename iterator
drop: drop list structure if only one file attribute
is returned.
format code:
n: file name, N: absolute file name
c: core name, C: absolute core name
b: base name, B: absolute base name
d: directory, D: absolute directory
e: extension, E: absolute extension
"""
src = resolve_path(src)
if pt.isdir(src):
src = pt.join(src, "*")
if flt == None:
flt = lambda w: True
i = 0
for fn in gg(src):
if not flt(fn):
continue
rt = []
for c in fmt:
if c == 'i':
r = i
elif c == 'n':
r = fn
elif c == 'N': # absolute filename
r = pt.abspath(fn)
elif c == 'C': # absolute corename
r = pt.abspath(fn).split('.')[0]
elif c == 'c': # ralative corename
r = pt.basename(fn).split('.')[0]
elif c == 'B': # basename.extension
r = pt.basename(pt.abspath(fn))
elif c == 'b': # basename
r = pt.basename(fn)
elif c == 'D': # absolute directory
r = pt.dirname(pt.abspath(fn))
elif c == 'd': # relative directory
r = pt.dirname(fn)
elif c == 'e': # extension(s)
r = pt.basename(fn).split('.')[1:]
if len(r) == 1:
r = r[0]
if len(r) == 0:
r = None
elif c == 'E':
r = pt.basename(fn).split('.')[1:]
if len(r) > 0:
r = r[-1]
if len(r) == 0:
r = None
else:
continue
rt.append(r)
i += 1
if drop and len(rt) == 1:
yield rt[0]
else:
yield rt
def num_pk(src):
return len(gg(src))
def glob(pathname, recursive=False):
return gg(pathname)
plt.clf()
if __name__ == "__main__":
niftipath = str(sys.argv[1])
mnipath = str(sys.argv[2])
ortho = str(sys.argv[3])
nRows = int(sys.argv[4])
nCuts = int(sys.argv[5])
showLRannot = bool(int(sys.argv[6]))
figLayout = str(sys.argv[7])
threshpos = int(sys.argv[8])
threshneg = int(sys.argv[9])
findOptimalCut = bool(int(sys.argv[10]))
imageType = str(sys.argv[11])
# Go through all the files in the data folder if requested
if niftipath == 'data':
fileList = gg('data/*.nii*')
for fpath in fileList:
for o in list(ortho):
plotGlassbrainSlices(
fpath, mnipath, o, nRows, nCuts, threshpos, threshneg,
figLayout, showLRannot, findOptimalCut, imageType)
else:
for o in list(ortho):
plotGlassbrainSlices(
niftipath, mnipath, o, nRows, nCuts, threshpos, threshneg,
figLayout, showLRannot, findOptimalCut, imageType)
def firstGlob(*args, ext=None):
path = getPath(*args, ext=ext)
path = (gg(path, recursive=True) if "**" in args else gg(path))
path = (path[0] if len(path) > 0 else None)
return path
def recursiveGlob(*args, ext=None):
path = getPath(*args, ext=ext)
pathList = sorted(gg(path, recursive=True))
pathList = (pathList if len(pathList) > 0 else None)
return pathList
def glob(*args, ext=None):
path = getPath(*args, ext=ext)
pathList = sorted(gg(path))
pathList = (pathList if len(pathList) > 0 else None)
return pathList
# a = gg(pathname='Test/*.json')
#
# print(a)
#
# df = pd.DataFrame()
#
# print(df)
#
# for f in a:
# tmp = pd.read_json(f, orient='index')
# df = pd.concat([df, tmp], axis=0, ignore_index=True)
#
# print(df)
# with open('Test/'+'test1.json') as g:
# read_data = pd.read_json(g, orient='index')
# print(read_data)
# Use glob to make a list of json files in the TikTok_Test directory
b = gg(pathname='TikTok_Test/*.json')
print(b[0])
import json
with open(b[0], "r") as h:
data = json.load(h)
print(data)
def main(src, dst):
for fi in gg(pt.join(src, '*.pgz')):
fo = pt.join(dst, pt.basename(fi))
work_3(fi, fo)
print(text[0:50])
print(text.lower()[0:50])
print(type(text))
print(len(text))
# =====================
import os
from os.path import join
from os.path import join as oj
textdatei = oj("data", "Kraus.txt")
with open(textdatei, "r", encoding="utf8") as infile:
text = infile.read()
print(text[0:50])
print(text.lower()[0:50])
print(type(text))
print(len(text))
# ===============================
import glob
from glob import glob as gg
for file in gg(join("data", "*.txt")):
#print(file)
with open(file, "r", encoding="utf8") as infile:
text = infile.read()
print(text[0:50])
def __init__(self,
type,
envi_hdr="",
envi_file="",
ascii_spectra="",
meta_csv="",
ascii_bands="",
directory_path="",
meta_tab=""):
"""
loads a spectral library from common spectral library formats including crism (envi format), asu spectral library
(ascii spectra and csv meta), and USGS (directory path and spectral bands ascii path)
stores important spectral library features (spectra, spectral bands, name of spectra, and one hot labels) in a standard format
Note: it is assumed that the first word of the "name" is the label of the mineral, which is generally true for all
libraries with some exceptions. For these exceptions, the data should be relabelled.
:param type: "asu", "crism", or "USGS"
:param envi_hdr: path to envi header file (only for crism)
:param envi_file: path to envi file (only for crism)
:param ascii_spectra: path to ascii spectra file (ASU)
:param meta_csv: path to meta csv file ( ASU)
:param ascii_bands: path to spectral bands ascii file (USGS)
:param director_path: path to directory (USGS)
"""
#assign object variables per asu spec lib type
if type == "asu":
self.source = "asu"
self.spectra = np.loadtxt(ascii_spectra)
self.spectra = np.delete(
self.spectra, 0,
1) # delete first spectra column (wavenumbers)
self.bands = np.loadtxt(ascii_spectra, usecols=0)
self.meta = pd.read_csv(meta_csv)
self.names = self.meta.sample_name.tolist()
self.category = self.meta.category.tolist()
if type == "kim":
self.source = "kim"
self.spectra = np.loadtxt(ascii_spectra)
self.spectra = np.delete(
self.spectra, 0,
1) # delete first spectra column (wavenumbers)
self.bands = np.loadtxt(ascii_spectra, usecols=0)
with open(meta_tab) as f:
self.names = list(csv.reader(f, delimiter='\t'))
self.category = self.names
# assign object variables per crism spec lib type
if type == "crism":
self.source = "crism"
self.envi_file = envi.open(envi_hdr, envi_file)
self.spectra = self.envi_file.spectra.transpose()
self.bands = self.envi_file.bands.centers
self.names = self.envi_file.names
self.category = self.names
if type == "usgs":
self.source = "usgs"
self.bands = np.loadtxt(ascii_bands, skiprows=1)
#iterate through all txt files in directory path
first = True
for f in gg(directory_path):
temp_spectra = np.loadtxt(f, skiprows=1)
temp_spectra = temp_spectra.reshape((len(temp_spectra), 1))
temp_meta = open(f, "r").readlines()[0].split()
temp_name = temp_meta[2]
if first:
self.spectra = temp_spectra
self.names = [temp_name]
first = False
else:
self.spectra = np.append(self.spectra,
temp_spectra,
axis=1)
self.names.append(temp_name)
self.text_labels = self.names
self.category = self.names
#assign general object variables
self.text_labels = [names.partition(" ")[0] for names in self.names]
self.index = range(len(self.names))
self.src_index = range(len(self.names))
encoder = LabelBinarizer()
self.onehot_labels = encoder.fit_transform(self.text_labels)
self.onehot_category = encoder.fit_transform(self.category)
