def aloi_writesubmit_supersim(inflist,
prepflist,
perceptflist,
runscr,
clean=False):
"""
Write and execute a condor submission file with the IO specified in aloi_getpaths().
Parameters
----------
inflist, prepflist, perceptflist : lists
lists containing IO created by aloi_getpaths.
runscr : str
path to bash script executed by condor.
clean : bool
remove submission file after execution?
Returns
-------
perceptflist : list
same as input, just passed along for convenience
"""
submitf = write_submission_file(runscr, inflist, prepflist, perceptflist)
exec_submission(submit_fpath=abp(submitf), cleanup=clean)
print('Submitted ALOI selection to condor for simulation with script %s' % runscr)
return perceptflist
def aloi_writesubmit_hmax(prepfiles, perceptfiles, intactouts, perceptouts,
runscr, clean=False):
# merge input and output lists
hmaxinputs = prepfiles + perceptfiles
hmaxoutputs = intactouts + perceptouts
submitf = write_submission_file(runscr, hmaxinputs, hmaxoutputs)
exec_submission(submit_fpath=abp(submitf), cleanup=clean)
print('Submitted ALOI prepped and percept files to condor for HMAXing with %s' % runscr)
return hmaxinputs, hmaxoutputs
def hmax_image(image_infile,
hmax_outfile,
hmax_python_dir=pjoin('..', 'hmax-python')):
"""
Run python-hmax on a single image.
:param image_infile : str. path to input image file
:param hmax_outfile: str. path to output hmax file. Note that file suffix must be '.ascii'
:param hmax_python_dir:
:return: outfile. str. Same as the input argument 'outfile' but returned for convenience in file handling later.
"""
# get absolute path of in and out file
infile_abs, outfile_abs = abp(image_infile), abp(hmax_outfile)
# get absolute path to script with hmax python implementation
script_abs = abp(pjoin(hmax_python_dir, 'runhmaxonimages.py'))
# call pre-implemented hmax-python
call(['python', script_abs, '-i', infile_abs, '-o', outfile_abs])
return hmax_outfile
def hmax_filelist(filelist):
for fname in filelist:
# absolute path names
fname_abp = abp(fname)
if 'orig' in fname_abp:
outname_abp = fname_abp.replace('orig', 'results') + '.ascii'
elif 'sim' in fname_abp:
outname_abp = fname_abp.replace('sim', 'results') + '.ascii'
# call function
if not os.path.exists(outname_abp):
call([
'python', 'runhmaxonimages.py', '-i', fname_abp, '-o',
outname_abp
])
else:
print('file %s already exists' % outname_abp)
def hmaxfile2dict(hmax_file):
"""
read a given output file returned by python-hmax and store its information in a dict.
stim name, category name, vision type must be indicated by file name (e.g. 'apple_1_percept.png.ascii')
:param hmax_file: str. path to .ascii file returned by python-hmax
:return: pattern_dict. dict. contains stimulus name, category name, response pattern, and vision type
"""
# in case of aloi stimuli, it looks like this:
# /home/contier/ri_hmax/workdir/aloi/hmaxout/490_r90_percept.ascii
# get hmax output vector
hmax_df = pd.read_csv(hmax_file, header=None)
pattern = np.array([row[0] for row in hmax_df.values])
# removes all of path except name of the .ascii file, withoug suffix
fname_nopath = hmax_file.split('/')[-1].replace('.ascii', '')
# get stimulus name, category name, and vision
catname, exemplar, vision = fname_nopath.split('_')[0:3]
if vision == 'percept':
vision = 'ri_' + vision
# stimname = hmax_file.replace('.png', '')
# for substring, vision in zip(['percept', 'intact'], ['ri_percept', 'intact']):
# if substring in hmax_file:
# vision = vision
# stimname = hmax_file.replace('_' + substring, '') # result should be 'apple_1'
# catname = stimname.split('_')[0] # result should be 'apple'
# create file specific dict
pattern_dict = {
'pattern': pattern,
'category': catname,
'exemplar': exemplar,
'vision': vision,
'filename': abp(hmax_file)
}
return pattern_dict
target = pd.DataFrame(
[[1, "2016-01-01T00:00:00"], [2, "2016-01-01T00:00:00"],
[3, "2016-01-01T00:00:00"], [4, "2016-01-01T00:00:00"],
[5, "2016-01-01T00:00:00"], [6, "2016-01-01T00:00:00"],
[7, "2016-01-01T00:00:00"], [8, "2016-01-01T00:00:00"]],
columns=["category_ind", "update_time"])
target.to_sql(ts.article_categories.__tablename__,
con.engine,
if_exists='replace',
index=False)
def insert_catname(con):
target = pd.DataFrame([[1, "エンタメ"], [2, "スポーツ"], [3, "おもしろ"], [4, "日本"],
[5, "海外"], [6, "コラム"], [7, "IT"], [8, "グルメ"]],
columns=["category_ind", "category_name"])
target.to_sql(ts.article_categories_names.__tablename__,
con.engine,
if_exists='replace',
index=False)
if __name__ == '__main__':
_p = drn(abp(__file__))
with open(_p + '/user_info.json') as f:
info = json.load(f)
con = connectPSQL(info)
con.create_tables()
insert_cat(con)
insert_catname(con)