def decode_url(url, metadata={}, render=True):
# Basic URL validation
if not url.startswith('http://'):
url = 'http://' + url
ext = re.search('\.nii(\.gz)?$', url)
if ext is None:
return error_page("Invalid image extension; currently the decoder only"
" accepts images in nifti format.")
# Check that an image exists at the URL
head = requests.head(url)
if head.status_code not in [200, 301, 302]:
return error_page("No image was found at the provided URL.")
headers = head.headers
if 'content-length' in headers and \
int(headers['content-length']) > 4000000 and render:
return error_page("The requested Nifti image is too large. Files must "
"be under 4 MB in size.")
dec = _get_decoding(url=url)
# Delete old record
if not settings.CACHE_DECODINGS and dec is not None:
db.session.delete(dec)
db.session.commit()
dec = None
if dec is None:
unique_id = uuid.uuid4().hex
filename = join(settings.DECODED_IMAGE_DIR, unique_id + ext.group(0))
f = requests.get(url)
with open(filename, 'wb') as outfile:
outfile.write(f.content)
# Make sure celery worker has permission to overwrite
os.chmod(filename, 0666)
# Named args to pass to Decoding initializer
modified = headers.get('last-modified', None)
if modified is not None:
modified = datetime(*parsedate(modified)[:6])
kwargs = {
'uuid': unique_id,
'url': url,
'name': metadata.get('name', basename(url)),
'image_modified_at': modified,
'filename': filename,
'neurovault_id': metadata.get('nv_id', None)
}
dec = _run_decoder(**kwargs)
if render:
return show(dec, dec.uuid)
else:
return dec.uuid
def decode_url(url, metadata={}, render=True):
# Basic URL validation
if not re.search('^https?\:\/\/', url):
url = 'http://' + url
ext = re.search('\.nii(\.gz)?$', url)
if ext is None:
return error_page("Invalid image extension; currently the decoder only"
" accepts images in nifti format.")
# Check that an image exists at the URL
head = requests.head(url)
if head.status_code not in [200, 301, 302]:
return error_page("No image was found at the provided URL.")
headers = head.headers
if 'content-length' in headers and \
int(headers['content-length']) > 4000000 and render:
return error_page("The requested Nifti image is too large. Files must "
"be under 4 MB in size.")
dec = _get_decoding(url=url)
# Delete old record
if not settings.CACHE_DECODINGS and dec is not None:
db.session.delete(dec)
db.session.commit()
dec = None
if dec is None:
unique_id = uuid.uuid4().hex
filename = join(settings.DECODED_IMAGE_DIR, unique_id + ext.group(0))
f = requests.get(url)
with open(filename, 'wb') as outfile:
outfile.write(f.content)
# Make sure celery worker has permission to overwrite
os.chmod(filename, 0666)
# Named args to pass to Decoding initializer
modified = headers.get('last-modified', None)
if modified is not None:
modified = datetime(*parsedate(modified)[:6])
kwargs = {
'uuid': unique_id,
'url': url,
'name': metadata.get('name', basename(url)),
'image_modified_at': modified,
'filename': filename,
'neurovault_id': metadata.get('nv_id', None)
}
dec = _run_decoder(**kwargs)
if render:
return show(dec, dec.uuid)
else:
return dec.uuid
def get_decoding_data(image, get_json=True):
if Image.query.get(image) is None:
return error_page("Invalid image requested for decoding. Please check"
" to make sure there is a valid image with id=%d." %
image)
dec = decode_analysis_image(image)
df = os.path.join(settings.DECODING_RESULTS_DIR, dec.uuid + '.txt')
if not os.path.exists(df):
return error_page("An unspecified error occurred during decoding.")
data = open(df).read().splitlines()
data = [x.split('\t') for x in data]
data = [[f, round(float(v or '0'), 3)] for (f, v) in data]
return jsonify(data=data) if get_json else data
def get_decoding_data(image, get_json=True):
if Image.query.get(image) is None:
return error_page("Invalid image requested for decoding. Please check"
" to make sure there is a valid image with id=%d." %
image)
dec = decode_analysis_image(image)
df = os.path.join(settings.DECODING_RESULTS_DIR, dec.uuid + '.txt')
if not os.path.exists(df):
return error_page("An unspecified error occurred during decoding.")
data = open(df).read().splitlines()
data = [x.split('\t') for x in data]
data = [[f, round(float(v or '0'), 3)] for (f, v) in data]
return jsonify(data=data) if get_json else data
def index():
# Call API to do the decoding
status, dec = _get_decoding_object()
if status == 200:
return show(dec, dec.uuid)
elif status == 99:
return render_template('decode/index.html')
else:
return error_page(dec)
def run_custom_analysis(uid):
"""
Given a uuid, kick off the analysis run and redirect the user to the
results page once the analysis is complete.
"""
custom = CustomAnalysis.query.filter_by(uuid=uid).first()
if not custom or not custom.studies:
abort(404)
# Only generate images if the analysis has never been run, if changes have
# been made since the last run, or if images are missing.
if not custom.last_run_at or (custom.last_run_at < custom.updated_at) or \
not custom.images:
ids = [s.pmid for s in custom.studies]
if tasks.run_metaanalysis.delay(ids, custom.uuid).wait():
# Update analysis record
rev_inf = '%s_pFgA_z_FDR_0.01.nii.gz' % custom.uuid
rev_inf = join(settings.IMAGE_DIR, 'custom', rev_inf)
fwd_inf = '%s_pAgF_z_FDR_0.01.nii.gz' % custom.uuid
fwd_inf = join(settings.IMAGE_DIR, 'custom', fwd_inf)
if exists(rev_inf):
images = [
CustomAnalysisImage(
name='%s (forward inference)' % custom.name,
image_file=fwd_inf,
label='%s (forward inference)' % custom.name,
stat='z-score',
display=1,
download=1
),
CustomAnalysisImage(
name='%s (reverse inference)' % custom.name,
image_file=rev_inf,
label='%s (reverse inference)' % custom.name,
stat='z-score',
display=1,
download=1
)
]
custom.images = images
custom.last_run_at = dt.datetime.utcnow()
db.session.add(custom)
db.session.commit()
return redirect(url_for('analyses.show_custom_analysis',
uid=uid))
return error_page("An unspecified error occurred while trying "
"to run the custom meta-analysis. Please try "
"again.")
return redirect(url_for('analyses.show_custom_analysis', uid=uid))
def run_custom_analysis(uid):
"""
Given a uuid, kick off the analysis run and redirect the user to the
results page once the analysis is complete.
"""
custom = CustomAnalysis.query.filter_by(uuid=uid).first()
if not custom or not custom.studies:
abort(404)
# Only generate images if the analysis has never been run, if changes have
# been made since the last run, or if images are missing.
if not custom.last_run_at or (custom.last_run_at < custom.updated_at) or \
not custom.images:
ids = [s.pmid for s in custom.studies]
if tasks.run_metaanalysis.delay(ids, custom.uuid).wait():
# Update analysis record
rev_inf = '%s_specificity_z_FDR_0.01.nii.gz' % custom.uuid
rev_inf = join(settings.IMAGE_DIR, 'custom', rev_inf)
fwd_inf = '%s_consistency_FDR_0.01.nii.gz' % custom.uuid
fwd_inf = join(settings.IMAGE_DIR, 'custom', fwd_inf)
if exists(rev_inf):
images = [
CustomAnalysisImage(name='%s (consistency)' % custom.name,
image_file=fwd_inf,
label='%s (consistency)' % custom.name,
stat='z-score',
display=1,
download=1),
CustomAnalysisImage(name='%s (specificity)' % custom.name,
image_file=rev_inf,
label='%s (specificity)' % custom.name,
stat='z-score',
display=1,
download=1)
]
custom.images = images
custom.last_run_at = dt.datetime.utcnow()
db.session.add(custom)
db.session.commit()
return redirect(
url_for('analyses.show_custom_analysis', uid=uid))
return error_page("An unspecified error occurred while trying "
"to run the custom meta-analysis. Please try "
"again.")
return redirect(url_for('analyses.show_custom_analysis', uid=uid))
def run_custom_analysis(uid):
"""
Given a uuid, kick off the analysis run and redirect the user to the
results page once the analysis is complete.
"""
custom = CustomAnalysis.query.filter_by(uuid=uid).first()
if not custom or not custom.studies:
abort(404)
result = api_run_custom(uid)
if result is None:
return error_page("An unspecified error occurred while trying "
"to run the custom meta-analysis. Please try "
"again.")
return redirect(url_for('analyses.show_custom_analysis', uid=uid))
def show(symbol):
gene = Gene.query.filter_by(symbol=symbol).first()
if gene is None:
return error_page("We have no data for the gene '%s'" % symbol)
image = gene.images[0]
# Run decoder if it hasn't been run before
dec = decode_analysis_image(image.id)
url = url_for('api_images.download', val=image.id)
images = [{
'id': image.id,
'name': symbol,
'url': url,
'colorPalette': 'intense red-blue',
'download': url,
'sign': 'both'
}]
return render_template('genes/show.html', gene=gene,
images=json.dumps(images), image_id=dec.uuid)
def run_custom_analysis(uid):
"""
Given a uuid, kick off the analysis run and redirect the user to the
results page once the analysis is complete.
"""
custom = CustomAnalysis.query.filter_by(uuid=uid).first()
if not custom or not custom.studies:
abort(404)
result = api_run_custom(uid)
if result is None:
return error_page("An unspecified error occurred while trying "
"to run the custom meta-analysis. Please try "
"again.")
return redirect(url_for('analyses.show_custom_analysis', uid=uid))
def show(symbol):
gene = Gene.query.filter_by(symbol=symbol).first()
if gene is None:
return error_page("We have no data for the gene '%s'" % symbol)
image = gene.images[0]
# Run decoder if it hasn't been run before
dec = decode_analysis_image(image.id)
url = url_for('api_images.download', val=image.id)
images = [{
'id': image.id,
'name': symbol,
'url': url,
'colorPalette': 'intense red-blue',
'download': url,
'sign': 'both'
}]
return render_template('genes/show.html',
gene=gene,
images=json.dumps(images),
image_id=dec.uuid)
