def calculate_similarity_search(template,query_png,query_id,corr_df,button_url,
image_url,max_results,absolute_value,container_width,responsive=True):
"""Generate web interface for similarity search
template: html template (similarity_search)
query_png: image png (must be in "png" column) that the others will be compared to
query_id: id of the query image, to look up in corr_df
corr_df: matrix of correlation values for images, with "png" column corresponding to image paths, "tags" corresponding to
button_url: prefix of url that the "compare" button will link to. format will be prefix/[query_id]/[other_id]
image_url: prefix of the url that the "view" button will link to. format will be prefix/[other_id]
max_results: maximum number of results to return
absolute_value: return absolute value of score (default=True)
responsive: for larger number of returned results: will load images only when scrolled to.
"""
query_row = corr_df[corr_df["png"] == query_png]
# Sort based on (absolute value of) similarity score
if absolute_value:
query_similar = corr_df["scores"].abs()
query_similar.sort(ascending=False)
query_similar = corr_df.loc[query_similar.index]
else: query_similar = corr_df.sort(columns="scores",ascending=False)
# Remove the query image, and cut down to 100 results
query_similar = query_similar[query_similar.index != query_id]
if query_similar.shape[0] > max_results: query_similar = query_similar[0:max_results]
# Prepare data for show_similarity_search
image_ids = query_similar.image_ids.tolist()
all_tags = query_similar.tags.tolist()
scores = np.round(query_similar.scores.values,2)
png_images = query_similar.png.tolist()
top_text = query_similar.top_text.tolist()
bottom_text = query_similar.bottom_text.tolist()
# Get the unique tags
unique_tags = unwrap_list_unique(all_tags)
placeholders = dict()
for tag in unique_tags: placeholders[tag] = tag.replace(" ","")
# Create custom urls
button_urls = ["%s/%s/%s" %(button_url,query_id,x) for x in image_ids]
image_urls = ["%s/%s" %(image_url,x) for x in image_ids]
portfolio = create_glassbrain_portfolio(image_paths=png_images,all_tags=all_tags,unique_tags=unique_tags,
placeholders=placeholders,values=scores,button_urls=button_urls,
image_urls=image_urls,top_text=top_text,bottom_text=bottom_text)
elements = {"SIMILARITY_PORTFOLIO":portfolio,"CONTAINER_WIDTH":container_width}
template = add_string(elements,template)
html_snippet = add_string({"QUERY_IMAGE":query_png},template)
return html_snippet
def plot_histogram(image,
title="Image Histogram",
height=400,
width=1000,
view_in_browser=True,
bins=25,
remove_zeros=True):
'''plot interactive histogram (in browser)'''
image = get_nii_obj(image)[0]
data = image.get_data()
histogram = get_histogram_data(data, bins=bins, remove_zeros=remove_zeros)
bins = '"%s"' % ('","'.join(["%.2f" % (x) for x in histogram["bins"]]))
counts = '"%s"' % ('","'.join(["%s" % (x) for x in histogram["counts"]]))
template = get_template("histogram")
elements = [{
"HISTOGRAM_DATA": counts
}, {
"HISTOGRAM_LABELS": bins
}, {
"HISTOGRAM_TITLE": title
}, {
"HISTOGRAM_HEIGHT": height
}, {
"HISTOGRAM_WIDTH": width
}]
for element in elements:
template = add_string(element, template)
if view_in_browser == True:
view(template)
else:
return template
def plot_histogram(image,
title="Image Histogram",
height=400,
width=1000,
view_in_browser=True,
bins=25,
remove_zeros=True):
'''plot interactive histogram (in browser)'''
image = get_nii_obj(image)[0]
data = image.get_data()
histogram = get_histogram_data(data,bins=bins,remove_zeros=remove_zeros)
bins = '"%s"' %('","'.join(["%.2f" %(x) for x in histogram["bins"]]))
counts = '"%s"' %('","'.join(["%s" %(x) for x in histogram["counts"]]))
template = get_template("histogram")
elements = [{"HISTOGRAM_DATA":counts},
{"HISTOGRAM_LABELS":bins},
{"HISTOGRAM_TITLE":title},
{"HISTOGRAM_HEIGHT":height},
{"HISTOGRAM_WIDTH":width}]
for element in elements:
template = add_string(element,template)
if view_in_browser==True:
view(template)
else:
return template
def make_scatterplot_interface(corr_df,
elements,
error=None,
remove_scripts=None):
'''make_scatterplot_interface
corr_df: pandas data frame
a pandas data frame with INPUT_DATA_ONE,INPUT_DATA_TWO,
ATLAS_DATA,ATLAS_LABELS,ATLAS_CORR,ATLAS_COLORS
elements: list
a list of string elements to add to the template,
each element is a dictionary with:
- key corresponding to tag to replace in template
- value corresponding to the text that will replace the tag
error: str
if specified, will replace scatterplot with error to show user
'''
# We want to return only regions with 3+ points
counts = dict(collections.Counter(corr_df.ATLAS_LABELS.tolist()))
regions_to_eliminate = [x for x, y in counts.items() if y < 3]
corr_df = corr_df[corr_df.ATLAS_LABELS.isin(regions_to_eliminate) == False]
# Error: If all regions have fewer than 3 values
if corr_df.shape[0] == 0:
error = "Fewer than three values in all regions shared by images!"
template = get_template("scatter_atlas", corr_df)
for element in elements:
template = add_string(element, template)
if error != None:
template = scatterplot_compare_error(template, error)
if remove_scripts != None:
if isinstance(remove_scripts, str):
remove_scripts = [remove_scripts]
template = remove_resources(template, script_names=remove_scripts)
return template
def make_scatterplot_interface(corr_df, elements, error=None, remove_scripts=None):
'''make_scatterplot_interface
corr_df: pandas data frame
a pandas data frame with INPUT_DATA_ONE,INPUT_DATA_TWO,
ATLAS_DATA,ATLAS_LABELS,ATLAS_CORR,ATLAS_COLORS
elements: list
a list of string elements to add to the template,
each element is a dictionary with:
- key corresponding to tag to replace in template
- value corresponding to the text that will replace the tag
error: str
if specified, will replace scatterplot with error to show user
'''
# We want to return only regions with 3+ points
counts = dict(collections.Counter(corr_df.ATLAS_LABELS.tolist()))
regions_to_eliminate = [x for x,y in counts.items() if y < 3]
corr_df = corr_df[corr_df.ATLAS_LABELS.isin(regions_to_eliminate)==False]
# Error: If all regions have fewer than 3 values
if corr_df.shape[0] == 0:
error = "Fewer than three values in all regions shared by images!"
template = get_template("scatter_atlas",corr_df)
for element in elements:
template = add_string(element,template)
if error != None:
template = scatterplot_compare_error(template,error)
if remove_scripts != None:
if isinstance(remove_scripts,str):
remove_scripts = [remove_scripts]
template = remove_resources(template,
script_names=remove_scripts)
return template
def run_qa(mr_paths,html_dir,software="FSL",voxdim=[2,2,2],outlier_sds=6,investigator="brainman",
nonzero_thresh=0.25,calculate_mean_image=True,view=True):
'''run_qa: a tool to generate an interactive qa report for statistical maps
mr_paths: a list of paths to brain statistical maps that can be read with nibabel [REQUIRED]
software: currently only freesurfer is supporte [default:FREESURFER]
voxdim: a list of x,y,z dimensions to resample data when normalizing [default [2,2,2]]
outlier_sds: the number of standard deviations from the mean to define an outlier [default:6]
investigator: the name (string) of an investigator to add to alerts summary page [defauflt:None]
nonzero_thresh: images with # of nonzero voxels in brain mask < this value will be flagged as thresholded [default:0.25]
calculate_mean_image: Default True, should be set to False for larger datasets where memory is an issue
view: view the web report in a browser at the end [default:True]
'''
# First resample to standard space
print("Resampling all data to %s using %s standard brain..." %(voxdim,software))
reference_file = get_standard_brain(software)
mask_file = get_standard_mask(software)
images_resamp, reference_resamp = resample_images_ref(mr_paths,reference_file,resample_dim=voxdim,interpolation="continuous")
mask = resample_img(mask_file, target_affine=np.diag(voxdim))
mask_bin = compute_epi_mask(mask)
mask_out = np.zeros(mask_bin.shape)
mask_out[mask_bin.get_data()==0] = 1
voxels_out = np.where(mask_out==1)
total_voxels = np.prod(mask_bin.shape)
total_voxels_in = len(np.where(mask_bin.get_data().flatten()==1)[0])
total_voxels_out = len(np.where(mask_out.flatten()==1)[0])
mask_out = nib.Nifti1Image(mask_out,affine=mask_bin.get_affine())
# We will save qa values for all in a data frame
results = pandas.DataFrame(columns=["voxels_in","voxels_out","standard_deviation_resamp","mean_resamp","variance_resamp","median_resamp","n_outliers_low_%ssd" %(outlier_sds),"n_outliers_high_%ssd" %(outlier_sds),"nonzero_percent_voxels_in_mask","threshold_flag"])
# We also need to save distributions for the summary page
all_histograms = []
image_names = []
# Set up directories
pwd = get_package_dir()
images_dir = "%s/img" %(html_dir)
make_dir(images_dir)
# Calculate a mean image for the entire set
if calculate_mean_image == True:
print("Calculating mean image...")
all_masked_data = apply_mask(images_resamp, mask_bin, dtype='f', smoothing_fwhm=None, ensure_finite=True)
mean_image = np.zeros(mask_bin.shape)
mean_image[mask_bin.get_data()==1] = np.mean(all_masked_data,axis=0)
mean_image = nib.Nifti1Image(mean_image,affine=mask_bin.get_affine())
mean_intensity = np.mean(mean_image.get_data()[mask_bin.get_data()==1])
histogram_data_mean = get_histogram_data(mean_image.get_data()[mask_bin.get_data()==1])
histogram_mean_counts = ",".join([str(x) for x in histogram_data_mean["counts"]])
nib.save(mean_image,"%s/mean.nii" %(html_dir))
make_stat_image("%s/mean.nii" %(html_dir),png_img_file="%s/mean.png" %(html_dir))
nib.save(reference_resamp,"%s/standard.nii" %(html_dir))
nib.save(mask_bin,"%s/mask.nii" %(html_dir))
nib.save(mask_out,"%s/mask_out.nii" %(html_dir))
make_anat_image("%s/mask.nii" %(html_dir),png_img_file="%s/mask.png" %(html_dir))
make_anat_image("%s/mask_out.nii" %(html_dir),png_img_file="%s/mask_out.png" %(html_dir))
unzip("%s/static/qa_report.zip" %(pwd),html_dir)
for m in range(0,len(mr_paths)):
mr = images_resamp[m]
mr_original = nib.load(mr_paths[m])
image_name = os.path.split(mr_paths[m])[1]
print("Generating qa report for %s" %(mr_paths[m]))
# Output folder generation
mr_folder = "%s/%s" %(html_dir,m)
make_dir(mr_folder)
mr_images = "%s/img" %(mr_folder)
make_dir(mr_images)
masked_in_data = mr.get_data()[mask_bin.get_data()==1]
masked_out_data = mr.get_data()[mask_out.get_data()==1]
mr_in_mask,mr_out_mask = make_in_out_mask(mask_bin=mask_bin,mr_folder=mr_folder,masked_in=masked_in_data,masked_out=masked_out_data,img_dir=mr_images)
# Glass brain, masked, and histogram data
make_stat_image("%s/masked.nii" %(mr_folder),png_img_file="%s/mr_masked.png" %(mr_images))
make_glassbrain_image("%s/masked.nii" %(mr_folder),png_img_file="%s/glassbrain.png" %(mr_images))
metrics = central_tendency(masked_in_data)
# Header metrics
mr_metrics = header_metrics(mr_original)
histogram_data_in = get_histogram_data(masked_in_data)
histogram_data_out = get_histogram_data(masked_out_data)
# Counting voxels (should eventually be in/out brain ratios)
count_in,count_out = count_voxels(masked_in=masked_in_data,masked_out=masked_out_data)
high_out,low_out = outliers(masked_in_data,n_std=outlier_sds)
# estimate thresholded or not. If the original image is the same shape as the mask, use it
if mr_original.shape == mask.shape:
threshold_flag,percent_nonzero = is_thresholded(mr_original,mask,threshold=nonzero_thresh)
else: # this will return biased high values because we have resampled with this standard!
threshold_flag,percent_nonzero = is_thresholded(mr,mask,threshold=nonzero_thresh)
# Add everything to table, prepare single page template
results.loc[m] = [count_in,count_out,metrics["std"],metrics["mean"],metrics["var"],metrics["med"],low_out,high_out,percent_nonzero,threshold_flag]
if calculate_mean_image == True:
template = get_template("qa_single_statmap_mean")
else:
template = get_template("qa_single_statmap")
# Things to fill into individual template
if m != 0: last_page = m-1;
else: last_page = len(mr_paths)-1;
if m != len(mr_paths)-1: next_page = m+1;
else: next_page = 0
histogram_in_counts = ",".join([str(x) for x in histogram_data_in["counts"]])
all_histograms.append(histogram_in_counts)
image_names.append(image_name)
histogram_out_counts = ",".join([str(x) for x in histogram_data_out["counts"]])
histogram_bins = '"%s"' % '","'.join([str(np.round(x,2)) for x in histogram_data_in["bins"]])
substitutions = {"NUMBER_IMAGES":len(mr_paths),
"IMAGE_NAME": image_name,
"NONZERO_VOXELS": "%0.3f" % (percent_nonzero),
"THRESHOLD_FLAG": "%s" % (threshold_flag),
"NONZERO_THRESH": "%s" % (nonzero_thresh),
"TOTAL_VOXELS":total_voxels,
"MEAN_SCORE":"%0.2f" % metrics["mean"],
"MEDIAN_SCORE":"%0.2f" % metrics["med"],
"VARIANCE_SCORE":"%0.2f" % metrics["var"],
"OUTLIERS_HIGH": high_out,
"OUTLIERS_LOW":low_out,
"OUTLIERS_STANDARD_DEVIATION":outlier_sds,
"STANDARD_DEVIATION_SCORE":"%0.2f" % metrics["std"],
"STATMAP_HISTOGRAM":histogram_in_counts,
"NEXT_PAGE":"../%s/%s.html" %(next_page,next_page),
"LAST_PAGE":"../%s/%s.html" %(last_page,last_page),
"OVERLAY_IMAGE":"%s/masked.nii" %(mr_folder),
"INVESTIGATOR":investigator
}
template = add_string(substitutions,template)
if calculate_mean_image == True:
template = add_string({"MEAN_IMAGE_HISTOGRAM":histogram_mean_counts},template)
save_template(template,"%s/%s.html" %(mr_folder,m))
# Individual pages done, now make summary pages, first the histograms
template = get_template("qa_histograms")
if calculate_mean_image == True:
statmap_histograms = ['<div class="span2 statbox purple" onTablet="span2" onDesktop="span2">\n<div class="boxchart">%s</div><div class="number" style="font-size:30px">%s</div><div class="title">images</div><div class="footer"></div></div>' %(histogram_mean_counts,len(mr_paths))]
else:
statmap_histograms = []
m = 0
for mean in results["mean_resamp"]:
if calculate_mean_image == True:
if mean >= mean_intensity:
statmap_histograms.append('<div class="span2 statbox blue" onTablet="span2"\n onDesktop="span2"><div class="boxchart">%s</div><div class="number" style="font-size:30px"><i class="icon-arrow-up"></i></div><div class="title">%s</div><div class="footer"><a href="%s/%s.html"> detail</a></div></div>' %(all_histograms[m],m,m,m))
else:
statmap_histograms.append('<div class="span2 statbox red" onTablet="span2"\n onDesktop="span2"><div class="boxchart">%s</div><div class="number" style="font-size:30px"><i class="icon-arrow-down"></i></div><div class="title">%s</div><div class="footer"><a href="%s/%s.html"> detail</a></div></div>' %(all_histograms[m],m,m,m))
else:
statmap_histograms.append('<div class="span2 statbox red" onTablet="span2"\n onDesktop="span2"><div class="boxchart">%s</div><div class="number" style="font-size:30px"></div><div class="title">%s</div><div class="footer"><a href="%s/%s.html"> detail</a></div></div>' %(all_histograms[m],m,m,m))
m+=1
template = add_string({"STATMAP_HISTOGRAMS":"\n".join(statmap_histograms),
"NUMBER_IMAGES":len(mr_paths),
"INVESTIGATOR":investigator},template)
save_template(template,"%s/histograms.html" %(html_dir))
# Summary table page and alerts
template_summary = get_template("qa_summary_table")
template_alerts = get_template("qa_alerts")
statmap_table = []; alerts_passing = []; alerts_outliers = []; alerts_thresh = []; count=0;
for res in results.iterrows():
# SUMMARY ITEMS ----
# If the image has too many zeros:
if res[1]["threshold_flag"] == True:
alerts_thresh.append('<div class="task high"><div class="desc"><div class="title">Thresholded Map</div><div>Image ID %s has been flagged as being thresholded! Nonzero voxels in mask: %s.</div></div><div class="time"><div class="date">%s</div></div></div>' %(count,res[1]["nonzero_percent_voxels_in_mask"],time.strftime("%c")))
# If the image has outliers or is thresholded:
total_outliers = res[1]["n_outliers_low_%ssd" %(outlier_sds)] + res[1]["n_outliers_high_%ssd" %(outlier_sds)]
flagged = (total_outliers > 0) | res[1]["threshold_flag"]
if flagged == True:
statmap_table.append('<tr><td>%s</td><td class="center">%s</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center"><a class="btn btn-danger" href="%s/%s.html"><i class="icon-flag zoom-in"></i></a></td></tr>' %(image_names[count],count,res[1]["mean_resamp"],res[1]["median_resamp"],res[1]["variance_resamp"],res[1]["standard_deviation_resamp"],res[1]["n_outliers_low_%ssd" %(outlier_sds)],res[1]["n_outliers_high_%ssd" %(outlier_sds)],count,count))
if res[1]["n_outliers_high_%ssd" %(outlier_sds)] > 0:
alerts_outliers.append('<div class="task medium"><div class="desc"><div class="title">Outlier High</div><div>Image ID %s has been flagged to have a high outlier</div></div><div class="time"><div class="date">%s</div><div></div></div></div>' %(count,time.strftime("%c")))
if res[1]["n_outliers_low_%ssd" %(outlier_sds)] > 0:
alerts_outliers.append('<div class="task medium"><div class="desc"><div class="title">Outlier Low</div><div>Image ID %s has been flagged to have a high outlier</div></div><div class="time"><div class="date">%s</div><div></div></div></div>' %(count,time.strftime("%c")))
# Image is passing!
else:
statmap_table.append('<tr><td>%s</td><td class="center">%s</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center"><a class="btn btn-success" href="%s/%s.html"><i class="icon-check zoom-in"></i></a></td></tr>' %(image_names[count],count,res[1]["mean_resamp"],res[1]["median_resamp"],res[1]["variance_resamp"],res[1]["standard_deviation_resamp"],res[1]["n_outliers_low_%ssd" %(outlier_sds)],res[1]["n_outliers_high_%ssd" %(outlier_sds)],count,count))
alerts_passing.append('<div class="task low"><div class="desc"><div class="title">%s</div><div>This map has no flags as determined by the standards of only this report.</div></div><div class="time"><div class="date">%s</div><div></div></div></div>' %(image_names[count],time.strftime("%c")))
count+=1
# ALERTS ITEMS ----
# In the case of zero of any of the above
if len(alerts_thresh) == 0:
alerts_thresh = ['<div class="task high last"><div class="desc"><div class="title">No Thresholded Maps</div><div>No images have been flagged as thresholded [percent nonzero voxels in mask <= %s]</div></div><div class="time"><div class="date">%s</div></div></div>' %(nonzero_thresh,time.strftime("%c"))]
number_thresh = 0
else:
alerts_thresh[-1] = alerts_thresh[-1].replace("task high","task high last")
number_thresh = len(alerts_thresh)
if len(alerts_outliers) == 0:
alerts_outliers = ['<div class="task medium last"><div class="desc"><div class="title">No Outliers</div><div>No images have been flagged for outliers %s standard deviations in either direction.</div></div><div class="time"><div class="date">%s</div></div></div>' %(outlier_sds,time.strftime("%c"))]
number_outliers = 0
else:
alerts_outliers[-1] = alerts_outliers[-1].replace("task medium","task medium last")
number_outliers = len(alerts_outliers)
if len(alerts_passing) == 0:
alerts_passing = ['<div class="task low last"><div class="desc"><div class="title">No Passing!</div><div>No images are passing! What did you do?!</div></div><div class="time"><div class="date">%s</div></div></div>' %(time.strftime("%c"))]
# Alerts and summary template
template_alerts = add_string({"ALERTS_PASSING":"\n".join(alerts_passing),
"ALERTS_OUTLIERS":"\n".join(alerts_outliers),
"NUMBER_IMAGES":len(mr_paths),
"OUTLIERS_STANDARD_DEVIATIONS":outlier_sds,
"ALERTS_THRESH":"\n".join(alerts_thresh),
"INVESTIGATOR":investigator},template_alerts)
template_summary = add_string({"STATMAP_TABLE":"\n".join(statmap_table),
"NUMBER_IMAGES":len(mr_paths),
"OUTLIERS_STANDARD_DEVIATIONS":outlier_sds,
"INVESTIGATOR":investigator},template_summary)
save_template(template_summary,"%s/summary.html" %(html_dir))
save_template(template_alerts,"%s/alerts.html" %(html_dir))
# Finally, save the index
index_template = get_template("qa_index")
image_gallery = ['<div id="image-%s" class="masonry-thumb"><a style="background:url(%s/img/glassbrain.png) width=200px" title="%s" href="%s/%s.html"><img class="grayscale" src="%s/img/glassbrain.png" alt="%s"></a></div>' %(m,m,image_names[m],m,m,m,image_names[m]) for m in range(0,len(mr_paths)) ]
substitutions = {"GLASSBRAIN_GALLERY":"\n".join(image_gallery),
"NUMBER_OUTLIERS":number_outliers,
"NUMBER_THRESH":number_thresh,
"NUMBER_IMAGES":len(mr_paths),
"INVESTIGATOR":investigator
}
index_template = add_string(substitutions,index_template)
if calculate_mean_image == True:
index_template = add_string({"MEAN_IMAGE_HISTOGRAM":histogram_mean_counts},index_template)
save_template(index_template,"%s/index.html" %(html_dir))
# Save results to file
results.to_csv("%s/allMetrics.tsv" %(html_dir),sep="\t")
if view==True:
os.chdir(html_dir)
run_webserver(PORT=8091)
def make_reverse_inference_tree_d3(data_structure):
'''Render d3 of ontology tree, return html with embedded data'''
temp = get_template("reverse_inference_tree")
temp = add_string({"INPUT_ONTOLOGY_JSON": data_structure}, temp)
return temp
def run_qa(mr_paths,html_dir,software="FSL",voxdim=[2,2,2],outlier_sds=6,investigator="brainman",
nonzero_thresh=0.25,calculate_mean_image=True,view=True):
'''run_qa: a tool to generate an interactive qa report for statistical maps
mr_paths: a list of paths to brain statistical maps that can be read with nibabel [REQUIRED]
software: currently only freesurfer is supporte [default:FREESURFER]
voxdim: a list of x,y,z dimensions to resample data when normalizing [default [2,2,2]]
outlier_sds: the number of standard deviations from the mean to define an outlier [default:6]
investigator: the name (string) of an investigator to add to alerts summary page [defauflt:None]
nonzero_thresh: images with # of nonzero voxels in brain mask < this value will be flagged as thresholded [default:0.25]
calculate_mean_image: Default True, should be set to False for larger datasets where memory is an issue
view: view the web report in a browser at the end [default:True]
'''
# First resample to standard space
print "Resampling all data to %s using %s standard brain..." %(voxdim,software)
reference_file = get_standard_brain(software)
mask_file = get_standard_mask(software)
images_resamp, reference_resamp = resample_images_ref(mr_paths,reference_file,resample_dim=voxdim,interpolation="continuous")
mask = resample_img(mask_file, target_affine=np.diag(voxdim))
mask_bin = compute_epi_mask(mask)
mask_out = np.zeros(mask_bin.shape)
mask_out[mask_bin.get_data()==0] = 1
voxels_out = np.where(mask_out==1)
total_voxels = np.prod(mask_bin.shape)
total_voxels_in = len(np.where(mask_bin.get_data().flatten()==1)[0])
total_voxels_out = len(np.where(mask_out.flatten()==1)[0])
mask_out = nib.Nifti1Image(mask_out,affine=mask_bin.get_affine())
# We will save qa values for all in a data frame
results = pandas.DataFrame(columns=["voxels_in","voxels_out","standard_deviation_resamp","mean_resamp","variance_resamp","median_resamp","n_outliers_low_%ssd" %(outlier_sds),"n_outliers_high_%ssd" %(outlier_sds),"nonzero_percent_voxels_in_mask","threshold_flag"])
# We also need to save distributions for the summary page
all_histograms = []
image_names = []
# Set up directories
pwd = get_package_dir()
images_dir = "%s/img" %(html_dir)
make_dir(images_dir)
# Calculate a mean image for the entire set
if calculate_mean_image == True:
print "Calculating mean image..."
all_masked_data = apply_mask(images_resamp, mask_bin, dtype='f', smoothing_fwhm=None, ensure_finite=True)
mean_image = np.zeros(mask_bin.shape)
mean_image[mask_bin.get_data()==1] = np.mean(all_masked_data,axis=0)
mean_image = nib.Nifti1Image(mean_image,affine=mask_bin.get_affine())
mean_intensity = np.mean(mean_image.get_data()[mask_bin.get_data()==1])
histogram_data_mean = get_histogram_data(mean_image.get_data()[mask_bin.get_data()==1])
histogram_mean_counts = ",".join([str(x) for x in histogram_data_mean["counts"]])
nib.save(mean_image,"%s/mean.nii" %(html_dir))
make_stat_image("%s/mean.nii" %(html_dir),png_img_file="%s/mean.png" %(html_dir))
nib.save(reference_resamp,"%s/standard.nii" %(html_dir))
nib.save(mask_bin,"%s/mask.nii" %(html_dir))
nib.save(mask_out,"%s/mask_out.nii" %(html_dir))
make_anat_image("%s/mask.nii" %(html_dir),png_img_file="%s/mask.png" %(html_dir))
make_anat_image("%s/mask_out.nii" %(html_dir),png_img_file="%s/mask_out.png" %(html_dir))
unzip("%s/static/qa_report.zip" %(pwd),html_dir)
for m in range(0,len(mr_paths)):
mr = images_resamp[m]
mr_original = nib.load(mr_paths[m])
image_name = os.path.split(mr_paths[m])[1]
print "Generating qa report for %s" %(mr_paths[m])
# Output folder generation
mr_folder = "%s/%s" %(html_dir,m)
make_dir(mr_folder)
mr_images = "%s/img" %(mr_folder)
make_dir(mr_images)
masked_in_data = mr.get_data()[mask_bin.get_data()==1]
masked_out_data = mr.get_data()[mask_out.get_data()==1]
mr_in_mask,mr_out_mask = make_in_out_mask(mask_bin=mask_bin,mr_folder=mr_folder,masked_in=masked_in_data,masked_out=masked_out_data,img_dir=mr_images)
# Glass brain, masked, and histogram data
make_stat_image("%s/masked.nii" %(mr_folder),png_img_file="%s/mr_masked.png" %(mr_images))
make_glassbrain_image("%s/masked.nii" %(mr_folder),png_img_file="%s/glassbrain.png" %(mr_images))
metrics = central_tendency(masked_in_data)
# Header metrics
mr_metrics = header_metrics(mr_original)
histogram_data_in = get_histogram_data(masked_in_data)
histogram_data_out = get_histogram_data(masked_out_data)
# Counting voxels (should eventually be in/out brain ratios)
count_in,count_out = count_voxels(masked_in=masked_in_data,masked_out=masked_out_data)
high_out,low_out = outliers(masked_in_data,n_std=outlier_sds)
# estimate thresholded or not. If the original image is the same shape as the mask, use it
if mr_original.shape == mask.shape:
threshold_flag,percent_nonzero = is_thresholded(mr_original,mask,threshold=nonzero_thresh)
else: # this will return biased high values because we have resampled with this standard!
threshold_flag,percent_nonzero = is_thresholded(mr,mask,threshold=nonzero_thresh)
# Add everything to table, prepare single page template
results.loc[m] = [count_in,count_out,metrics["std"],metrics["mean"],metrics["var"],metrics["med"],low_out,high_out,percent_nonzero,threshold_flag]
if calculate_mean_image == True:
template = get_template("qa_single_statmap_mean")
else:
template = get_template("qa_single_statmap")
# Things to fill into individual template
if m != 0: last_page = m-1;
else: last_page = len(mr_paths)-1;
if m != len(mr_paths)-1: next_page = m+1;
else: next_page = 0
histogram_in_counts = ",".join([str(x) for x in histogram_data_in["counts"]])
all_histograms.append(histogram_in_counts)
image_names.append(image_name)
histogram_out_counts = ",".join([str(x) for x in histogram_data_out["counts"]])
histogram_bins = '"%s"' % '","'.join([str(np.round(x,2)) for x in histogram_data_in["bins"]])
substitutions = {"NUMBER_IMAGES":len(mr_paths),
"IMAGE_NAME": image_name,
"NONZERO_VOXELS": "%0.3f" % (percent_nonzero),
"THRESHOLD_FLAG": "%s" % (threshold_flag),
"NONZERO_THRESH": "%s" % (nonzero_thresh),
"TOTAL_VOXELS":total_voxels,
"MEAN_SCORE":"%0.2f" % metrics["mean"],
"MEDIAN_SCORE":"%0.2f" % metrics["med"],
"VARIANCE_SCORE":"%0.2f" % metrics["var"],
"OUTLIERS_HIGH": high_out,
"OUTLIERS_LOW":low_out,
"OUTLIERS_STANDARD_DEVIATION":outlier_sds,
"STANDARD_DEVIATION_SCORE":"%0.2f" % metrics["std"],
"STATMAP_HISTOGRAM":histogram_in_counts,
"NEXT_PAGE":"../%s/%s.html" %(next_page,next_page),
"LAST_PAGE":"../%s/%s.html" %(last_page,last_page),
"OVERLAY_IMAGE":"%s/masked.nii" %(mr_folder),
"INVESTIGATOR":investigator
}
template = add_string(substitutions,template)
if calculate_mean_image == True:
template = add_string({"MEAN_IMAGE_HISTOGRAM":histogram_mean_counts},template)
save_template(template,"%s/%s.html" %(mr_folder,m))
# Individual pages done, now make summary pages, first the histograms
template = get_template("qa_histograms")
if calculate_mean_image == True:
statmap_histograms = ['<div class="span2 statbox purple" onTablet="span2" onDesktop="span2">\n<div class="boxchart">%s</div><div class="number" style="font-size:30px">%s</div><div class="title">images</div><div class="footer"></div></div>' %(histogram_mean_counts,len(mr_paths))]
else:
statmap_histograms = []
m = 0
for mean in results["mean_resamp"]:
if calculate_mean_image == True:
if mean >= mean_intensity:
statmap_histograms.append('<div class="span2 statbox blue" onTablet="span2"\n onDesktop="span2"><div class="boxchart">%s</div><div class="number" style="font-size:30px"><i class="icon-arrow-up"></i></div><div class="title">%s</div><div class="footer"><a href="%s/%s.html"> detail</a></div></div>' %(all_histograms[m],m,m,m))
else:
statmap_histograms.append('<div class="span2 statbox red" onTablet="span2"\n onDesktop="span2"><div class="boxchart">%s</div><div class="number" style="font-size:30px"><i class="icon-arrow-down"></i></div><div class="title">%s</div><div class="footer"><a href="%s/%s.html"> detail</a></div></div>' %(all_histograms[m],m,m,m))
else:
statmap_histograms.append('<div class="span2 statbox red" onTablet="span2"\n onDesktop="span2"><div class="boxchart">%s</div><div class="number" style="font-size:30px"></div><div class="title">%s</div><div class="footer"><a href="%s/%s.html"> detail</a></div></div>' %(all_histograms[m],m,m,m))
m+=1
template = add_string({"STATMAP_HISTOGRAMS":"\n".join(statmap_histograms),
"NUMBER_IMAGES":len(mr_paths),
"INVESTIGATOR":investigator},template)
save_template(template,"%s/histograms.html" %(html_dir))
# Summary table page and alerts
template_summary = get_template("qa_summary_table")
template_alerts = get_template("qa_alerts")
statmap_table = []; alerts_passing = []; alerts_outliers = []; alerts_thresh = []; count=0;
for res in results.iterrows():
# SUMMARY ITEMS ----
# If the image has too many zeros:
if res[1]["threshold_flag"] == True:
alerts_thresh.append('<div class="task high"><div class="desc"><div class="title">Thresholded Map</div><div>Image ID %s has been flagged as being thresholded! Nonzero voxels in mask: %s.</div></div><div class="time"><div class="date">%s</div></div></div>' %(count,res[1]["nonzero_percent_voxels_in_mask"],time.strftime("%c")))
# If the image has outliers or is thresholded:
total_outliers = res[1]["n_outliers_low_%ssd" %(outlier_sds)] + res[1]["n_outliers_high_%ssd" %(outlier_sds)]
flagged = (total_outliers > 0) | res[1]["threshold_flag"]
if flagged == True:
statmap_table.append('<tr><td>%s</td><td class="center">%s</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center"><a class="btn btn-danger" href="%s/%s.html"><i class="icon-flag zoom-in"></i></a></td></tr>' %(image_names[count],count,res[1]["mean_resamp"],res[1]["median_resamp"],res[1]["variance_resamp"],res[1]["standard_deviation_resamp"],res[1]["n_outliers_low_%ssd" %(outlier_sds)],res[1]["n_outliers_high_%ssd" %(outlier_sds)],count,count))
if res[1]["n_outliers_high_%ssd" %(outlier_sds)] > 0:
alerts_outliers.append('<div class="task medium"><div class="desc"><div class="title">Outlier High</div><div>Image ID %s has been flagged to have a high outlier</div></div><div class="time"><div class="date">%s</div><div></div></div></div>' %(count,time.strftime("%c")))
if res[1]["n_outliers_low_%ssd" %(outlier_sds)] > 0:
alerts_outliers.append('<div class="task medium"><div class="desc"><div class="title">Outlier Low</div><div>Image ID %s has been flagged to have a high outlier</div></div><div class="time"><div class="date">%s</div><div></div></div></div>' %(count,time.strftime("%c")))
# Image is passing!
else:
statmap_table.append('<tr><td>%s</td><td class="center">%s</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center">%0.2f</td><td class="center"><a class="btn btn-success" href="%s/%s.html"><i class="icon-check zoom-in"></i></a></td></tr>' %(image_names[count],count,res[1]["mean_resamp"],res[1]["median_resamp"],res[1]["variance_resamp"],res[1]["standard_deviation_resamp"],res[1]["n_outliers_low_%ssd" %(outlier_sds)],res[1]["n_outliers_high_%ssd" %(outlier_sds)],count,count))
alerts_passing.append('<div class="task low"><div class="desc"><div class="title">%s</div><div>This map has no flags as determined by the standards of only this report.</div></div><div class="time"><div class="date">%s</div><div></div></div></div>' %(image_names[count],time.strftime("%c")))
count+=1
# ALERTS ITEMS ----
# In the case of zero of any of the above
if len(alerts_thresh) == 0:
alerts_thresh = ['<div class="task high last"><div class="desc"><div class="title">No Thresholded Maps</div><div>No images have been flagged as thresholded [percent nonzero voxels in mask <= %s]</div></div><div class="time"><div class="date">%s</div></div></div>' %(nonzero_thresh,time.strftime("%c"))]
number_thresh = 0
else:
alerts_thresh[-1] = alerts_thresh[-1].replace("task high","task high last")
number_thresh = len(alerts_thresh)
if len(alerts_outliers) == 0:
alerts_outliers = ['<div class="task medium last"><div class="desc"><div class="title">No Outliers</div><div>No images have been flagged for outliers %s standard deviations in either direction.</div></div><div class="time"><div class="date">%s</div></div></div>' %(outlier_sds,time.strftime("%c"))]
number_outliers = 0
else:
alerts_outliers[-1] = alerts_outliers[-1].replace("task medium","task medium last")
number_outliers = len(alerts_outliers)
if len(alerts_passing) == 0:
alerts_passing = ['<div class="task low last"><div class="desc"><div class="title">No Passing!</div><div>No images are passing! What did you do?!</div></div><div class="time"><div class="date">%s</div></div></div>' %(time.strftime("%c"))]
# Alerts and summary template
template_alerts = add_string({"ALERTS_PASSING":"\n".join(alerts_passing),
"ALERTS_OUTLIERS":"\n".join(alerts_outliers),
"NUMBER_IMAGES":len(mr_paths),
"OUTLIERS_STANDARD_DEVIATIONS":outlier_sds,
"ALERTS_THRESH":"\n".join(alerts_thresh),
"INVESTIGATOR":investigator},template_alerts)
template_summary = add_string({"STATMAP_TABLE":"\n".join(statmap_table),
"NUMBER_IMAGES":len(mr_paths),
"OUTLIERS_STANDARD_DEVIATIONS":outlier_sds,
"INVESTIGATOR":investigator},template_summary)
save_template(template_summary,"%s/summary.html" %(html_dir))
save_template(template_alerts,"%s/alerts.html" %(html_dir))
# Finally, save the index
index_template = get_template("qa_index")
image_gallery = ['<div id="image-%s" class="masonry-thumb"><a style="background:url(%s/img/glassbrain.png) width=200px" title="%s" href="%s/%s.html"><img class="grayscale" src="%s/img/glassbrain.png" alt="%s"></a></div>' %(m,m,image_names[m],m,m,m,image_names[m]) for m in range(0,len(mr_paths)) ]
substitutions = {"GLASSBRAIN_GALLERY":"\n".join(image_gallery),
"NUMBER_OUTLIERS":number_outliers,
"NUMBER_THRESH":number_thresh,
"NUMBER_IMAGES":len(mr_paths),
"INVESTIGATOR":investigator
}
index_template = add_string(substitutions,index_template)
if calculate_mean_image == True:
index_template = add_string({"MEAN_IMAGE_HISTOGRAM":histogram_mean_counts},index_template)
save_template(index_template,"%s/index.html" %(html_dir))
# Save results to file
results.to_csv("%s/allMetrics.tsv" %(html_dir),sep="\t")
if view==True:
os.chdir(html_dir)
run_webserver(PORT=8091)
for row in thresh_df.iterrows():
connections = numpy.where(row[1] != 0)
network = network_names[c]
if len(connections[0]) > 0:
connection_names = list(thresh_df.columns[connections])
connection_groups = [groups[x] for x in connections[0]]
connection_labels = ["%s.%s" %(connection_groups[i],connection_names[i]) for i in range(0,len(connections[0]))]
connection_labels_string = '","'.join([str(x) for x in connection_labels])
connection_values = row[1][connections[0]]
connection_values_string = "|".join([str(x) for x in connection_values])
# If we aren't at the last row
if c+1 != thresh_df.shape[0]:
myjson.append('{"name":"%s.%s","strength":"%s","x":99,"y":99,"z":99,"image":"#","order":%s,"color":"%s","network":"%s","connections":["%s"]},' %(groups[c],labels[c],connection_values_string,labels[c],network_colors[network],network,connection_labels_string))
else:
myjson.append('{"name":"%s.%s","strength":"%s","x":99,"y":99,"z":99,"image":"#","order":%s,"color":"%s","network":"%s","connections":["%s"]}' %(groups[c],labels[c],connection_values_string,labels[c],network_colors[network],network,connection_labels_string))
# If there are no connections
else:
if c+1 != thresh_df.shape[0]:
myjson.append('{"name":"%s.%s","x":99,"y":99,"z":99,"image":"#","order":%s,"color":"%s","network":"%s"},' %(groups[c],labels[c],labels[c],network_colors[network],network))
else:
myjson.append('{"name":"%s.%s","x":99,"y":99,"z":99,"image":"#","order":%s,"color":"%s","network":"%s"}' %(groups[c],labels[c],labels[c],network_colors[network],network))
c=c+1
myjson = "\n".join([x for x in myjson])
myjson = "[%s]" % myjson
# Plug into the template
template = get_template("connectogram")
template = add_string({"CONNECTOGRAM_JSON":myjson},template)
return template
def make_reverse_inference_tree_d3(data_structure):
'''Render d3 of ontology tree, return html with embedded data'''
temp = get_template("reverse_inference_tree")
temp = add_string({"INPUT_ONTOLOGY_JSON":data_structure},temp)
return temp
def connectogram(matrix_file,groups,threshold,network_names=None):
'''connectogram: Generate a d3 connectogram for a functionary connectivity matrix.
matrix_file: a tab separated correlation matrix
groups: a list of connection groups, whatever names you want
threshold: a 99% (.99) threhsold means we include the top 1% of negative and positive values
'''
conn_df = pandas.read_csv(matrix_file,sep="\t")
if conn_df.shape[0] != conn_df.shape[1]:
print("Matrix is size [%s,%s], please check file formatting!" %(conn_df.shape[0],conn_df.shape[1]))
return
if not network_names:
network_names = groups
# Fill NaN with 0 in matrix
conn_df = conn_df.fillna(0)
pos_df = conn_df.copy()
neg_df = conn_df.copy()
pos_df[pos_df<0] = 0
neg_df[neg_df>0] = 0
pos_df = pos_df.fillna(0)
neg_df = neg_df.fillna(0)
# Try getting quantiles for top and bottom
qpos = numpy.percentile(pos_df,threshold*100)
qneg = numpy.percentile(neg_df,threshold*100)
pos_df[pos_df < qpos] = 0
neg_df[neg_df > (-1*qneg)] = 0
pos_df = pos_df.fillna(0)
neg_df = neg_df.fillna(0)
thresh_df = pos_df + neg_df
# Get colors
unique_networks = numpy.unique(network_names)
colors = random_colors(len(unique_networks))
network_colors = dict()
for network in unique_networks: network_colors[network] = colors.pop(0)
# Network node labels
labels = list(thresh_df.columns)
# For each, we will output a json object with our variables of interest
myjson = []
c = 0
for row in thresh_df.iterrows():
connections = numpy.where(row[1] != 0)
network = network_names[c]
if len(connections[0]) > 0:
connection_names = list(thresh_df.columns[connections])
connection_groups = [groups[x] for x in connections[0]]
connection_labels = ["%s.%s" %(connection_groups[i],connection_names[i]) for i in range(0,len(connections[0]))]
connection_labels_string = '","'.join([str(x) for x in connection_labels])
connection_values = row[1][connections[0]]
connection_values_string = "|".join([str(x) for x in connection_values])
# If we aren't at the last row
if c+1 != thresh_df.shape[0]:
myjson.append('{"name":"%s.%s","strength":"%s","x":99,"y":99,"z":99,"image":"#","order":%s,"color":"%s","network":"%s","connections":["%s"]},' %(groups[c],labels[c],connection_values_string,labels[c],network_colors[network],network,connection_labels_string))
else:
myjson.append('{"name":"%s.%s","strength":"%s","x":99,"y":99,"z":99,"image":"#","order":%s,"color":"%s","network":"%s","connections":["%s"]}' %(groups[c],labels[c],connection_values_string,labels[c],network_colors[network],network,connection_labels_string))
# If there are no connections
else:
if c+1 != thresh_df.shape[0]:
myjson.append('{"name":"%s.%s","x":99,"y":99,"z":99,"image":"#","order":%s,"color":"%s","network":"%s"},' %(groups[c],labels[c],labels[c],network_colors[network],network))
else:
myjson.append('{"name":"%s.%s","x":99,"y":99,"z":99,"image":"#","order":%s,"color":"%s","network":"%s"}' %(groups[c],labels[c],labels[c],network_colors[network],network))
c=c+1
myjson = "\n".join([x for x in myjson])
myjson = "[%s]" % myjson
# Plug into the template
template = get_template("connectogram")
template = add_string({"CONNECTOGRAM_JSON":myjson},template)
return template
def calculate_similarity_search(template,
query_png,
query_id,
corr_df,
button_url,
image_url,
max_results,
absolute_value,
container_width,
responsive=True):
"""Generate web interface for similarity search
template: html template (similarity_search)
query_png: image png (must be in "png" column) that others compared to
query_id: id of the query image, to look up in corr_df
corr_df: matrix of correlation values for images, with "png" column
corresponding to image paths, "tags" corresponding to
button_url: prefix of url that the "compare" button will link to.
format will be prefix/[query_id]/[other_id]
image_url: prefix of the url that the "view" button will link to.
format will be prefix/[other_id]
max_results: maximum number of results to return
absolute_value: return absolute value of score (default=True)
responsive: for larger number of returned results: will load
images only when scrolled to.
"""
query_row = corr_df[corr_df["png"] == query_png]
# Sort based on (absolute value of) similarity score
if absolute_value:
query_similar = corr_df["scores"].abs()
query_similar.sort(ascending=False)
query_similar = corr_df.loc[query_similar.index]
else:
query_similar = corr_df.sort(columns="scores", ascending=False)
# Remove the query image, and cut down to 100 results
query_similar = query_similar[query_similar.index != query_id]
if query_similar.shape[0] > max_results:
query_similar = query_similar[0:max_results]
# Prepare data for show_similarity_search
image_ids = query_similar.image_ids.tolist()
all_tags = query_similar.tags.tolist()
scores = np.round(query_similar.scores.values,2)
png_images = query_similar.png.tolist()
top_text = query_similar.top_text.tolist()
bottom_text = query_similar.bottom_text.tolist()
# Get the unique tags
unique_tags = unwrap_list_unique(all_tags)
placeholders = dict()
for tag in unique_tags: placeholders[tag] = tag.replace(" ","")
# Create custom urls
button_urls = ["%s/%s/%s" %(button_url,query_id,x) for x in image_ids]
image_urls = ["%s/%s" %(image_url,x) for x in image_ids]
portfolio = create_glassbrain_portfolio(image_paths=png_images,
all_tags=all_tags,
unique_tags=unique_tags,
placeholders=placeholders,
values=scores,
button_urls=button_urls,
image_urls=image_urls,
top_text=top_text,
bottom_text=bottom_text)
elements = {"SIMILARITY_PORTFOLIO":portfolio,
"CONTAINER_WIDTH":container_width}
template = add_string(elements,template)
html_snippet = add_string({"QUERY_IMAGE":query_png},template)
return html_snippet
def connectogram(matrix_file,groups,threshold,network_names=None):
'''connectogram: Generate a d3 connectogram for a functionary connectivity matrix.
matrix_file: a tab separated correlation matrix
groups: a list of connection groups, whatever names you want
threshold: a 99% (.99) threhsold means we include the top 1% of negative and positive values
'''
conn_df = pandas.read_csv(matrix_file,sep="\t")
if conn_df.shape[0] != conn_df.shape[1]:
print "Matrix is size [%s,%s], please check file formatting!" %(conn_df.shape[0],conn_df.shape[1])
return
if not network_names:
network_names = groups
# Fill NaN with 0 in matrix
conn_df = conn_df.fillna(0)
pos_df = conn_df.copy()
neg_df = conn_df.copy()
pos_df[pos_df<0] = 0
neg_df[neg_df>0] = 0
pos_df = pos_df.fillna(0)
neg_df = neg_df.fillna(0)
# Try getting quantiles for top and bottom
qpos = numpy.percentile(pos_df,threshold*100)
qneg = numpy.percentile(neg_df,threshold*100)
pos_df[pos_df < qpos] = 0
neg_df[neg_df > (-1*qneg)] = 0
pos_df = pos_df.fillna(0)
neg_df = neg_df.fillna(0)
thresh_df = pos_df + neg_df
# Get colors
unique_networks = numpy.unique(network_names)
colors = random_colors(len(unique_networks))
network_colors = dict()
for network in unique_networks: network_colors[network] = colors.pop(0)
# Network node labels
labels = list(thresh_df.columns)
# For each, we will output a json object with our variables of interest
myjson = []
c = 0
for row in thresh_df.iterrows():
connections = numpy.where(row[1] != 0)
network = network_names[c]
if len(connections[0]) > 0:
connection_names = list(thresh_df.columns[connections])
connection_groups = [groups[x] for x in connections[0]]
connection_labels = ["%s.%s" %(connection_groups[i],connection_names[i]) for i in range(0,len(connections[0]))]
connection_labels_string = '","'.join([str(x) for x in connection_labels])
connection_values = row[1][connections[0]]
connection_values_string = "|".join([str(x) for x in connection_values])
# If we aren't at the last row
if c+1 != thresh_df.shape[0]:
myjson.append('{"name":"%s.%s","strength":"%s","x":99,"y":99,"z":99,"image":"#","order":%s,"color":"%s","network":"%s","connections":["%s"]},' %(groups[c],labels[c],connection_values_string,labels[c],network_colors[network],network,connection_labels_string))
else:
myjson.append('{"name":"%s.%s","strength":"%s","x":99,"y":99,"z":99,"image":"#","order":%s,"color":"%s","network":"%s","connections":["%s"]}' %(groups[c],labels[c],connection_values_string,labels[c],network_colors[network],network,connection_labels_string))
# If there are no connections
else:
if c+1 != thresh_df.shape[0]:
myjson.append('{"name":"%s.%s","x":99,"y":99,"z":99,"image":"#","order":%s,"color":"%s","network":"%s"},' %(groups[c],labels[c],labels[c],network_colors[network],network))
else:
myjson.append('{"name":"%s.%s","x":99,"y":99,"z":99,"image":"#","order":%s,"color":"%s","network":"%s"}' %(groups[c],labels[c],labels[c],network_colors[network],network))
c=c+1
myjson = "\n".join([x for x in myjson])
myjson = "[%s]" % myjson
# Plug into the template
template = get_template("connectogram")
template = add_string({"CONNECTOGRAM_JSON":myjson},template)
return template