def saveTable(table: DecodedIntensityTable, savename: str):
"""
Reformats and saves a DecodedIntensityTable.
"""
intensities = IntensityTable(
table.where(table[Features.PASSES_THRESHOLDS], drop=True))
traces = intensities.stack(traces=(Axes.ROUND.value, Axes.CH.value))
# traces = table.stack(traces=(Axes.ROUND.value, Axes.CH.value))
traces = traces.to_features_dataframe()
traces.to_csv(savename)
spot_intensities, results = psd.run(filtered_imgs)
spot_intensities = IntensityTable(
spot_intensities.where(spot_intensities[Features.PASSES_THRESHOLDS],
drop=True))
###################################################################################################
# Here, we:
#
# 1. Pick a rolony that was succesfully decoded to a gene.
# 2. Pull out the average pixel trace for that rolony.
# 3. Plot that pixel trace against the barcode of that gene.
#
# In order to assess, visually, how close decoded barcodes match their targets.
# reshape the spot intensity table into a RxC barcode vector
pixel_traces = spot_intensities.stack(traces=(Axes.ROUND.value, Axes.CH.value))
# extract dataframe from spot intensity table for indexing purposes
pixel_traces_df = pixel_traces.to_features_dataframe()
pixel_traces_df['area'] = np.pi * pixel_traces_df.radius**2
# pick index of a barcode that was read and decoded from the ImageStack
ind = 4
# get the the corresponding gene this barcode was decoded to
gene = pixel_traces_df.loc[ind].target
# query the codebook for the actual barcode corresponding to this gene
real_barcode = experiment.codebook[experiment.codebook.target == gene].stack(
traces=(Axes.ROUND.value, Axes.CH.value)).values[0]
read_out_barcode = pixel_traces[ind, :]
plt.gca().add_collection(pc)
plt.figure(figsize=(10,10))
plt.imshow(new_image[600:800, 100:300], cmap = 'nipy_spectral');
plt.axis('off');
plt.title('Coded rolonies, zoomed in');
# EPY: END code
# EPY: START markdown
#### visualization of matched barcodes
#here, we 1. pick a rolony that was succesfully decoded to a gene. 2. pull out the average pixel trace for that rolony and 3. plot that pixel trace against the barcode of that gene
# EPY: END markdown
# EPY: START code
# reshape the spot intensity table into a RxC barcode vector
pixel_traces = spot_intensities.stack(traces=(Indices.ROUND.value, Indices.CH.value))
# extract dataframe from spot intensity table for indexing purposes
pixel_traces_df = pixel_traces.to_features_dataframe()
pixel_traces_df['area'] = np.pi*pixel_traces_df.radius**2
# pick index of a barcode that was read and decoded from the ImageStack
ind = 4
# The test will error here on pixel_traces[ind,:] with an out of index error
# because we are using the test data.
# get the the corresponding gene this barcode was decoded to
gene = pixel_traces_df.loc[ind].target
# query the codebook for the actual barcode corresponding to this gene