<jupyter_start><jupyter_text>Generate unique IDs<jupyter_code>#now that our dataset looks good, we can generate unique IDs for each of the nuclei in our dataset
df_merge_OUTPUT = df1
df_merge_OUTPUT.reset_index(inplace=True)
df_merge_OUTPUT.rename(columns={'ObjectNumber_NUC':'NUC_ID'}, inplace=True)
df_merge_OUTPUT.head()
#CellProfiler can create empty NaN rows, so we can just filter them out using dropna
df_merge_OUTPUT.dropna(subset=['NUC_ID'], inplace=True)
df_merge_OUTPUT.head()
df_merge_OUTPUT.columns.values
#again if you have forgotten to add some data, you can add that now e.g. experiment id or biorep
#df_merge_OUTPUT['Metadata_exp_NUC'] = "117"
#df_merge_OUTPUT['Metadata_biorep_NUC'] = "repB"
#conversion to string for concatenation
### CODE CAN BE IMPROVED ###
df_merge_OUTPUT["NUC_ID"] = df_merge_OUTPUT["NUC_ID"].astype(float).astype(int).astype(str)
df_merge_OUTPUT["ImageNumber"] = df_merge_OUTPUT["ImageNumber"].astype(float).astype(int).astype(str)
df_merge_OUTPUT['Metadata_date_NUC'] = df_merge_OUTPUT['Metadata_date_NUC'].astype(float).astype(int).astype(str)
df_merge_OUTPUT['Metadata_timepoint_NUC'] = df_merge_OUTPUT['Metadata_timepoint_NUC'].astype(str)
#df_merge_OUTPUT.set_index(["Metadata_timepoint", "Metadata_biorep"],inplace=True)
df_merge_OUTPUT["Unique_ID"] = df_merge_OUTPUT['Metadata_exp_NUC'].str.cat(df_merge_OUTPUT[['Metadata_date_NUC', 'Metadata_biorep_NUC','Metadata_timepoint_NUC','ImageNumber','NUC_ID']], sep="_") 
df_merge_OUTPUT["Unique_ID"].head()<jupyter_output><empty_output><jupyter_text>export thhese nuclei to imagej macro<jupyter_code>#now we can perform some calculations to get the right coordinates of each nuclei on our full sized image dataset:

# multiply by 8 to compensate for downsizing in cellprofiler
df_merge_OUTPUT['NUC_x0'] = df_merge_OUTPUT[('Location_Center_X_NUC')]*8 #correcting for downscaling
df_merge_OUTPUT['NUC_y0'] = df_merge_OUTPUT[('Location_Center_Y_NUC')]*8 #correcting for downscaling
df_merge_OUTPUT['CYTO_x0'] = df_merge_OUTPUT[('Location_CenterMassIntensity_X_gB_small')]*8 #correcting for downscaling
df_merge_OUTPUT['CYTO_y0'] = df_merge_OUTPUT[('Location_CenterMassIntensity_Y_gB_small')]*8 #correcting for downscaling
#to make things easier downstream, we will rename our individual channels as C1, C2, C3, or C4

#get the names of the current channels:
ImageURL_df.columns.values
#rename the URL columns because subsetting with multi-index is HARD
df_merge_OUTPUT['URL_C1'] = df_merge_OUTPUT[('URL_gB')] #this will be red in imageJ
df_merge_OUTPUT['URL_C2'] = df_merge_OUTPUT[('URL_DAPI')] #this will be green in imageJ
df_merge_OUTPUT['URL_C3'] = df_merge_OUTPUT[('URL_H3K9me3')] #this will be blue in imageJ
#df_merge_OUTPUT['URL_C4'] = df_merge_OUTPUT[('URL_AcMT')] #this will be white in imageJ
#export this for all nuclei
df_merge_EXPORT= df_merge_OUTPUT[["Unique_ID", 'NUC_x0','NUC_y0','URL_C1','URL_C2','URL_C3','AreaShape_Orientation','CYTO_x0','CYTO_y0']] #MAKE THESE URLS for C1, C2, C3 and C4. You have a method for finding these names
df_merge_EXPORT.to_csv(f'{name_var}_IDsandCoords.csv', index=False)
#used the below macro to export all the single channels
#sometimes you will need to test things using smaller datasets, you can export a random subset of you dataset instead using the following code:
#num = 1000
#df_merge_EXPORT_rand = df_merge_EXPORT.sample(n=num)
#df_merge_EXPORT_rand.to_csv(f'{name_var}_IDsandCoords_rand_{num}.csv', index=False)<jupyter_output><empty_output>
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