added ability to export extracted signals as csv

zhounapeuw · Sep 29, 2022 · 696655f · 696655f
1 parent f045c27
commit 696655f
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Showing 3 changed files with 17 additions and 13 deletions.
diff --git a/napeca/.ipynb_checkpoints/main_parallel-checkpoint.ipynb b/napeca/.ipynb_checkpoints/main_parallel-checkpoint.ipynb
@@ -162,11 +162,11 @@
     "    pool = mp.Pool(processes=num_processes)\n",
     "    \n",
     "    # perform parallel processing; pass iterable list of file params to the analysis module selection code\n",
-    "    pool.map(single_file_process.process, fparams)\n",
+    "    #pool.map(single_file_process.process, fparams)\n",
     "    \n",
     "    ## for testing\n",
-    "    #for fparam in fparams:\n",
-    "    #    single_file_process.process(fparam) \n",
+    "    for fparam in fparams:\n",
+    "        single_file_process.process(fparam) \n",
     "\n",
     "    pool.close()\n",
     "    pool.join()"
@@ -190,13 +190,6 @@
     "    \n",
     "    batch_process(fdir)\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/napeca/calculate_neuropil.py b/napeca/calculate_neuropil.py
@@ -11,6 +11,7 @@
 import time
 import warnings
 import re
+from fnmatch import fnmatch
 import matplotlib
 import matplotlib.pyplot as plt
 
@@ -384,13 +385,23 @@ def save_neuropil_corrected_signals(indir, signals, neuropil_signals, beta_rois,
                                                                                             min_neuropil_radius,
                                                                                             neuropil_radius)),
             corrected_signals)
+        np.savetxt(
+            os.path.join(indir, '%s_neuropil_corrected_signals_%d_%d_betacalculated.csv' % (fname,
+                                                                                            min_neuropil_radius,
+                                                                                            neuropil_radius)),
+                                                                                            corrected_signals, delimiter=",")
     else:
         corrected_signals = signals - beta_rois * neuropil_signals
         np.save(os.path.join(indir, '%s_neuropil_corrected_signals_%d_%d_beta_%.1f.npy' % (fname,
                                                                                            min_neuropil_radius,
                                                                                            neuropil_radius,
                                                                                            beta_rois)),
                 corrected_signals)
+        np.savetxt(os.path.join(indir, '%s_neuropil_corrected_signals_%d_%d_beta_%.1f.csv' % (fname,
+                                                                                           min_neuropil_radius,
+                                                                                           neuropil_radius,
+                                                                                           beta_rois)),
+                                                                                           corrected_signals, delimiter=",")
 
 
 def CDFplot(x, ax, color=None, label='', linetype='-'):
@@ -423,10 +434,10 @@ def load_analyzed_data(indir, fname):
     extract_sig_file = [f for f in tempfiles if 'extractedsignals.npy' in f and fbasename in f][0]
     analyzed_data['extract_signals'] = np.squeeze(np.load(os.path.join(indir, extract_sig_file)))
     # load masks
-    npil_sig_file = [f for f in tempfiles if 'neuropilsignals' in f and fbasename in f][0]
+    npil_sig_file = [f for f in tempfiles if fnmatch(f, '*neuropilsignals*.npy') and fbasename in f][0]
     analyzed_data['npil_sig'] = np.load(os.path.join(indir, npil_sig_file))
     # load masks
-    npilcorr_sig_file = [f for f in tempfiles if 'neuropil_corrected_signals' in f and fbasename in f][0]
+    npilcorr_sig_file = [f for f in tempfiles if fnmatch(f, '*neuropil_corrected_signals*.npy') and fbasename in f][0]
     analyzed_data['npil_corr_sig'] = np.load(os.path.join(indir, npilcorr_sig_file))
 
     return analyzed_data

diff --git a/napeca/sima_extract_roi_sig.py b/napeca/sima_extract_roi_sig.py
@@ -24,5 +24,5 @@ def extract(fpath):
     signals = dataset.extract(rois)
     extracted_signals = np.asarray(signals['raw'])  # turn signals list into an np array
     np.save(os.path.join(fdir, fname + '_extractedsignals.npy'), extracted_signals)
-
+    np.savetxt(os.path.join(fdir, fname + '_extractedsignals.csv'), np.squeeze(extracted_signals), delimiter=",")
     print('Done with extracting roi signals')