Added PDF_report function

Still needs some more coding to make the PDF look fancy, and also it would be interesting to add a new function in order to generate the PDF report for a set of files.

src/pyNAVIS/functions.py

@@ -24,13 +24,16 @@
 import time
 import copy
 import random
+import datetime
 
+import matplotlib.backends.backend_pdf
 import matplotlib.pyplot as plt
 import numpy as np
 
 from .loaders import SpikesFile
 from .savers import Savers
 from .utils import Utils
+from .plots import Plots
 
 class Functions:
 
@@ -260,4 +263,82 @@ def extract_channels_activities(spikes_file, addresses, reset_addresses = True,
 		if reset_addresses == True:
 			new_spikes_file.addresses = [addr - addresses[0] for addr in new_spikes_file.addresses]
 		new_spikes_file.timestamps = spikes_per_channels_ts
-		return new_spikes_file
+		return new_spikes_file
+
+
+
+
+	@staticmethod
+	def PDF_report(spikes_file, settings, output_path, plots = ["Spikegram", "Sonogram", "Histogram", "Average activity", "Difference between L/R"], vector = False, verbose = False):
+		"""
+		Generates a PDF report with the spikegram, sonogram, histogram, average activity and difference between L/R plots obtained from the input SpikesFile or path containing SpikeFiles.
+		
+		Parameters:
+				spikes_file (SpikesFile or string): File or path to use.
+				settings (MainSettings): Configuration parameters for the input file.
+				vector (boolean, optional): Set to True if you want the Spikegram plot vectorized. Note: this may make your PDF heavy.
+				verbose (boolean, optional): Set to True if you want the execution time of the function to be printed.
+
+		Returns:
+				None.
+		
+		Notes:   
+				If the path used as input is a folder instead of a spikes file, the PDF report is generated for every spikes file contained in the folder.
+		"""
+
+		if isinstance(spikes_file, str):
+
+			spikes_file_extension = os.path.splitext(spikes_file)
+
+			if spikes_file_extension == ".aedat":
+				spikes_file = Loaders.loadAEDAT(spikes_file, settings)
+			elif spikes_file_extension == ".csv":
+				spikes_file = Loaders.loadCSV(spikes_file, settings)
+			elif spikes_file_extension == ".txt":
+				spikes_file = Loaders.loadZynqGrabberData(spikes_file, settings)
+
+			spikes_file = Functions.adapt_SpikesFile(spikes_file, settings)
+
+		if isinstance(spikes_file, SpikesFile):
+
+			pdf = matplotlib.backends.backend_pdf.PdfPages(output_path)
+
+			# Spikegram
+			if any("Spikegram" in s for s in plots):
+				spikegram = Plots.spikegram(spikes_file, settings, )
+				pdf.savefig(spikegram)
+				plt.draw()
+			# Sonogram
+			if any("Sonogram" in s for s in plots):
+				sonogram = Plots.sonogram(spikes_file, settings)		
+				pdf.savefig(sonogram)
+				plt.draw()
+			# Histogram
+			if any("Histogram" in s for s in plots):
+				Plots.histogram(spikes_file, settings,)		
+				pdf.savefig()
+				plt.draw()
+			# Average activity
+			if any("Average activity" in s for s in plots):
+				Plots.average_activity(spikes_file, settings,)		
+				pdf.savefig()
+				plt.draw()
+			# Difference between L/R
+			if settings.mono_stereo == 1 and any("Difference between L/R" in s for s in plots):		
+				Plots.difference_between_LR(spikes_file, settings,)		
+				pdf.savefig()
+				plt.draw()
+
+
+			d = pdf.infodict()
+			d['Title'] = 'pyNAVIS report'
+			d['Author'] = 'Juan P. Dominguez-Morales'
+			d['Subject'] = 'pyNAVIS report'
+			d['Keywords'] = 'pyNAVIS'
+			d['CreationDate'] = datetime.datetime.today()
+			d['ModDate'] = datetime.datetime.today()
+
+			pdf.close()
+
+		else:
+			print("[Functions.PDF_report] > InputFileError: the input SpikesFile is not valid.")