checksum on shpfile with metadata in npz of matrix

scenariosAleatoires/indice_biodiversite_2.py

@@ -22,14 +22,18 @@ class Biodiversity:
 	function P(i,j) = exp(-l*d). The coefficient l (lambda) is determined for a given
 	minimal probability at the maximal distance d.
 	'''
-	def __init__(self, initial_patches, dmax, epsilon=0.001):
+	def __init__(self, initial_patches, patches_md5sum, dmax, epsilon=0.001):
 		'''
 		Creates an indicator with initial parameters
+		The original file giving the patches is needed for checking the correspondance
+		between this file and the matrix cached in a file.
 
+		:param patches_filename file will be used for computing checksum
 		:param dmax: the maximum distance threshold for considering a pair of patches
 		:param epsilon: the minimum probability threshold that should resulted of
 		 the exponential function at distance dmax
 		'''
+		self.md5sum = patches_md5sum
 		self.dmax = dmax
 		self.epsilon = epsilon
 		self.final_result_ratio = pow(initial_patches['SURF_PARC'].sum(), 2) / 100000
@@ -44,13 +48,13 @@ class Biodiversity:
 		# get the lambda value for given thresholds
 		l = -log(self.epsilon, 2)/self.dmax
 		# Creating a buffer for these patches, for finding patches under the distance threshold by intersection
-		patches = patches
 		buffer = patches.buffer(self.dmax)
 		index = buffer.sindex
 		# sparse matrix initialisation
 		self.probabilities = lil_matrix((len(patches),len(patches)), dtype='d')
+		geom_idx=patches.columns.get_loc('geometry')
 		for i in tqdm(range(len(patches))):
-			geom_i = patches.iloc[i].geometry
+			geom_i = patches.iat[i,geom_idx]
 			for fid in list(index.intersection(geom_i.bounds)):
 				# proba zero on the diagonal
 				if i == fid:
@@ -58,10 +62,10 @@ class Biodiversity:
 				if i >= fid:
 					# the matrix is symetric, so we process only one half and
 					# affect the proba on two sides
-					d = patches.iloc[fid].geometry.boundary.distance(geom_i.boundary)
+					d = patches.iat[fid,geom_idx].boundary.distance(geom_i.boundary)
 					if d <= self.dmax:
 						# patches i and fid have a distance less than the max distance
-						p = patches.iloc[fid].geometry.area*geom_i.area*l*exp(-l*d)
+						p = patches.iat[fid,geom_idx].area*geom_i.area*l*exp(-l*d)
 						self.probabilities[i,fid] = p
 						self.probabilities[fid,i] = p
 					else:
@@ -85,16 +89,23 @@ class Biodiversity:
 		Save the matrix of probabilities and initial parameters in a file (npz format).
 		'''
 		m = self.probabilities
-		np.savez(filename, dmax=self.dmax, epsilon=self.epsilon, data=m.data,
-			indices=m.indices, indptr=m.indptr, shape=m.shape)
+		np.savez(filename, dmax=self.dmax, epsilon=self.epsilon, md5sum=self.md5sum,
+			data=m.data, indices=m.indices, indptr=m.indptr, shape=m.shape)
 
 	def load_probabilities(self, filename):
 		'''
 		Load the matrix of probabilities and initial parameters from a file (npz format).
+
+		The correspondance will be checked between
+		 class attributes and metadata stored in the file
 		'''
 		loader = np.load(filename)
-		self.dmax = loader['dmax']
-		self.epsilon = loader['epsilon']
+		error_message = ('Matrix metadata {0} does\'nt correspond to metadata given at indicator construction'
+			'\n\t{0} in metadata: {1} while asked: {2}'
+			'\n\tmatrix filename: ') + filename
+		for k1,k2 in [[self.dmax,'dmax'],[self.epsilon,'epsilon'],[self.md5sum,'md5sum']]:
+			if k2 not in loader or k1!=loader[k2]:
+				raise ValueError(error_message.format(k2, dict(loader.items()).get(k2, 'NA'), k1))
 		self.probabilities = csr_matrix(
 			(loader['data'], loader['indices'], loader['indptr']),
 			shape=loader['shape']
@@ -102,10 +113,17 @@ class Biodiversity:
 
 if __name__ == '__main__':
 	import geopandas as gpd
-	patches = gpd.GeoDataFrame.from_file("../output/PAT_patches/PAT_patches.shp", encoding='utf-8')
-	matrix_filename='../output/matrix_biodiversity.npz'
 	import os
-	biodiv = Biodiversity(patches, 1000, 0.001)
+	import hashlib
+	filename = "../output/PAT_patches/PAT_patches.shp"
+	patches = gpd.GeoDataFrame.from_file(filename, encoding='utf-8')
+	patches = patches[patches['cultgeopat']!='Non Considérée']
+	matrix_filename='../output/matrix_biodiversity_test.npz'
+	hash_md5 = hashlib.md5()
+	with open(filename, "rb") as f:
+		for chunk in iter(lambda: f.read(4096), b""):
+			hash_md5.update(chunk)
+	biodiv = Biodiversity(patches, hash_md5.hexdigest(), 1000, 0.001)
 	if not os.path.isfile(matrix_filename):
 		biodiv.compute_dispersal_probabilities(patches)
 		biodiv.save_probabilities(matrix_filename)