minor

geobipy/src/classes/data/dataset/FdemData.py

@@ -554,7 +554,7 @@ def read_csv(cls, dataFilename, system):
         try:
             df = read_csv(dataFilename, index_col=False, usecols=channels, skipinitialspace = True)
         except:
-            df = read_csv(dataFilename, index_col=False, usecols=channels, sep='\s+', skipinitialspace = True)
+            df = read_csv(dataFilename, index_col=False, usecols=channels, sep='\\s+', skipinitialspace = True)
         df = df.replace('NaN',nan)
 
         # Assign columns to variables

geobipy/src/classes/mesh/RectilinearMesh2D_stitched.py

@@ -175,6 +175,7 @@ def pcolor(self, values, **kwargs):
         y_edges = self.y_edges
         y_edges = utilities._power(y_edges, self.y_log)
 
+        min_edge = min(y_edges[isfinite(y_edges)])
         max_edge = max(y_edges[isfinite(y_edges)])
         if npany(isinf(y_edges)):
             if nanmin(y_edges) == -inf:

geobipy/src/classes/model/Model.py

@@ -436,7 +436,6 @@ def prior_derivative(self, order):
 
         if self.gradient.hasPrior:
             Wz = self.mesh.gradient_operator
-
             operator += dot(Wz.T, dot(self.gradient.priorDerivative(order=2), Wz))
 
         return dot(operator, self.values.prior.deviation(self.values)) if order == 1 else operator

geobipy/src/classes/statistics/Histogram.py

@@ -506,7 +506,7 @@ def sample(self, n_samples, log=None):
         values, dum = utilities._log(values, log)
         return values
 
-    def transparency(self, percent, log=None, axis=0, **kwargs):
+    def transparency(self, percent=95.0, log=None, axis=0, **kwargs):
         """Return a transparency value between 0 and 1 based on the difference between credible invervals of the hitmap.
 
         Higher ranges in credibility are mapped to more transparency.