More robust error handling; check third-party imports

libkinetics/__init__.py

@@ -1,10 +1,19 @@
 #!/usr/bin/python
 # -*- coding: utf-8 -*-
 
-from scipy import stats, optimize
-import numpy as np
 import logging
 import warnings
+import operator
+
+try:
+    import numpy as np
+except ImportError:
+    print('----- NumPy must be installed! -----')
+
+try:
+    from scipy import stats, optimize
+except ImportError:
+    print('----- SciPy must be installed! -----')
 
 
 class Error(Exception):
@@ -128,6 +137,9 @@ class Measurement():
 
         return results
 
+    def get_concentration(self):
+        return self.concentration
+
 
 class Experiment():
 
@@ -142,7 +154,8 @@ class Experiment():
             and log file.
         measurements: array_like
             list of individual measurements of the experiment.
-            Usually defined by different substrate concentrations.
+            Individual measurements are sorted by their substrate
+            concentration.
         fit_to_replicates: boolean
             whether to fit to individual replicates instead to the average of
             each measurement.
@@ -192,12 +205,22 @@ class Experiment():
 
         # parse data files and generate measurements
         for csvfile in data_files:
+            try:
                 tmp = np.genfromtxt(str(csvfile), comments='#')
                 with open(str(csvfile)) as datafile:
                     head = [next(datafile) for x in range(2)]
+            except OSError:
+                msg = "Failed reading file {}".format(str(csvfile))
+                self.logger.error(msg)
+                raise
+
             # extract concentration and unit from header
             # TODO: move unit to parameter
-            # TODO: More error-proof header detection
+            # check header for correct number of items
+            if len(head) < 2 or len(head) > 2:
+                msg = 'Parsing header of data files failed! Wrong format?'
+                self.logger.error(msg)
+                raise Error(msg)
             conc = head[0].strip('#').strip()
             unit = head[1].strip('#').strip()
             # split x and y data apart
@@ -209,6 +232,10 @@ class Experiment():
             measurement = Measurement((x, y), conc, unit, self)
             self.measurements.append(measurement)
 
+        # sort measurements by concentration
+        self.measurements = sorted(self.measurements,
+                                   key=operator.attrgetter('concentration'))
+
         # iterate over all measurements
         for m in self.measurements:
             if self.fit_to_replicates: