Merge pull request #1435 from danforthcenter/fix-PTC-W0010

fix-PTC-W0010

Author: nfahlgren

plantcv/learn/naive_bayes.py

@@ -149,15 +149,15 @@ def naive_bayes_multiclass(samples_file, outfile, mkplots=False):
         for channel, cls in pdfs.items():
             _plot_pdf(channel, os.path.dirname(outfile), **cls)
     # Write the PDFs to a text file
-    out = open(outfile, "w")
-    # Write the column labels
-    out.write("class\tchannel\t" + "\t".join(map(str, range(0, 256))) + "\n")
-    # For each channel
-    for channel, cls in pdfs.items():
-        # For each class
-        for class_name, pdf in cls.items():
-            # Each row is the PDF for the given class and color channel
-            out.write(class_name + "\t" + channel + "\t" + "\t".join(map(str, pdf)) + "\n")
+    with open(outfile, "w") as out:
+        # Write the column labels
+        out.write("class\tchannel\t" + "\t".join(map(str, range(0, 256))) + "\n")
+        # For each channel
+        for channel, cls in pdfs.items():
+            # For each class
+            for class_name, pdf in cls.items():
+                # Each row is the PDF for the given class and color channel
+                out.write(class_name + "\t" + channel + "\t" + "\t".join(map(str, pdf)) + "\n")
 
 
 def _split_plant_background_signal(channel, mask):

plantcv/plantcv/classes.py

@@ -178,39 +178,39 @@ def save_results(self, filename, outformat="json"):
 
         elif outformat.upper() == "CSV":
             # Open output CSV file
-            csv_table = open(filename, "w")
-            # Gather any additional metadata
-            metadata_key_list = list(self.metadata.keys())
-            metadata_val_list = [val["value"] for val in self.metadata.values()]
-            # Write the header
-            header = metadata_key_list + ["sample", "trait", "value", "label"]
-            csv_table.write(",".join(map(str, header)) + "\n")
-            # Iterate over data samples
-            for sample in self.observations:
-                # Iterate over traits for each sample
-                for var in self.observations[sample]:
-                    val = self.observations[sample][var]["value"]
-                    # If the data type is a list or tuple we need to unpack the data
-                    if isinstance(val, (list, tuple)):
-                        # Combine each value with its label
-                        for value, label in zip(self.observations[sample][var]["value"],
-                                                self.observations[sample][var]["label"]):
-                            # Skip list of tuple data types
-                            if not isinstance(value, tuple):
-                                # Save one row per value-label
-                                row = metadata_val_list + [sample, var, value, label]
-                                csv_table.write(",".join(map(str, row)) + "\n")
-                    # If the data type is Boolean, store as a numeric 1/0 instead of True/False
-                    elif isinstance(val, bool):
-                        row = metadata_val_list + [sample, var, int(self.observations[sample][var]["value"]),
-                                                   self.observations[sample][var]["label"]]
-                        csv_table.write(",".join(map(str, row)) + "\n")
-                    # For all other supported data types, save one row per trait
-                    # Assumes no unusual data types are present (possibly a bad assumption)
-                    else:
-                        row = metadata_val_list + [sample, var, self.observations[sample][var]["value"],
-                                                   self.observations[sample][var]["label"]]
-                        csv_table.write(",".join(map(str, row)) + "\n")
+            with open(filename, "w") as csv_table:
+                # Gather any additional metadata
+                metadata_key_list = list(self.metadata.keys())
+                metadata_val_list = [val["value"] for val in self.metadata.values()]
+                # Write the header
+                header = metadata_key_list + ["sample", "trait", "value", "label"]
+                csv_table.write(",".join(map(str, header)) + "\n")
+                # Iterate over data samples
+                for sample in self.observations:
+                    # Iterate over traits for each sample
+                    for var in self.observations[sample]:
+                        val = self.observations[sample][var]["value"]
+                        # If the data type is a list or tuple we need to unpack the data
+                        if isinstance(val, (list, tuple)):
+                            # Combine each value with its label
+                            for value, label in zip(self.observations[sample][var]["value"],
+                                                    self.observations[sample][var]["label"]):
+                                # Skip list of tuple data types
+                                if not isinstance(value, tuple):
+                                    # Save one row per value-label
+                                    row = metadata_val_list + [sample, var, value, label]
+                                    csv_table.write(",".join(map(str, row)) + "\n")
+                        # If the data type is Boolean, store as a numeric 1/0 instead of True/False
+                        elif isinstance(val, bool):
+                            row = metadata_val_list + [sample, var, int(self.observations[sample][var]["value"]),
+                                                       self.observations[sample][var]["label"]]
+                            csv_table.write(",".join(map(str, row)) + "\n")
+                        # For all other supported data types, save one row per trait
+                        # Assumes no unusual data types are present (possibly a bad assumption)
+                        else:
+                            row = metadata_val_list + [sample, var, self.observations[sample][var]["value"],
+                                                       self.observations[sample][var]["label"]]
+                            csv_table.write(",".join(map(str, row)) + "\n")
 
     def plot_dists(self, variable):
         """Plot a distribution of data.

plantcv/utils/converters.py

@@ -204,13 +204,12 @@ def tabulate_bayes_classes(input_file, output_file):
                 class_dict[class_name].append("")
 
     # Open the output file
-    out = open(output_file, "w")
-    # Create the output table
-    class_names = class_dict.keys()
-    out.write("\t".join(map(str, class_names)) + "\n")
-    for i in range(0, total_rgb):
-        row = []
-        for class_name in class_names:
-            row.append(class_dict[class_name][i])
-        out.write("\t".join(map(str, row)) + "\n")
-    out.close()
+    with open(output_file, "w") as out:
+        # Create the output table
+        class_names = class_dict.keys()
+        out.write("\t".join(map(str, class_names)) + "\n")
+        for i in range(0, total_rgb):
+            row = []
+            for class_name in class_names:
+                row.append(class_dict[class_name][i])
+            out.write("\t".join(map(str, row)) + "\n")