Merge pull request #341 from incf-nidash/fail-warn

Make pytest error on warnings

Author: yarikoptic

src/nidm/experiment/CDE.py

@@ -28,7 +28,8 @@ def getCDEs(file_list=None):
     cache_file_name = tempfile.gettempdir() + "/cde_graph.{}.pickle".format(h)
 
     if path.isfile(cache_file_name):
-        rdf_graph = pickle.load(open(cache_file_name, "rb"))
+        with open(cache_file_name, "rb") as fp:
+            rdf_graph = pickle.load(fp)
         getCDEs.cache = rdf_graph
         return rdf_graph
 
@@ -58,9 +59,8 @@ def getCDEs(file_list=None):
             cde_graph = nidm.experiment.Query.OpenGraph(fname)
             rdf_graph = rdf_graph + cde_graph
 
-    cache_file = open(cache_file_name, "wb")
-    pickle.dump(rdf_graph, cache_file)
-    cache_file.close()
+    with open(cache_file_name, "wb") as cache_file:
+        pickle.dump(rdf_graph, cache_file)
 
     getCDEs.cache = rdf_graph
     return rdf_graph

src/nidm/experiment/Query.py

@@ -1407,8 +1407,8 @@ def OpenGraph(file):
     # If we have a Blazegraph instance, load the data then do the rest
     if "BLAZEGRAPH_URL" in environ.keys():
         try:
-            f = open(file)
-            data = f.read()
+            with open(file) as f:
+                data = f.read()
             logging.debug("Sending {} to blazegraph".format(file))
             requests.post(
                 url=environ["BLAZEGRAPH_URL"],
@@ -1429,11 +1429,13 @@ def OpenGraph(file):
 
     pickle_file = "{}/rdf_graph.{}.pickle".format(tempfile.gettempdir(), digest)
     if path.isfile(pickle_file):
-        return pickle.load(open(pickle_file, "rb"))
+        with open(pickle_file, "rb") as fp:
+            return pickle.load(fp)
 
     rdf_graph = Graph()
     rdf_graph.parse(file, format=util.guess_format(file))
-    pickle.dump(rdf_graph, open(pickle_file, "wb"))
+    with open(pickle_file, "wb") as fp:
+        pickle.dump(rdf_graph, fp)
 
     # new graph, so to be safe clear out all cached entries
     memory.clear(warn=False)
@@ -1512,7 +1514,8 @@ def getCDEs(file_list=None):
     cache_file_name = tempfile.gettempdir() + "/cde_graph.{}.pickle".format(h)
 
     if path.isfile(cache_file_name):
-        rdf_graph = pickle.load(open(cache_file_name, "rb"))
+        with open(cache_file_name, "rb") as fp:
+            rdf_graph = pickle.load(fp)
         getCDEs.cache = rdf_graph
         return rdf_graph
 
@@ -1543,9 +1546,8 @@ def getCDEs(file_list=None):
             cde_graph = OpenGraph(fname)
             rdf_graph = rdf_graph + cde_graph
 
-    cache_file = open(cache_file_name, "wb")
-    pickle.dump(rdf_graph, cache_file)
-    cache_file.close()
+    with open(cache_file_name, "wb") as cache_file:
+        pickle.dump(rdf_graph, cache_file)
 
     getCDEs.cache = rdf_graph
     return rdf_graph

src/nidm/experiment/Utils.py

@@ -966,8 +966,8 @@ def load_nidm_terms_concepts():
     concept_url = "https://raw.githubusercontent.com/NIDM-Terms/terms/master/terms/NIDM_Concepts.jsonld"
 
     try:
-        response = urlopen(concept_url)
-        concept_graph = json.loads(response.read().decode("utf-8"))
+        with urlopen(concept_url) as response:
+            concept_graph = json.loads(response.read().decode("utf-8"))
     except Exception:
         logging.info("Error opening %s used concepts file..continuing" % concept_url)
         return None

src/nidm/experiment/tools/bidsmri2nidm.py

@@ -242,9 +242,10 @@ def addbidsignore(directory, filename_to_add):
         with open(os.path.join(directory, ".bidsignore"), "w") as text_file:
             text_file.write("%s\n" % filename_to_add)
     else:
-        if filename_to_add not in open(os.path.join(directory, ".bidsignore")).read():
-            with open(os.path.join(directory, ".bidsignore"), "a") as text_file:
-                text_file.write("%s\n" % filename_to_add)
+        with open(os.path.join(directory, ".bidsignore")) as fp:
+            if filename_to_add not in fp.read():
+                with open(os.path.join(directory, ".bidsignore"), "a") as text_file:
+                    text_file.write("%s\n" % filename_to_add)
 
 
 def addimagingsessions(

src/nidm/experiment/tools/nidm2bids.py

@@ -145,12 +145,11 @@ def GetImageFromURL(url):
     # try to open the url and get the pointed to file
     try:
         # open url and get file
-        opener = ur.urlopen(url)
-        # write temporary file to disk and use for stats
-        temp = tempfile.NamedTemporaryFile(delete=False)
-        temp.write(opener.read())
-        temp.close()
-        return temp.name
+        with ur.urlopen(url) as opener:
+            # write temporary file to disk and use for stats
+            with tempfile.NamedTemporaryFile(delete=False) as temp:
+                temp.write(opener.read())
+                return temp.name
     except Exception:
         print("ERROR! Can't open url: %s" % url)
         return -1

src/nidm/experiment/tools/nidm_affinity_propagation.py

@@ -56,9 +56,8 @@ def data_aggregation():  # all data from all the files is collected
 
         print("Your command was: " + command)
         if o is not None:
-            f = open(o, "w")
-            f.write("Your command was " + command)
-            f.close()
+            with open(o, "w") as f:
+                f.write("Your command was " + command)
         verbosity = 0
         restParser = RestParser(verbosity_level=int(verbosity))
         restParser.setOutputFormat(RestParser.OBJECT_FORMAT)
@@ -118,9 +117,10 @@ def data_aggregation():  # all data from all the files is collected
             ) as temp:  # turns the dataframe into a temporary csv
                 df.to_csv(temp.name + ".csv")
                 temp.close()
-            data = list(
-                csv.reader(open(temp.name + ".csv"))
-            )  # makes the csv a 2D list to make it easier to call the contents of certain cells
+            with open(temp.name + ".csv") as fp:
+                data = list(
+                    csv.reader(fp)
+                )  # makes the csv a 2D list to make it easier to call the contents of certain cells
             numcols = (len(data) - 1) // (
                 len(model_list)
             )  # Finds the number of columns in the original dataframe
@@ -255,20 +255,18 @@ def data_aggregation():  # all data from all the files is collected
                     + ". The model cannot run because this will skew the data. Try checking your spelling or use nidm_query.py to see other possible variables."
                 )
                 if o is not None:
-                    f = open(o, "a")
-                    f.write("Your variables were " + v)
-                    f.write(
-                        "The following variables were not found in "
-                        + nidm_file
-                        + ". The model cannot run because this will skew the data. Try checking your spelling or use nidm_query.py to see other possible variables."
-                    )
-                    f.close()
+                    with open(o, "a") as f:
+                        f.write("Your variables were " + v)
+                        f.write(
+                            "The following variables were not found in "
+                            + nidm_file
+                            + ". The model cannot run because this will skew the data. Try checking your spelling or use nidm_query.py to see other possible variables."
+                        )
                 for i in range(0, len(not_found_list)):
                     print(str(i + 1) + ". " + not_found_list[i])
                     if o is not None:
-                        f = open(o, "a")
-                        f.write(str(i + 1) + ". " + not_found_list[i])
-                        f.close()
+                        with open(o, "a") as f:
+                            f.write(str(i + 1) + ". " + not_found_list[i])
                 for j in range(len(not_found_list) - 1, 0, -1):
                     not_found_list.pop(j)
                 not_found_count = not_found_count + 1
@@ -340,13 +338,12 @@ def dataparsing():  # The data is changed to a format that is usable by the line
     )
     print()
     if o is not None:
-        f = open(o, "a")
-        f.write(df_final.to_string(header=True, index=True))
-        f.write(
-            "\n\n***********************************************************************************************************"
-        )
-        f.write("\n\nModel Results: ")
-        f.close()
+        with open(o, "a") as f:
+            f.write(df_final.to_string(header=True, index=True))
+            f.write(
+                "\n\n***********************************************************************************************************"
+            )
+            f.write("\n\nModel Results: ")
 
 
 def ap():
@@ -398,8 +395,8 @@ def ap():
     plt.title(title)
     plt.show()
     if o is not None:
-        f = open(o, "a")
-        f.close()
+        with open(o, "a"):
+            pass
 
 
 def opencsv(data):

src/nidm/experiment/tools/nidm_agglomerative_clustering.py

@@ -56,9 +56,8 @@ def data_aggregation():  # all data from all the files is collected
 
         print("Your command was: " + command)
         if o is not None:
-            f = open(o, "w")
-            f.write("Your command was " + command)
-            f.close()
+            with open(o, "w") as f:
+                f.write("Your command was " + command)
         verbosity = 0
         restParser = RestParser(verbosity_level=int(verbosity))
         restParser.setOutputFormat(RestParser.OBJECT_FORMAT)
@@ -118,9 +117,10 @@ def data_aggregation():  # all data from all the files is collected
             ) as temp:  # turns the dataframe into a temporary csv
                 df.to_csv(temp.name + ".csv")
                 temp.close()
-            data = list(
-                csv.reader(open(temp.name + ".csv"))
-            )  # makes the csv a 2D list to make it easier to call the contents of certain cells
+            with open(temp.name + ".csv") as fp:
+                data = list(
+                    csv.reader(fp)
+                )  # makes the csv a 2D list to make it easier to call the contents of certain cells
             numcols = (len(data) - 1) // (
                 len(model_list)
             )  # Finds the number of columns in the original dataframe
@@ -255,20 +255,18 @@ def data_aggregation():  # all data from all the files is collected
                     + ". The model cannot run because this will skew the data. Try checking your spelling or use nidm_query.py to see other possible variables."
                 )
                 if o is not None:
-                    f = open(o, "a")
-                    f.write("Your variables were " + v)
-                    f.write(
-                        "The following variables were not found in "
-                        + nidm_file
-                        + ". The model cannot run because this will skew the data. Try checking your spelling or use nidm_query.py to see other possible variables."
-                    )
-                    f.close()
+                    with open(o, "a") as f:
+                        f.write("Your variables were " + v)
+                        f.write(
+                            "The following variables were not found in "
+                            + nidm_file
+                            + ". The model cannot run because this will skew the data. Try checking your spelling or use nidm_query.py to see other possible variables."
+                        )
                 for i in range(0, len(not_found_list)):
                     print(str(i + 1) + ". " + not_found_list[i])
                     if o is not None:
-                        f = open(o, "a")
-                        f.write(str(i + 1) + ". " + not_found_list[i])
-                        f.close()
+                        with open(o, "a") as f:
+                            f.write(str(i + 1) + ". " + not_found_list[i])
                 for j in range(len(not_found_list) - 1, 0, -1):
                     not_found_list.pop(j)
                 not_found_count = not_found_count + 1
@@ -340,13 +338,12 @@ def dataparsing():  # The data is changed to a format that is usable by the line
     )
     print()
     if o is not None:
-        f = open(o, "a")
-        f.write(df_final.to_string(header=True, index=True))
-        f.write(
-            "\n\n***********************************************************************************************************"
-        )
-        f.write("\n\nModel Results: ")
-        f.close()
+        with open(o, "a") as f:
+            f.write(df_final.to_string(header=True, index=True))
+            f.write(
+                "\n\n***********************************************************************************************************"
+            )
+            f.write("\n\nModel Results: ")
 
 
 def ac():
@@ -386,8 +383,8 @@ def ac():
     plt.show()
 
     if o is not None:
-        f = open(o, "a")
-        f.close()
+        with open(o, "a"):
+            pass
 
 
 def opencsv(data):

src/nidm/experiment/tools/nidm_gmm.py

@@ -85,9 +85,8 @@ def data_aggregation():  # all data from all the files is collected
 
         print("Your command was: " + command)
         if o is not None:
-            f = open(o, "w")
-            f.write("Your command was " + command)
-            f.close()
+            with open(o, "w") as f:
+                f.write("Your command was " + command)
         verbosity = 0
         restParser = RestParser(verbosity_level=int(verbosity))
         restParser.setOutputFormat(RestParser.OBJECT_FORMAT)
@@ -149,9 +148,11 @@ def data_aggregation():  # all data from all the files is collected
             ) as temp:  # turns the dataframe into a temporary csv
                 df.to_csv(temp.name + ".csv")
                 temp.close()
-            data = list(
-                csv.reader(open(temp.name + ".csv"))
-            )  # makes the csv a 2D list to make it easier to call the contents of certain cells
+
+            with open(temp.name + ".csv") as fp:
+                data = list(
+                    csv.reader(fp)
+                )  # makes the csv a 2D list to make it easier to call the contents of certain cells
 
             var_list = variables.split(",")  # makes a list of the independent variables
             numcols = (len(data) - 1) // (
@@ -293,20 +294,18 @@ def data_aggregation():  # all data from all the files is collected
                     + ". The model cannot run because this will skew the data. Try checking your spelling or use nidm_query.py to see other possible variables."
                 )
                 if o is not None:
-                    f = open(o, "a")
-                    f.write("Your variables were " + v)
-                    f.write(
-                        "The following variables were not found in "
-                        + nidm_file
-                        + ". The model cannot run because this will skew the data. Try checking your spelling or use nidm_query.py to see other possible variables."
-                    )
-                    f.close()
+                    with open(o, "a") as f:
+                        f.write("Your variables were " + v)
+                        f.write(
+                            "The following variables were not found in "
+                            + nidm_file
+                            + ". The model cannot run because this will skew the data. Try checking your spelling or use nidm_query.py to see other possible variables."
+                        )
                 for i in range(0, len(not_found_list)):
                     print(str(i + 1) + ". " + not_found_list[i])
                     if o is not None:
-                        f = open(o, "a")
-                        f.write(str(i + 1) + ". " + not_found_list[i])
-                        f.close()
+                        with open(o, "a") as f:
+                            f.write(str(i + 1) + ". " + not_found_list[i])
                 for j in range(len(not_found_list) - 1, 0, -1):
                     not_found_list.pop(j)
                 not_found_count = not_found_count + 1
@@ -388,13 +387,12 @@ def dataparsing():  # The data is changed to a format that is usable by the line
     )
     print()
     if o is not None:
-        f = open(o, "a")
-        f.write(df_final.to_string(header=True, index=True))
-        f.write(
-            "\n\n***********************************************************************************************************"
-        )
-        f.write("\n\nModel Results: ")
-        f.close()
+        with open(o, "a") as f:
+            f.write(df_final.to_string(header=True, index=True))
+            f.write(
+                "\n\n***********************************************************************************************************"
+            )
+            f.write("\n\nModel Results: ")
 
 
 def cluster_number():
@@ -562,8 +560,8 @@ def cluster_number():
         plt.show()
 
     if o is not None:
-        f = open(o, "a")
-        f.close()
+        with open(o, "a"):
+            pass
 
 
 def opencsv(data):