Format code with black

Format some modules, in special the new ones.

core/controller/ticket_controller.py

@@ -1,6 +1,7 @@
 """
 Module that contains TicketController class
 """
+
 import json
 from copy import deepcopy
 from environment import (

core/utils/das.py

@@ -20,9 +20,10 @@ def get_lumi_ranges(i):
     result = []
     for _, b in itertools.groupby(enumerate(i), lambda pair: pair[1] - pair[0]):
         b = list(b)
-        result.append([b[0][1],b[-1][1]])
+        result.append([b[0][1], b[-1][1]])
     return result
 
+
 def das_do_command(query):
     """
     A simple wrapper for dasgoclient.
@@ -34,10 +35,13 @@ def das_do_command(query):
        list[str]: the dasgoclient command output split by newlines.
 
     """
-    cmd = 'dasgoclient --query="%s"'%(query)
-    out = subprocess.check_output(cmd, shell=True, executable="/bin/bash").decode('utf8')
+    cmd = 'dasgoclient --query="%s"' % (query)
+    out = subprocess.check_output(cmd, shell=True, executable="/bin/bash").decode(
+        "utf8"
+    )
     return out.split("\n")
 
+
 def das_file_data(dataset):
     """
     Given a dataset create a pandas DataFrame with the
@@ -51,15 +55,16 @@ def das_file_data(dataset):
         - the file name;
         - the number of events in each file.
     """
-    query = 'file dataset=%s | grep file.name, file.nevents'%(dataset)
+    query = "file dataset=%s | grep file.name, file.nevents" % (dataset)
     out = das_do_command(query)
-    out = [np.array(r.split(" "))[[0,3]] for r in out if len(r) > 0]
+    out = [np.array(r.split(" "))[[0, 3]] for r in out if len(r) > 0]
 
-    df = pd.DataFrame(out,columns=["file","events"])
+    df = pd.DataFrame(out, columns=["file", "events"])
     df.events = df.events.values.astype(int)
 
     return df
 
+
 def das_lumi_data(dataset):
     """
     Produces a file by file+lumi+run pandas DataFrame
@@ -73,17 +78,17 @@ def das_lumi_data(dataset):
         - the lumisections.
 
     """
-    query = 'file,lumi,run dataset=%s '%(dataset)
+    query = "file,lumi,run dataset=%s " % (dataset)
 
     out = das_do_command(query)
-    out = [r.split(" ") for r in out if len(r)>0]
+    out = [r.split(" ") for r in out if len(r) > 0]
 
-    df = pd.DataFrame(out,columns=["file","run","lumis"])
+    df = pd.DataFrame(out, columns=["file", "run", "lumis"])
 
     return df
 
-def get_events_df(golden,dataset,events):
 
+def get_events_df(golden, dataset, events):
     """
     Produces a file by file pandas DataFrame
 
@@ -104,9 +109,11 @@ def get_events_df(golden,dataset,events):
     lumi_df = das_lumi_data(dataset)
     file_df = das_file_data(dataset)
 
-    df = lumi_df.merge(file_df,on="file",how="inner") # merge file informations with run and lumis
+    df = lumi_df.merge(
+        file_df, on="file", how="inner"
+    )  # merge file informations with run and lumis
     df["lumis"] = [
-        [int(ff) for ff in f.replace("[","").replace("]","").split(",")]
+        [int(ff) for ff in f.replace("[", "").replace("]", "").split(",")]
         for f in df.lumis.values
     ]
 
@@ -123,29 +130,32 @@ def get_events_df(golden,dataset,events):
         if df_r["events"].sum() < 10000:
             continue
 
-        good_lumis = np.array([len([ll for ll in l if ll in golden[r]]) for l in df_r.lumis])
+        good_lumis = np.array(
+            [len([ll for ll in l if ll in golden[r]]) for l in df_r.lumis]
+        )
         n_lumis = np.array([len(l) for l in df_r.lumis])
-        df_rs.append(df_r[good_lumis==n_lumis])
+        df_rs.append(df_r[good_lumis == n_lumis])
 
-    if len(df_rs)==0:
+    if len(df_rs) == 0:
         return pd.DataFrame([])
-    if len(df_rs)==1:
+    if len(df_rs) == 1:
         df = df_rs
     else:
         df = pd.concat(df_rs)
 
     ## lumi sorting
-    df.loc[:,"min_lumi"] = [min(f) for f in df.lumis]
-    df.loc[:,"max_lumi"] = [max(f) for f in df.lumis]
-    df = df.sort_values(["run","min_lumi","max_lumi"])
+    df.loc[:, "min_lumi"] = [min(f) for f in df.lumis]
+    df.loc[:, "max_lumi"] = [max(f) for f in df.lumis]
+    df = df.sort_values(["run", "min_lumi", "max_lumi"])
 
     ## events skimming
-    df = df[df["events"] <= events] #jump too big files
-    df.loc[:,"sum_evs"] = df.loc[:,"events"].cumsum()
+    df = df[df["events"] <= events]  # jump too big files
+    df.loc[:, "sum_evs"] = df.loc[:, "events"].cumsum()
     df = df[df["sum_evs"] < events]
 
     return df
 
+
 def get_run_lumi(df):
     """
     Produces the lumi mask dict starting from a pandas DataFrame
@@ -170,17 +180,18 @@ def get_run_lumi(df):
     lumi_list = [
         get_lumi_ranges(
             np.sort(
-                np.concatenate(df.loc[df["run"]==r,"lumis"].values).ravel()
+                np.concatenate(df.loc[df["run"] == r, "lumis"].values).ravel()
             ).tolist()
         )
         for r in run_list
     ]
 
-    lumi_ranges = dict(zip(run_list,lumi_list))
+    lumi_ranges = dict(zip(run_list, lumi_list))
 
     return lumi_ranges
 
-def get_lumi_dict(golden,dataset,events):
+
+def get_lumi_dict(golden, dataset, events):
     """
     Produces a lumi mask for a given dataset, up to events, using a certification json
 
@@ -197,7 +208,7 @@ def get_lumi_dict(golden,dataset,events):
         E.g. {run : [[lumi_1,lumi_2],[lumi_3,lumi_4]]}
     """
 
-    df = get_events_df(golden,dataset,events)
+    df = get_events_df(golden, dataset, events)
     lumi = get_run_lumi(df)
 
     return lumi

core/utils/dqm.py

@@ -2,16 +2,17 @@
 import json
 import os
 
-#pylint: disable=import-error
+# pylint: disable=import-error
 import numpy as np
 import requests
 from bs4 import BeautifulSoup
 from requests.exceptions import HTTPError
 
-#pylint: enable=import-error
+# pylint: enable=import-error
 
 base_cert_path = "/eos/user/c/cmsdqm/www/CAF/certification/"
 
+
 def list_certification_files(cert_type):
     """
     List all the certification files related to a certification type
@@ -48,6 +49,7 @@ def list_certification_files(cert_type):
 
     return file_names
 
+
 def get_certification_file(path):
     """
     Get a certification file from the CMS DQM certification
@@ -68,6 +70,7 @@ def get_certification_file(path):
 
     return file.json()
 
+
 def get_cert_type(dataset):
     """
     List all the certification files related to a certification type
@@ -81,8 +84,8 @@ def get_cert_type(dataset):
             or Commisioning).
 
     """
-    year = dataset.split("Run")[1][2:4] # from 20XX to XX
-    PD = dataset.split("/")[1] # pylint: disable=invalid-name
+    year = dataset.split("Run")[1][2:4]  # from 20XX to XX
+    PD = dataset.split("/")[1]  # pylint: disable=invalid-name
     cert_type = "Collisions" + str(year)
     if "Cosmics" in dataset:
         cert_type = "Cosmics" + str(year)
@@ -93,7 +96,8 @@ def get_cert_type(dataset):
 
     return cert_type
 
-def get_json_list(cert_type,web_fallback):
+
+def get_json_list(cert_type, web_fallback):
     """
     List all the certification files related to a certification type
     either stored on CMS DQM EOS either, as a fallback,
@@ -113,7 +117,9 @@ def get_json_list(cert_type,web_fallback):
         cert_path = base_cert_path + cert_type + "/"
         json_list = os.listdir(cert_path)
         json_list = [c for c in json_list if "Golden" in c and "era" not in c]
-        json_list = [c for c in json_list if c.startswith("Cert_C") and c.endswith("json")]
+        json_list = [
+            c for c in json_list if c.startswith("Cert_C") and c.endswith("json")
+        ]
     ## ... if not we go to the website
     else:
         json_list = list_certification_files(cert_type=cert_type)
@@ -127,6 +133,7 @@ def get_json_list(cert_type,web_fallback):
 
     return json_list
 
+
 def get_golden_json(dataset):
     """
     Output a the golden certification dictionary (json) for a specific datasets.
@@ -148,12 +155,14 @@ def get_golden_json(dataset):
     cert_path = base_cert_path + cert_type + "/"
     web_fallback = not os.path.isdir(cert_path)
 
-    json_list = get_json_list(cert_type,web_fallback)
+    json_list = get_json_list(cert_type, web_fallback)
 
     # the larger the better, assuming file naming schema
     # Cert_X_RunStart_RunFinish_Type.json
     run_ranges = [int(c.split("_")[3]) - int(c.split("_")[2]) for c in json_list]
-    latest_json = np.array(json_list[np.argmax(run_ranges)]).reshape(1,-1)[0].astype(str)
+    latest_json = (
+        np.array(json_list[np.argmax(run_ranges)]).reshape(1, -1)[0].astype(str)
+    )
     best_json = str(latest_json[0])
     if not web_fallback:
         with codecs.open(cert_path + "/" + best_json, encoding="utf-8") as js:
@@ -164,9 +173,9 @@ def get_golden_json(dataset):
 
     # golden json with all the lumisections one by one
     for k in golden:
-        R = [] # pylint: disable=invalid-name
+        R = []  # pylint: disable=invalid-name
         for r in golden[k]:
-            R = R + list(range(r[0], r[1] + 1)) # pylint: disable=invalid-name
+            R = R + list(range(r[0], r[1] + 1))  # pylint: disable=invalid-name
         golden_flat[k] = R
 
     return golden_flat